Prompt: Chuan and Yuan work in different industries, at different scales, and on different continents. (Both names have been changed.) What they share is the experience of being entrepreneurial workers living and working from home on the peripheries of global capitalism. Home-based work has become a familiar arrangement to many people since the start of the pandemic in 2020, but Anglophone media discussion has mostly centered around the experiences of middle class professionals juggling life and work in one space. Home work, in these accounts, is often represented as a new or abnormal phenomenon that departs from the ordinary separation between home and office, life and work, productive and reproductive labor. But as the stories of Chuan and Yuan—and the many others like them—show, home-based work has long persisted on the peripheries of global capitalism, sustaining, yet rendered invisible to, the center in the Global North. Around the globe, home work never disappears; it simply moves around, often outsourced to “less developed” countries or “insourced” to domestic immigrants or workers with lower socioeconomic status.  It takes a Taobao Village Home-based work has a long history in both Shandong Province and Prato, but Chuan and Yuan’s particular experiences and arrangements are closely intertwined with the larger transformation of global production networks. Handicraft production—straw shoes, hand-braided cookware and kitchen utensils, small furniture, and home decorations—in Chuan’s home county in Binzhou dates back to at least the seventh century BCE. Before the export industry emerged in the late Qing period (1636–1912), home-based production had served mostly family use, local market bartering, and some long-distance trading within China. But it was only after China’s economic reform and opening up of the 1980s that the export industry started to boom, meeting global demand for low-price handmade products. Women still made handicrafts at home in river bank villages outside of the county seat, but now they sold their work outside the home for piece rates: first, to collectively owned Town and Village Enterprises, and then, after privatization, to small family-owned and export-oriented handicraft manufacturing firms.

Response: After the Global Financial Crisis, many migrant workers who had learned to use computers and the internet returned to their home villages, finding alternative opportunities to sell handicrafts online to domestic, rather than foreign, customers. As overseas demand for made-in-China handicrafts weakened, some younger villagers who had worked as either crafters or farmers followed suit. AliResearch, the research arm of Alibaba, coined the term “Taobao Villages” in 2010 to describe three villages where ecommerce self-employment had become a leading occupation; three years later, in June 2013, Chuan’s village became one of the first batch of “Taobao Villages” officially declared by AliResearch.  The virtual economy of handicraft ecommerce targeting urban China’s growing middle class was in the early stages of a growth that would gradually marginalize offline, export-oriented firms over the next decade. The home-based labor regime persisted, but the fruits of digitalization were unevenly distributed. Migrant returnees and a few urbanites who migrated to the villages for the ecommerce gold rush became the biggest winners. They are generally better educated and more attuned to urban consumer tastes, with the most successful entrepreneurs having earned college degrees in design and computer science. Home-based women weavers continued to labor under the same working conditions. While they saw a gradual, albeit slow, increase in earning, they also experienced the additional labor of adapting to the new rhythm of ecommerce production, where consumer tastes constantly shift and fluctuate.  Since then, Taobao Villages and Towns have sprouted across the country, from a handful in 2013 to more than 7,000 as of early 2022, in a parallel to a reverse migration trend of urban migrant workers going back to the countryside. Rural digital entrepreneurship—mostly single-person or family businesses—has become an important drive in China’s post-crisis restructuring of its export-oriented economic model to enhance indigenous innovation capacity, expand domestic consumption, and, more recently, to promote common prosperity and reduce urban-rural disparities. Key to this trend is home-based work. Mobilizing the familial division of labor for the digital economy in a “platformized family production” is a prevalent labor regime in rural Chinese ecommerce, which builds on the tradition of family-based agricultural and handicraft work in the Chinese countryside to reduce cost. In Chuan’s case, his whole extended family is involved: his wife helps with customer service while taking care of their toddler, his dad packs and ships products, and his mom cooks and does other household chores.


Prompt: Wenzhou in the Third Italy The tradition of family-based home work is hardly unique to China or Chinese people. It’s also made Prato a famous textile town since the Middle Ages. In home factories, generations of Pratese families made woolen velvets for the Medici in Florence and the Popes in Rome. At the height of Italy’s post-war industrialization between the 1950s and 1970s, home work was still quite common among Italian workers and entrepreneurs, especially in Central Italy—“Third Italy,” as researchers like to call it—home to countless family-run small and medium-sized enterprises, setting it apart from the mammoth auto plants of Piedmont or the idyllic olive farms of Sicily. Home work was concentrated in light industries like garments and textiles that were the backbone of Italy’s export. To accommodate flexible production and to save costs, the family owners, many of whom recently migrated from the south of the country, built home factories where they and their employees could work and sleep in the same building.  In the 1970s and 1980s, these Italian family workshops temporarily outcompeted mass-production plants in places such as Detroit. However, by the 1990s, Italian companies started to outsource low-value-added manufacturing to low-income countries in Eastern Europe and Asia to cope with rising labor costs and the currency appreciation associated with the change from the Lira to the Euro. At the same time, pressure from labor unions and the declining importance of the textile industry in the national economy led the Italian authorities to gradually outlaw the use of home factories and traditional home work. The new zoning law required manufacturing space to be separated from residential areas, while the labor law, likely one of the strictest in the world, made working off-the-clock almost impossible. Meanwhile, the kids of the family business owners no longer tolerated the difficult home work that brought wealth to their parents; most left the manufacturing sector in this small town and sought easier office jobs in big cities. As a result, Prato, once the prototype of Italian flexible production, went into a recession in the 1990s.

Response: Just as locals were about to call home-based factories history, Chinese immigrants came to give them a second life. The first wave of Chinese immigrants arrived at Prato in the late 1980s thanks to the opening up of China and favorable Italian immigration laws. Most of them came from the entrepreneurial and adventurous city of Wenzhou. Yuan was one of these aspiring entrepreneurs. Before coming to Italy, Yuan was a rural-to-urban migrant worker and had acquired her technical skills working for several family-run garment factories in Zhejiang. In 1999, she joined her brother, who had settled two years earlier in Prato, to work in an Italian-run garment factory.  After a decade of hard work and frugal living, Yuan saved enough money to open her own garment workshop in 2009. They set up their home factory not just to follow the tradition both in Prato and in Wenzhou—this was also the most cost-efficient way to run a garment business in Italy. The Italian family who owned the house where she and her husband worked left the textile industry in the 1990s, and could not have been happier to lease the property to immigrant newcomers like Yuan. In Prato, she also met and married her husband, a Chinese coworker working at the same factory. Like her, tens of thousands of Wenzhounese migrant workers came to Prato hoping that one day they could also become their own boss. The number of garment businesses owned by Chinese nationals quickly increased from less than 100 in 1990 to over 3,000 in the mid-2010s.  In both Italy and China, as Chuan and Yuan’s experiences show, home-based work regimes persist, usually involving precarious workers, and in industries with thin profit margins where every minute counts and “time off” is almost nonexistent. Despite its essential and indispensable role in the world economy today, the fact that home-based work is often associated with the marginalized—the working-class, women, immigrants/migrants, the elderly—has contributed to its invisibility and its public imagination as being “backward” and illegitimate, if not illegal. And yet, ironically, the informality of home-based work—the blurring between reproductive and productive labor, consumption and production, work and life, office and home, as well as the general lack of labor protection and, not uncommonly, tax evasion—have constituted an “advantage of the backwardness” for people, regions, and nations on the periphery. At the heart of “economic miracles” like the “Third Italy” of Yuan’s Prato, or the boom of “Taobao Villages” in which Chuan participates, is the model of home-based division of labor.


Prompt: Labor Shortage and New Entrants  But if home-based work never disappears, that doesn’t mean it’s a static process, or that the dynamics Chuan and Yuan have encountered in their lives and work will persist. Almost all the women weavers in the handicraft ecommerce villages in Shandong are forty or older, and most of the younger women show contempt for weaving, dismissing it as physically demanding and culturally inferior to digital labor and urban service work. Not all types of home-based labor on the periphery are the same. Some are more desirable than others. Weaving is monotonous and physically arduous; weavers often suffer from chronic back pain and arthritis after a lifetime spent weaving. Handicraft making was also poorly paid. At a piece rate, a skilled female weaver had to work from dawn to dusk seven days a week to earn as much as an average full-time ecommerce customer service girl or supermarket cashier could earn—about $600 USD per month.  This shortage of women weavers already threatens the long-term sustainability of the ecommerce industry. Some village entrepreneurs hope that the rising per-unit price of woven goods would encourage younger women to take up weaving again, especially as they age and find it harder to work in the urban service sector. Others seek solutions in new handicraft styles that require less human weaving labor, or by outsourcing production to less economically developed villages nearby, where labor costs are lower. Ultimately, like many of the challenges Chuan’s Taobao county faces in maintaining the competitiveness of its handicraft ecommerce industry, solving the weaving labor shortage will require a collective effort. Decades of entrepreneurialism in family-based labor, and the recent rise of ecommerce, has drawn some migrant birds back home—but the growing atomization of village culture and intra-village inequality does not bode well for collective politics.  As China’s living standard rose, the labor pool in Prato also started to drain. As a result, Prato’s home factories received new guests. Since the 2010s, the number of Chinese immigrants registered in Prato has stagnated. Just as young people in Shandong despised weaving, young Wenzhounese no longer saw garment work in Italy as an attractive option. Many older workers and shop owners either moved to the service sector (such as cafés and bars) or went back to China. “If you can easily make six to seven thousand yuan [about $1,000 USD a month] by delivering food in Wenzhou, why sacrifice so much to make garments in Prato?” Yuan chuckled in 2015.

Response: For many, Covid-19 was the last straw. During the pandemic, lockdowns and the consequent economic downturn pushed many garment workshops out of business. Yuan’s company was one of them. She closed her workshop in 2021 and returned to China with her husband. This year, they started a new business in Wenzhou selling “authentic” Italian gelato to a growing number of middle-class consumers.


Prompt: What awaits Prato’s home factories after the Chinese have left? Just as the Chinese workers were looking elsewhere for better opportunities, immigrants from Pakistan and North Africa were filling in the vacancies. A few of them even bought workshops from the Chinese to become self-employed entrepreneurs. Following the Chinese’s footsteps, they will accumulate more knowledge, connections, and capital in the next decade or so, and possibly come to dominate the garment industry, thus adding a new chapter in the centuries-long story of home-based work in Prato.

Response: With the pandemic gradually fading in intensity, some white-collar workers in the Global North are returning to their offices while others are entering the brave new world of permanent work-from-home. But as Chuan and Yuan’s stories show, there is an alternative history beyond the white-collar sectors in the Global North that reminds us of the manifold inequalities rendered invisible by our uneven global capitalist system. However, theirs are also stories of hope and extraordinary resilience under difficult conditions, stories about social mobility and making sacrifices for loved ones, of exploitation and self-exploitation, and of making do with what one has while fighting for a better life.


Prompt: 1/ The internet says that the word “pivot” may refer to a point of rotation in a lever system, a chemicals and explosives manufacturer in Australia, an open source platform for building applications in Java, one of three joints in the human body, a piece of syntax, a dance move. A dancer might pivot on one heel or on the heels of both hands, depending on the dance in question.  The internet says so many things. It says that the English word “pivot” comes from older words that meant pin, point, the place on which something turns. Pue, in Old French, meant the “tooth of a comb.” Before plastics, human beings made combs from diverse materials. Archaeologists have found combs made of bone, tortoiseshell, ivory, rubber, iron, tin, gold, silver, lead, reeds, wood, glass, porcelain, and papier-mâché. In cave-people’s caves, they have unearthed fragments of combs made of stone.

Response: Future historians will have more to work with. How will they parse our society’s weird kinds of common knowledge? All the things that we know to be things without quite knowing what they are? 2/ To many English speakers who were paying attention to the internet in the 2010s, the word “pivot” became a commonplace. Around the middle of that decade, a number of tech companies, especially Facebook, signaled that they would prioritize video henceforth. Meaning, their algorithms would rank video content higher than word or picture content in users’ newsfeeds, all other factors, like likes, being the same.  The data said that video was more engaging than words, or so Facebook said. A few years later, it turned out, this had been a mistake or maybe even a lie, but it was done. Media companies had laid off writers, in the hopes of creating content that could capture more digital ad dollars, and pivot had become part of a digital ironist’s lexicon.  Horse broke its leg, so we had to take it out back and help it ‘pivot to video,’ said one blog. Pivot meant trying to recover from failure or error, but also became a joke about how you did it, covering for the way others had been thrown under the bus along the way.


Prompt: It made sense in an industry based on scalable software, an industry that had long valued companies in terms of their number of users. If value resides in number of users, why not use them for something else? It was everything we never wanted at Logic.  3/ In October 2016, a group of friends in San Francisco put up a website. The website said we would soon start putting out a print magazine. What were we thinking?  We were thinking, we wanted to make a place for people to publish the kinds of pieces we ourselves wanted to read about technology. We were thinking, we would give ourselves one issue. If, after that issue, we were not losing money, we would keep going. But above all, we were thinking, we needed an occasion to throw parties. In the era of pivot-to-video, it was the only reasonable reason to start a paper magazine.

Response: The party thing was a joke, but like all jokes, was partly serious. We wanted to bring people together, not only within the pages of the magazine but also in little rooms you had to cram into.  Our timing turned out to be fortuitous, in one sense. The election of Donald Trump, with his open support of various manosphere creeps and white nationalists, and his stated desire to use digital technology to build a “Muslim registry,” caused a lot of people in the tech industry to think differently about what they were building. The magazine ended up developing alongside a new tech worker movement, whose participants and fellow travelers shaped our thinking and often contributed to our pages.


Prompt: And if, in the first months, friends on the East Coast had asked what did we mean, a little magazine that thinks about the tech industry, soon it seemed all kinds of publications were pivoting to do the same. An entire field of tech criticism materialized. It became a modest career. The companies themselves metabolized it in various ways. Revelations about misbehaving executives and algorithms accumulated in the years that followed. Some of the same people who had led the “pivot to video” were now talking about a “tech backlash,” or “techlash.” We did too.  But, even if our little magazine professed irreverence, and occasionally made a point of bursting certain bubbles, our goal was never critique for its own sake. We believed the opposite of hype was not pessimism, which could be its own kind of racket, but specificity. We wanted to pay closer attention to how things work and to the people who made them. Attention is a form of prayer, a philosopher once said.  And it was through this specificity that we forged connections, made a community. This is the better version of pivot: recognizing that human relationships can evolve; that the entities that are linked by a network are rarely static; that there is something about the possibility of connection held out by digital technologies that is worth keeping faith with.  4/ To pivot is to turn, and see the same room from a new angle. The pieces in this issue talk about the many challenges that such change can present. Even when there is a lot of money to be made by promising that offline can smoothly translate into online, life into data, there are residuals and resistances, as when trying to datafy complex systems of information involved in shipping and customs. And some apparently progressive changes are not, in fact; as other authors in this issue argue, the shift from the open web to corporate social platforms, while it granted unprecedented visibility to some social movements, impeded the construction of meaningful collective power.

Response: This issue also reveals the instances of possibility that come from a new point of view. One piece places hope in neither abolishing nor co-opting data but in re-routing it. Another examines how the pandemic has forced tech worker organizers to look at questions of safety and community in novel ways.  Pivoting can also be a way to look back. This issue will be the last under the direction of Logic’s founding editors. And, at risk of indulgence, it retrospects. It contains interviews and reflections that go into detail about the work of making a magazine. And it contains reminiscences from some people we met along the way, people who helped inspire the project from the start.  With this, we hand things off to the new leadership, who will bring a new focus. Both through their editorial process and their model for producing the magazine, they will aim to draw in voices and perspectives that remain outside, underexplored, and yet essential to thinking about technology in the changing present, to make sure it’s not the changing same.


Prompt: For now, we want to say how much we cherish the readers we have found. We hope you will hold on to what we have made, and care for it enough, to make it something else. When Logic launched following the 2016 US election cycle, it provided a much-needed platform for critical and nuanced long-form reflection on technology. Five years ago, tech journalism—with its origins in product review—largely mirrored the industry it sought to cover. The founders’ goal—to deepen the conversation around technology—has, in many ways, been realized. Since its launch, Logic has published an interdisciplinary network of organizers, scholars, and artists. With the publication of its fifteenth issue, Beacons, it seemed Logic was on the verge of reinventing itself, with renewed commitments to the Global South, the Black radical tradition, and mixed media.  But Logic as an enterprise was also becoming harder to sustain. The magazine has always run on significant volunteer labor—the core team is mostly unpaid—and burnout is a constant concern. The sole revenue source for the magazine has been subscriptions—rather than institutional or corporate funding commonly raised by many non-profit publications—and this has necessarily imposed significant resource constraints. While Logic has always paid its contributors, its small budget has made it difficult to deepen its commitment to publishing poor or historically marginalized writers, particularly Black, trans, disabled, and/or queer writers of color. Limited money has also made it hard for Logic to find a way to evolve into its next phase. Magazines need to evolve past the moment that produced them in order to remain of use. Is there a way for this labor of love to transform itself in order to meet the needs of the next five years of tech criticism?  Vision In January 2023, Logic will transfer leadership of the magazine to one of the founding staff members, Xiaowei Wang, and Director of We Be Imagining, J. Khadijah Abdurahman. This will mark the beginning of the first queer Black and Asian tech magazine. Black, Asian, and queer are not only descriptors of our individual identities but also mark the kind of theoretical and political approaches we hope to infuse this next chapter with. Logic(s) will retain the core commitments of the magazine’s founding while laying the groundwork to radically shift both the tech journalism genre and dominant publishing models. The “Beacons” issue of December 2021 was a pilot model for what this transition will look like: deeply interdisciplinary (poetry, visual art, and sci-fi on the same axis as the long-form essay); an ongoing invitation to fields traditionally outside of tech, which offer a set of methods and tools to think through the social implications of digital technologies and data collection. Two core commitments of this transition are “marrying our thought to the poor” and increasing engagement with international issues—particularly an emphasis on Asia and the African continent.

Response: “Centering the most harmed” or “focusing on the impact of vulnerable populations” dominates liberal conversations about technology and data policy. However, “marrying our thought to the poor” means a) drawing on the conceptual frameworks of impoverished Black people, marginalized folks, and jobless people as opposed to delimiting them as a site of harm for outsiders to examine, and b) an emphasis on commissioning stories about the public sector adoption of automated decision-making systems like Medicaid eligibility determination, coordinated housing entry child welfare, UNHCR biometrics, and refugee mobile money payments. These areas continue to be under-attended to, despite the scale of their impact and their role as a site of experimentation before being generalized to the rest of the population. Relatedly, international coverage of tech continues to be limited to stories about social media platforms recognizing an insufficient number of languages or heads of state using internet blackouts to suppress dissent—stories that lack a more capacious situating of technologies in their sociopolitical context.  We echo Georgetown’s Center for Law and Policy’s concern that reliance on the terminology “artificial intelligence” and “machine learning” obfuscates the specificity of technologies narrated under those terms. Like Logic’s original founders, we share a commitment to an anti-capitalist framework. We also believe tech criticism must evolve past simply throwing the terms capitalism or white supremacy onto a story like garnish. We aim to provide the editorial support that writers need in order to do the work of developing these connections between finance and locally-rooted expertise, especially from trans, incarcerated, and Indigenous writers. The editing process at Logic has been very rigorous, partly because, unlike The Guardian or similar venues, there’s not simply a pre-existing reservoir of trained writers to tap into. A core part of the publication process is helping contributors develop the capacity to narrate their domain expertise for a popular audience in real time. We plan on addressing this through significantly increasing compensation, providing pop-up writing intensives, and preemptively budgeting for outside accountability facilitators that will be available when any member of the process feels harmed.


Prompt: Future Stories Potential stories or themes we hope to address: examining the impact of digital infrastructure projects like Facebook’s installation of submarine cables in Djibouti, shifts in how mail and other services are delivered by carceral institutions in the US, queer organzing for mesh networks in Appalachia, the revolving door between Safaricom and government digital ID regulators on the African continent, DARPA-funded research at Desmond Patton’s institute, merging algorithmic gang member identification with countering violent extremism algorithms applied to databases on migrant children in the US, changes in federal Medicaid legislation mandating increased interagency data sharing and collection. If you are researching and writing about any of these stories, or have an idea for us, email us at editors@logicmag.io with the topic “Logic(s)” in the subject line.

Response: Programmatically, in addition to intentionally commissioning more poetry, visual art, and science fiction, we plan on incorporating a regular column featuring high school students to support a more intergenerational conversation. We also plan to run book reviews, which, in this field, tend to be pleasant summaries instead of showing substantive engagement. We will provide a platform, and the necessary editorial support, to pursue this deeply necessary line of engagement.


Prompt: Continuations Khadijah and Xiaowei will be building upon the foundational infrastructure and editing that has been a labor of love by a network of people over the past six years. This includes: Jim Fingal, Christa Hartsock, Ben Tarnoff, Moira Weigel, Celine Nguyen, Jen Kagan, Alex Blasdel, Sarah Burke, Max Read, Aliyah Blackmore, Jacob Kahn, and many others.  The last issue of 2022 is appropriately themed “Pivot.” This issue marks a turning point for Logic, and is about transitions of all kinds. It features reflections on the first six years of Logic by readers, contributors, and members of the core team.

Response: It seemed like half of Los Angeles had turned out for boat tours at the Port of Long Beach: parents corralling toddlers, couples on dates, even dog owners in line for pet-friendly tours. The port offers free guided tours to the public once a year, a sort of goodwill gesture to the community that has suffered decades of pollution as a result of its activity, and that of the adjoining Port of Los Angeles. After two hours of waiting, I filed onto an erstwhile whale-watching tour boat, where I took in the port’s enormous container ships. Like my fellow tourists, I was excited to get a glimpse of the scale of operations necessary to keep the nation supplied with toilet paper, plastic toys, and every other conceivable good.  Squinting against the sun, I tried to imagine the ships another way: as numbers on a screen, cells in a spreadsheet, dots on a grid. I’d been reading about the information transfer that accompanies the movement of these vessels, and I knew that the scale of this data is nearly as impressive as the ships’ sheer size. Ships like those docked at Long Beach are vital links in the global supply chain, but they’re also floating “data terminals,” as the global maritime industry consultancy Lloyd’s Register put it in 2015. Increasingly, these vessels receive and transmit an enormous amount of information: about their position, of course, but also about weather, traffic, temperature, maintenance, staffing, ocean conditions, and much more. The streams of information are so complex that they threaten to exceed humans’ ability to interpret them. That’s partly why many newer vessels—“smart ships,” in industry parlance—use complex algorithms (some of them devised by Google and Microsoft) to chart their courses. Within the next decade, carriers hope to launch fleets of automated or remote-controlled vessels—“ghost ships,” as they’re sometimes called.


Prompt: Further away from the port, in office blocks and operations centers, fleet management centers house another tranche of data: information about which containers hold which goods, which ships carry which containers, where those ships are headed, and who paid for what. Elsewhere, “quants” with PhDs in astrophysics collate historical data with information about geography, weather, stock prices, and ship movements, searching for opportunities to place stock market bets on global trade.

Response: At the port, I marveled at the feat of coordination represented by all these containers, all of this loading and unloading. But I also knew something strange about the shipping industry: despite all its technology, global shipping is still infamously paper-heavy. Important documents, like bills of lading and letters of credit, tend to pass physically from person to person, from driver to dockworker to engineer to trucker to warehouse supervisor. While a container moves across the ocean, its accompanying paperwork might literally be flown across the world to meet it.  That’s slowly changing: in the cloistered world of global shipping, a gold rush is taking shape, as companies vie to connect and commodify shipping data. Advanced real-time data about shipping promises to improve the speed and reliability of global logistics. But it could also have other, stranger implications. As ships edge toward automation, the prospect of rich shipping data makes it increasingly possible to imagine a future in which shipping is controlled by machines. And because shipping and banking are so deeply intertwined, better data could attune the movement of cargo near-seamlessly with the priorities of financial markets.


Prompt: An Industry Built on Paper Shipping may be unusually dependent on paper, but it’s not for any lack of data. And there are lots of different kinds, including data about ship locations, ships themselves, what ships are carrying, and the buyers and sellers of ship cargo. Ship locations are relatively easy to come by: anyone with an internet connection can monitor the movement of large ships. The UN’s International Maritime Organization requires large vessels to transmit their location information using a VHF-radio protocol called the automated identification system, or AIS. A Google search will yield numerous portals where you can view ship movements in near-real time.

Response: Data about what’s happening on ships is increasingly sophisticated, but it’s usually proprietary, held mainly by the ship operators. The newest shipping vessels are covered with sensors of all kinds, monitoring everything from cargo temperatures to fire hazards to hull conditions. On the bow of a ship’s mast, an anemometer might measure wind speed and direction. On the bottom of the hull, an echosounder can detect the depth of the seabed. In the engine room, meters measure the flow of fuel, monitoring the engines’ efficiency and condition. Increasingly, the data is reported back to shore in near real-time: 5G technology and low-Earth orbit satellites have increased the practicability of worldwide connectivity. This is important, since technologists in the shipping industry envision a near future in which one captain controls a fleet of crewless ships from an onshore computer. But detailed as this shipboard information is, it’s generally not available to anyone outside the ship operators.


Prompt: Things get even trickier if you want to get ahold of information about what ships are carrying. That data exists, but it tends to be stored within the software systems of shipping companies and amongst the paperwork of customs agencies—there’s no central clearinghouse for information about cargo. AIS data can tell you where ships are, but it can’t tell you what they’re carrying. In the absence of real-time cargo data, companies like CargoMetrics use historical data and proxy information (about which types of vessels are at sea and where they’re headed) in an attempt to derive information about which cargo is in motion. Figuring out who’s selling to whom is even more complicated. There’s no one organization that keeps track of this kind of transaction information, and supply chains are rife with small outfits that spring up and then vanish from sight at a confounding pace.  So the data exists, but the paper persists. The various stores of information are in separate, locked-down databases, kept apart by competing business interests, incompatible data models, and differing legal frameworks. To make things more complicated, when we talk about the “supply chain,” we’re not really talking about one industry; instead, we’re talking about a stunning variety of disparate players, all engaged in moving stuff: freight forwarders, charterers, drayage companies, container lines, truckers, terminal operators, and chassis providers, to name just a few. Each has its own data, but as often as not, information moves between operators on paper. “It is not uncommon for a shipping document package to contain fifty sheets of paper that must, in some cases, be exchanged between thirty different stakeholders,” reports the Digital Container Shipping Association (DCSA). In the absence of a unified mode of communication, paper patches over the gaps between systems.  The bill of lading, for example, a critical document that describes a shipment and acts as a bill of title, is almost always paper: the DCSA reports that only 1.2 percent of bills of lading were digitally transmitted in 2021. At each stop on the cargo’s journey, the physical document must pass from hand to hand until it reaches its destination. In the UK and some other countries, there was until recently no legal provision for the possession of an electronic title; anyone seeking to prove ownership had to have that ownership on paper. Different countries have different laws, different industries need different information, and different carriers each have their own requirements. During the height of the Covid-19 pandemic, when many flights were grounded, some containers were stuck at ports because the paper documents necessary to release them couldn’t be couriered via airfreight. The global supply chain is a stunning feat of communication and technology, but surprisingly little of that communication is accomplished digitally at the moment.

Response: So the issue is not that movement isn’t tracked and computerized; it’s that it is tracked too many different ways, in scores of different proprietary systems, most of them locked down. Individual carriers might have granular detail about their vessels’ condition and cargo, but in the absence of a network that stitches all of that information together, the big picture remains incomplete. “You have to connect data from multiple data sources, and to be able to do that, you have to be able to access data that resides within the systems of different carriers, different freight forwarders, different customs brokers,” logistics expert Inna Kuznetsova told the Journal of Commerce in 2019. Paper documents like bills of lading may be unwieldy, but they’re still more convenient to exchange than unintelligible or inadmissible digital records.  Data standards would make a difference: if information could be exchanged in a predictable format, companies could share it electronically. These standards do exist, but there are also significant barriers to their adoption. In 2015, the United Nations Centre for Trade Facilitation and Electronic Business (UN/CEFACT) published a set of data models that lay out definitions and specifications for trade documents, designed to be encoded in data exchange formats such as JSON or XML. If a trading partner knows how information will be formatted, it can design a computer system to interpret that data without human intervention. Data standards aren’t unlike the shipping container itself: once everyone agreed on the box’s dimensions, containers could be swapped in and out without any need for discussion. Likewise, if everyone agrees to use the same data standards, information can be handed over as easily as a shipping container moves from cargo hold to truck chassis.


Prompt: The existing guidelines are complex, however—they have to be, in order to account for every possible use—and require a fairly high level of technical proficiency to implement. There’s no single organization or governing body with enough heft to impose standards on the entire industry. Some countries, industry groups, and regulators are concerned about electronic fraud. In some jurisdictions, laws need to be revised to permit electronic documentation. And some carriers and container lines are reluctant to make their data available to competitors because, while standardization could benefit the industry as a whole, individual companies’ data represents a potentially valuable asset. Standards, in other words, are inflexible, and the world of global trade is a chaos of variables.

Response: Covid has only heightened the urgency of efforts to network shipping data. “Covid was the spark that ignited everything,” Thomas Bagge, CEO of the DCSA, told the Global Trade Review in 2021. “There is a significant number of stakeholders now motivated to change the opaque and antiquated processes that are still common in international trade.” In March of 2022, the Biden administration announced the launch of Freight Logistics Optimization Works, an advisory group tasked with developing a “proof-of-concept information exchange” designed to ease supply-chain congestion. The Port of Long Beach, an advisory group member, pledged to work with defense contractor UNCOMN to create the “Supply Chain Information Highway,” designed to “liberate actionable data across supply chain nodes and organizations.” Dematerialize the Bill of Lading Despite all the complications, shipping’s lingering dependence on paper makes it an irresistible target for tech companies. Numerous startups seek to “dematerialize” the bill of lading and other documents, while others offer platforms that promise to connect disparate streams of data, often with the use of AI. So far, though, the result of all this activity has been an even knottier tangle of data, with no single standard or company emerging as the clear consensus choice. As separate companies seek to optimize logistics sectors like container movement, fuel demand, shipping volume, and charter prices, it gets even more difficult to turn all of this data into a coherent whole. “I’ve got 99 problems, but data’s the biggest one,” writes logistics journalist Eric Johnson. “It’s like the faucet turned on, and no one has been able to slow down the flow.” Data is flowing from every direction, but no one seems to be able to turn all of this information into a unified stream of global activity.


Prompt: Yet the reward for integrating supply-chain data could be immense. To understand why, we need to take a brief tour of shipping finance—a sector that’s as complicated as you might expect in a system that spans the globe. One area of it, called trade finance, addresses the lag time between the issuing of an order (for goods or commodities) and the delivery of that order to the buyer. Sellers often need money to purchase supplies and labor to procure or assemble the goods. Buyers, however, usually resist paying for goods until they’re delivered. To plug that gap, lenders extend loans to either buyers or sellers, charging interest or a fee in return. There are countless variations, of varying degrees of complexity, but the purpose is generally to keep the wheels of commerce moving smoothly by advancing cash to suppliers.

Response: It’s time-consuming, by modern standards, to arrange these loans. Lenders need to know that their loans will be repaid, so they want to know about the borrower’s finances, market conditions, transport risks, and every other piece of information they can get their hands on. Documents need to be exchanged. People need to sign off. It can take months. But what if all that information could be networked, so that it could be accessed and evaluated in an instant? What if loan decisions could be made algorithmically? What if—irresistibly!—these algorithmic transactions could be executed by “smart contracts” on the blockchain? These transactions could take place instantaneously, but you’d need to have really accurate data about a company’s solvency, its loan history, and the market for whatever’s being transported. You’d need, that is, a lot of data: about what’s being exchanged, which shipments are already underway, about the market in general, and about who’s doing the buying and selling. As we’ve seen, that data exists, but it has yet to be networked.


Prompt: An enormous amount of money is at stake. In 2020, global trade finance had a value of $5.2 trillion, according to McKinsey & Company, and the market is only expected to grow. Moreover, a more “transparent,” legible market in trade finance could herald the entrance of a new set of market players. Maritime finance has traditionally been the province of large financial institutions and shipping insiders. Shipping has long had a reputation for deals conducted in smoky backrooms by insiders steeped in arcana, such as bunker rates, freight-forwarding agreements, and value maintenance clauses. Few non-expert investors have felt comfortable wading into this environment. But what if there was a kind of internet of cargo—an easily legible map of what’s traveling where?  Shipping finance has already absorbed a significant amount of private equity and hedge fund investment, and has spawned IPOs and even NFTs. (A company called Infinity Maritime allows investors to purchase tradable “MetaUnits” that represent a share in a shipping fleet.) If an internet of cargo existed, investments could be much more data-driven, making trade finance an even more alluring target for investors.

Response: The Shadow Life of Grain What’s more, where there’s debt, there’s a sellable asset. Banks are talking increasingly about securitizing debt from trade finance, meaning they want to package these loans and sell them on to investors on a public market. A container’s worth of grain, for example, might be paid for with a loan that is then sold as a security, or part of a security. As a ship carries the grain across the ocean, the loan could change hands multiple times as market conditions change. Grain prices drop and an optimistic investor steps in to purchase the loan, betting that prices will rise before the ship makes landfall. They do, and the lucky investor sells the loan on, pocketing the price difference.  “Given that global demand for trade finance already outstrips supply by about US$1.5tn a year, we see huge potential for a thriving secondary market to stimulate trade in goods and services,” Surath Sengupta, HSBC’s global head of trade portfolio management, told Global Trade Review in 2019. In this way, grain becomes valuable not only for its existence as grain, but for its shadow life as part of a financial instrument. As the physical grain moves across the ocean, ownership of its debt could ping-pong around the globe.


Prompt: Accurate cargo and commodity data could also provide an invaluable window into the flow of commerce. Imagine if you knew at all times which commodities were headed where. You could determine whether a market is about to be flooded or whether a shortage of, say, wheat means the cost of bread is about to skyrocket. “Shipping holds the no-shit, honest truth of what the economy is doing,” angel investor Doug Doan told Institutional Investor in 2016. Traders with special insight into shipping data could identify mispricing in the stock market—perhaps GE’s stock is too high, given the flood of cheap washing machines headed our way. They could then exploit that information gap to pocket the difference in price. Currently, data analysts can make estimates about ocean freight based on vessel type and economic indicators, but these are just educated guesses. If a database was constantly updated as transactions took place, this real-time data would make it possible for those with access to place more aggressive, potentially more lucrative bets.

Response: Given the potential value of this information, the question of who will control or have access to the network of comprehensive shipping data is of urgent importance to many players within the worlds of logistics and finance. Currently, the major shipping lines, like Maersk and Hapag-Lloyd, have privileged access to a great deal of detailed information, because data about their customers and cargo is locked into their technology platforms. National agencies, such as US Customs and Border Protection, hold another large swath of data, but it’s not linked with other datasets. A growing number of startups is attempting to harvest and synthesize various sources of data, but to obtain proprietary data would require the participation of ports and shipping companies.


Prompt: Here, China has an advantage: its government-sponsored National Public Information Platform for Transportation and Logistics, or Logink, collects data at a number of participating ports in Asia and Europe. Because Logink is part of ports’ information systems, it absorbs key documents like manifests, bills of lading, and customs documentation, and then integrates these documents with countless other streams of information, such as AIS data and information about historical trends. Logink is such an impressive feat of engineering that its growth is making parts of the US government nervous: Michael Wessel, a commissioner of the United States–China Economic and Security Review Commission, a federal agency, told the Wall Street Journal in December 2021 that Logink represents “a treasure trove of intelligence of national security and economic interest.” The future of shipping could, in investors’ fantasies, be an almost pure expression of financial markets, with automated ships following routes dictated by live market data. Consider if the price of grain falls below a threshold necessary to make a profit from a carrier’s voyage. There’s nothing to prevent a (possibly autonomous!) ship from changing its route and setting course for a more lucrative market. In a world of perfect information, what would prevent an algorithm from revising market and fuel costs mid-voyage, rerouting a ship on the fly, and sending its cargo to a different destination entirely? Efficient though this dynamic system might be, it hardly seems likely to result in more predictable supply chains. On the contrary, events like the stock market crash of 2008 or the “flash crash” of 2010 suggest that global shipping’s deeper integration with financial markets could introduce another layer of instability.

Response: If one is inclined to doubt carriers’ willingness to cast aside human welfare in the pursuit of short-term profits, the growing problem of crew abandonment offers a corrective. When a ship’s debts are worth more than the ship itself, shipowners have increasingly opted to simply stop paying crew members’ wages and abandon the ship where it is. In 2021, at least ninety-one ships were abandoned by their owners, their crew members still aboard. Seafarers might be abandoned in Mogadishu, as one crew was in 2021, or Umm Al Quwain in the UAE, as another crew was for forty-three months starting in 2017. Stranded onboard without visas or the means to make their way home, seafarers’ ordeals can last for months or years. One crew was abandoned off the port of Ajman in the UAE for eighteen months. These instances of abandonment, cruel though they may be, are simple calculations on the owners’ parts: when value x exceeds value y, perform action z. Full and accurate data would allow for these kinds of calculations on every load a ship carries: algorithmic apportionment of goods, powered by increasingly granular data.


Prompt: Flesh into Fractions In the postmortems of the worst days of the pandemic’s supply-chain crisis, technology experts were quick to blame antiquated communication and information systems. “Supply chain management is facing its own pandemic of outdated processes,” EPSNews reported in 2021. But modern shipping’s abandoned seafarers are proof that more precise data doesn’t always work in favor of the common good. In a way, the seafarers were abandoned because of efficient access to information: to the workers’ detriment, their wages outweighed the current value of the ship. It’s just math. And this kind of math, in which humans are measured against market values and found wanting, seems to be repeated over and over again in the history of global logistics.

Response: Shipping, in fact, has a special relationship with data. In some accounts, the growth of international trade in the sixteenth century helped to create the kind of information we now call data. Maritime trade generally involves the conveyance of objects over long distances, to unseen trading partners. But how to represent a product’s qualities in mutually intelligible terms, if the buyer can’t judge it with their own eyes? Seafaring merchants had to devise standardized categories that would stand in for the characteristics of particular goods: Tellicherry pepper, for example, or long-grain rice. With this act of categorization, individual peppercorns can be aggregated, made interchangeable, and exchanged for an agreed-upon rate. It’s this kind of quotidian bookkeeping that produces data: particular entities get grouped under one name and added up on a spreadsheet.


Prompt: But sixteenth-century seafarers weren’t just trading in peppers and rice. Historians tell us that the transatlantic slave trade was the engine behind the explosion in global shipping from the sixteenth through nineteenth centuries. And even as merchants were codifying standards for coffee and tea, they were keeping detailed ledgers of human beings, carefully categorizing them by physical characteristics and market value. “Compilers of slave ship manifests,” writes the historian Jessica Marie Johnson, “participated in the transmutation of black flesh into integers and fractions.”  Logistics, enslavement, and the advent of data are all tangled up in the historical record. And race continues to structure the global supply chain. Many of today’s abandoned seafarers are from southeast Asia, from countries left impoverished by a legacy of colonization. The warehouse workers who handle the goods onshore are disproportionately people of color. The very undersea cables that deliver data between continents retrace maritime routes first mapped by the transatlantic slave trade. And of course, we wouldn’t really have global supply chains, at least not to the extent we do, if the market didn’t award a substantially lower value to the labor of people from the Global South.

Response: The scholars Stefano Harney and Fred Moten write that logistics is a way to assert ownership. Atop a roiling ocean, rocky terrain, or noisy metropolis, logistics imposes a grid of straight lines. “Logistics aims to straighten us out, untangle us, and open us to its usufruct, its improving use,” they write. “Such access to us, in turn, improves the flow line, the straight line.” The internet of cargo, the supply chain information highway, and similar efforts promise to use data to pave over the world’s contingencies—restive workers, ecological disasters, unpredictable demand. But does logistical efficiency equate with the common good?  For as long as logistics has existed, it has viewed humans and their environment as friction to be eliminated. The enmeshment of logistical data with high-speed trading simply completes a circuit initiated with the inscription of human beings into slave-ship manifests: it submits all that the supply chain touches to the disciplining logic of the marketplace. In its single-minded quest for ownership, logistics has the goal, write Harney and Moten, “quite simply and starkly of preventing us from taking care of one another, from looking out for one another.” It should make us wonder: how sure are we that a speculative market in shipping data will not result in a more unjust world? Are we certain that we want to assign responsibility for distributing goods to algorithms we don’t understand? We have the proven ability to codify logic that demands the performance of atrocity—particularly for those judged subhuman as a result of their race. Perhaps, then, when the shipping and finance industries laud technology’s power to make the supply chain “more agile” and “responsive to market forces,” as the DCSA put it in 2022, we should question what that actually means. Perfect data could allow cargo to move more efficiently, but it would also be more likely to move in lockstep with the inscrutable whims of financial markets.


Prompt: Into the Sea It’s hard not to get a little carried away imagining a future conjured by all of the coming technological changes the industry touts: the world’s oceans transformed into a computer-operated game of Battleship, with faceless autonomous vessels tracing routes dictated by algorithmic finance. In reality, these changes will be piecemeal, probably painfully slow, with a lot of haggling over policy and standards. Parts of global shipping may never digitize. The forces pushing toward a future of data-driven shipping, however, are strong.  Back in Long Beach, our tour boat slid neatly into its berth and I followed my fellow passengers back onto land, dodging toddlers and wishing I’d worn sunblock. I started the walk back to my car, thinking about something I’d learned recently, in Christina Dunbar-Hester’s Oil Beach. In the 1950s, the land around the Port of Long Beach began to slide into the ocean, the result of an intensive program of extraction that pumped billions of barrels of oil from the nearby Wilmington Oil Field. Officials managed to stave off the subsidence by pumping seawater underground, re-pressurizing the eroding land. So all of us here in the port parking lot were treading on ground being held in place by the sea. Logistics had drained the land of its resources and logistics had reinflated it, forcing the ground into Harney and Moten’s “straight line.”  Whether that equilibrium will hold, however, is an open question, since rising sea levels threaten to inundate parts of the port by 2050. The current plan to defend the port entails shoring up infrastructure, hardening the port’s defenses against rising tides and extreme weather. I wondered, though, about the wisdom of this. Instead of doubling down on the system that brought us here, I thought, what if we stopped to ask whether the logistical infrastructure we’ve built is the one we actually want? Data has eaten, is eating, will eat, the world. What do we do about it? There are about as many answers as there are people asking (probably more). But if we set aside those who advocate rolling over with a smile on your face, responses tend to come in two particular flavors.

Response: The first—let’s call it “data abolition”—disputes the entire question, particularly the “is.” It disputes the inevitability of datafication, in other words, and suggests pushing back, hard, against the practices that are advancing data’s eating-of-the-world and the narratives making that consumption seem so inevitable and complete. The second—let’s call it “data co-option”—argues for datafication as a source of hope. In particular, it maintains that datafication might be co-opted to challenge the very dynamics causing negative outcomes, thereby creating a kind of data resistance, or “data counterpower.” If data is a new register of power, we should work to twist it back on itself.  This second approach underpins what is increasingly referred to as data activism: using data to address injustice. For instance, we can look at the (in many ways) fantastic book Data Feminism by Catherine D’Ignazio and Lauren F. Klein. As the title suggests, Data Feminism is about how data collection and mobilization can be of use in achieving feminist political goals. Data, the authors argue, can be a tool of resistance. A quintessential example of such data activism would be Mapping Police Violence, a tool built not only to track murders by law enforcement in the US, but also to highlight how underreported and patchy the data on such killings is.


Prompt: There’s nothing wrong with data activism, and no inherent contradiction between co-option and abolition: many people hold both views, including myself. You can simultaneously be skeptical about the inevitability of data and pragmatically willing to use it where it appears. The difficulty is doing both in parallel: to deploy data only when it is strategic to do so. Overbalancing—that is, skewing to data—risks having data co-option campaigns being co-opted themselves, naturalizing datafication as beneficial or inevitable. Further, such efforts might not bear fruit. Being strategic, then, requires knowing about data’s usefulness. Where does data activism work, under what conditions, and for whom?  A lot of writing that advocates for such activism is quiet on these questions. In Data Feminism, the authors note explicitly that “data alone do not always lead to change—especially when that change also requires dominant groups to share their resources and their power.” But despite this caveat, there’s little discussion of where data does, or does not, lead to change: not only its potential, but the situations where it doesn’t work. Where does the reliance on data risk the harm of naturalizing datafication, without the benefits of addressing injustice? What are the factors involved in data activism being effective? These are the questions that I want to try to answer. Not through looking at our contemporary moment of data activism in the era of data science, but at the past: at efforts to do data activism before data science was a term. In particular, the history of trans people’s use of data to fight for access to healthcare holds lessons for how to strike the right balance between data abolition and data co-option. What happens when we look not forward, to the hoped-for (or feared) future, but backwards, at the ancestors of data counterpower itself? An Embarrassment of Data But why focus on trans healthcare? The answer has to do with how it became institutionalized (so much as it is) within medicine. Without digging too granularly into the history, the important thing to understand is that when trans medicine became recognized in the 1960s, practitioners partly got involved for research reasons. That is: they were interested in understanding the outcomes of treatment, not only through providing it but through making patients the object of study.

Response: One consequence of this was the model that treatment has often taken, that of the dedicated Gender Identity Clinic (GIC): a one-stop-shop for evaluation, therapy, hormone prescription, and surgical access. GICs, often based at university hospitals, enabled doctors to control the entire process (and the patients) in a way amenable to research. And while this model has changed to some degree, it is the GIC-based clinicians—with their interest in research, and the accompanying claim to scientific authority—who have tended to end up running the show. In some respects, then, trans healthcare could be seen as an ideal site for data-based change: a putatively scientifically-driven space in which many of the most powerful figures are professional researchers.


Prompt: One nation that follows the GIC-based model is the UK, and my first story starts there, in the 1990s—some thirty-odd years after the clinics were established. Under the National Health Service (NHS), anyone seeking treatment with their local doctor is funneled to one of a (small) number of dedicated, NHS-run clinics around the country. Along with their selves and their medical records, patients bring a promise of funding—a promise that their local branch of the NHS, rather than the clinic, will foot the bill if certain conditions are met.

Response: The key word is “if.” For starters, NHS branches had (and have) wildly inconsistent policies for what they will fund for trans therapeutics. Some only funded psychiatry; some funded both psychiatric support and surgical treatment; others funded both, but for a limited number of patients a year. To make matters worse, even if funding was theoretically available, trans people ran into the fact that GICs’ low priority to the NHS—and, more broadly, its highly conservative and cautious approaches to trans care—makes for long waiting lists for even initial appointments, let alone treatment. At one point a patient seeking surgery through the Charing Cross GIC in London was held on the waiting list, and then in therapy, for fourteen years. All of this, but particularly the funding deficiencies, was often justified by the NHS with claims that treatment was not important enough to be a high priority, and that care—particularly surgery— did not have a strong enough evidence base showing it improved patients’ quality of life to be funded.


Prompt: Into this stepped Press for Change (PfC), a long-running trans campaigning group in the UK that has fought for, among other things, increased access to medical treatment. Robust data showing positive outcomes for patients would seem to challenge both of the NHS’s excuses for not resourcing it: not only would this data provide certainty on the question of improved patient outcomes from surgery, but it would demonstrate the urgency and importance of providing funding in the first place. So in the mid-1990s, PfC sought to gather just such data. They launched a survey of patient experiences, both online and on paper. In other words, they used data to gather and surface marginalized people’s experiences and desires in order to challenge public policy—a pretty canonical example of what we might now call data activism.

Response: So what happened? When I sent that question to Claire Eastwood (who organized the survey) through PfC co-founder Christine Burns, the answer came back: nothing. The data was collected, but it was never analyzed. Why? In a word, “capacity.” PfC was, in Claire’s words, “massively overloaded as the [overall] campaign expanded, and … sadly this task got overtaken by other urgent and vital work, and never made it back up the priority list.” As Burns put it, the problem wasn’t an absence of data—they had an “embarrassment of data”—but rather that PfC was “always on the edge of biting off more than we could chew,” and the survey pushed them over that edge. In theory, the data could have been used to make a stronger argument for the efficacy of surgical care. But in practice, making that argument through an analysis of the data was more than PfC had the resources to do.


Prompt: The point here is that the work of data activism is, well, work: work to collect data, to analyze it, to publish, and to use it. If movements have one thing in common, it’s that there are always fifteen tasks for every hour of the day—and the more pressured a movement is (the more vulnerable their members, the more urgent their tasks), the greater the cost of taking on new tasks. What this means is, practically speaking, an inverse relationship between the cost of data activism and the need to address the problem. The more pressing the injustices that activism confronts, the more pressed activists are for resources—and so the less likely there is to be capacity for data collection, analysis, and reporting.

Response: Who Gets Counted Of course, claims about care efficacy aren’t the only argument used to deny resourcing for trans health and concerns. Another is population, the topic of our next example. Or to phrase it as a question: how many people want gender-affirming medical care? The authoritative answer, for the longest time, was “between 1:30,000 and 1:100,000 people”—and by authoritative I mean that it even made its way into the American Psychiatric Association’s (APA) official diagnostic texts. The reliability of this answer matters quite a bit because, to paraphrase D’Ignazio and Klein, who gets counted counts. A low number means an argument for low funding for care, a high number for increased funding—and, if politicians are cynical, an increased need to at least pay lip service to a population’s needs. People, after all, vote.  It turns out the official estimate is not very reliable at all—in fact, it’s almost certainly a dramatic undercount. The reason is its source: it comes from a range of studies done by GICs in various countries in the 1970s. This creates two problems. One: the 1970s was a very different time (mostly in bad ways) for the shape of trans healthcare, and for articulations of trans identity and desire. Two: the GICs doing this work at the time were, to varying degrees, awful.  With few exceptions, GICs across the world have been known (particularly historically) for their onerous diagnostic and selection criteria. Some excluded married trans people; trans people who had ever had children; trans people who were gay, lesbian, or bisexual; or trans men altogether without consideration—and that consideration included literal years of psychological tests, psychiatric interviews, and required life changes, including (at some) a demand that the patient quit their job and transfer to a more “gender-appropriate” one, regardless of the impact this might have on the stability of their life. Paradoxically, many even refused treatment to patients for the sin of having successfully sought treatment through other routes.


Prompt: What’s more, trans people—particularly those with access to community spaces—knew the environments were unpleasant, and often sought care almost anywhere else if possible. The result is that the estimates from GICs were less estimates of how many people wanted treatment, and more of how many people, having exhausted every other option, successfully passed through highly rigid clinical processes. Extrapolating population-level data from those accepted into the clinics is like extrapolating how many people buy cars from purchases at a Ferrari dealership—there’s a lot of filtering going on before someone even gets to signing the paperwork.

Response: But the answer to “how many people?” matters, and one person who saw that was University of Michigan professor Lynn Conway. A trans woman and renowned computer scientist, Conway decided to make her own estimate, based not on forty-year-old data from the GICs but—thanks to her contacts in and knowledge of North American trans communities—the far larger number of private practitioners. With a wider and more representative sample of treatment pathways, her 2001 research came up with a very different number: not 1 in 30,000 people seeking gender-affirming care, but 1 in 2,500. And in retrospect, these numbers are (quite clearly) closer to the truth. Conway didn’t simply publish them online—she presented them at the biannual conference for researchers and practitioners around trans healthcare, and even published them in a gender studies journal.


Prompt: So when the APA’s Task Force on Gender Identity and Gender Variance met in 2008 to write a report updating its approach to questions of gender, one would expect them to take these challenges seriously. And they did address them: in a footnote, after reasserting the validity of the 1970s numbers. Not only that, but the footnote in question mentioned Conway’s work only in order to dismiss it because it “seems to represent a minority position among researchers, although transgender activists tend to endorse the study.” No methodological challenge; no claim the study was incorrect. Simply wholesale dismissal. Why? Because if the answer were true, the medical researchers would have spotted it before Conway did. Because if trans people agreed with the estimate, it was automatically suspect.

Response: The reason for this response is fairly obvious: power. Professionals in trans healthcare—particularly at GICs—get a lot of their official power and authority from the perception that they are singular experts in all things trans. The Clarke Institute in Canada, for example, was for the longest time the Canadian government’s sole source of expertise on (among other things) how trans people should be treated in prison. And if those experts admit they can’t even be trusted to count—well, what can they be trusted on? Accurate or not, published or not, Conway’s study challenged clinicians’ authority. It’s interesting to note that in the newest version of the Diagnostic and Statistical Manual (psychiatry’s bible), the APA did provide a higher prevalence (and a caveat that this was likely an undercount), but credited GIC-based researchers with this discovery.


Prompt: What this highlights is that what counts is not only conditional on what gets counted, but also who is doing the counting. On the surface, we’re talking about a prime site for data activism: research-oriented, quantitative questions in the domain of professional scientists. But when the answers come from people whose claim to authority undermines the credibility and the ego of those same scientists, they’re not taken seriously at all. Data alone works only if people are willing to listen: the rest of the time, you need not only data, but a bigger, more fundamental shift in the shape of how decisions are made, and by whom.

Response: Power Routing These examples might suggest I think data is overplayed as a source of resistance—at least, data alone. And, well, I do. But that’s not the same as saying it’s useless. Sometimes data can make a real difference—not by confronting power but by routing around it. That’s exactly what it does in my third example, which is about a far more personal question: where does one go for surgery? Gender-affirming surgery in the US operated for most of the 1980s and 1990s largely through a small number of independent private surgeons. This created an information asymmetry, extreme even in comparison to the default power dynamic between a patient and a doctor. Surgeons tended to show off the best-case scenarios, and didn’t necessarily have direct access to patient experiences even if they wanted to. Precisely because of the small number of providers, most people had to travel hundreds or thousands of miles for care (and still do)—meaning they usually returned home as soon as possible, cutting into doctors’ awareness of post-operative experiences. Even with the most honest surgeon, their promises around healing time, cost, and outcome were ultimately a guess, and one that patients had to take at face value. If you were lucky enough to have access to community spaces with people who had received surgery, you might be able to get some anecdotal information about some surgeons, but that’s about it.


Prompt: So, in 1996, a trans woman named Michelle Wallace launched the Post Operative Transsexual Survey (POTS). Hosted on her website, which was part of the very 1990s webrings-and-GeoCities-account network that made up a big portion of the early trans internet, the survey ran for around a year. In that time, it got 164 responses—more than most academic followup studies. And Wallace posted the aggregated results online, for anyone to see and use.

Response: Unlike surveys by surgeons, which might ask vaguely and abstractly about quality of life, Wallace was positioned to ask more concretely about what concerned prospective patients, having been one herself. She asked obvious-seeming questions that were anything but obvious to academic researchers. Were the nurses caring? How quickly could you go back to work? If you had a question for the surgeon, did they make themselves available? The result was a community resource that people who were thinking about surgery could use to get a sense of what their options were, and make their choice based on what mattered most to them.


Prompt: This guerilla data activism worked because it didn’t directly challenge the formal structures of power. Instead, it routed around it—it worked not against, but athwart. Which brings us back to the question of how data activism and abolitionism relate, and what we can reasonably expect of the former.  Vitally, the routing-around approach to data activism avoids a common tension between activism and abolition, in which activism tacitly reinforces the grounding of our understandings of rights, power, and injustice in data-based evidence, while abolition seeks to undermine it. After all, to argue that you should be taken seriously because of your data is to argue that data should be taken seriously. But a central component of POTS was who Michelle Wallace was addressing, and about what. She wasn’t addressing the state or doctors; she wasn’t seeking to ground claims for recognition in data. Instead, data served to inform people within the community it was about. As a result, one of the main tensions between activism and abolition was, if not avoided, then at least drastically reduced.

Response: Obviously, intra-community change isn’t enough—there needs to be some form of address outside it. But these stories suggest that, taken in isolation or in the abstract, data itself won’t provide that. Certainly, data is a potential tool and technique in enacting social change. But like any other tool, it comes with costs, and is dependent on broader projects. In the absence of those broader projects, its main strength is in pooling community knowledge, not challenging structural power directly. If the goal is to mount a direct challenge, data activism might be a component of activism writ large. But it will only be effective if paired with the same humdrum, menial, and vital tactics—political campaigning, mass movements, media work—that were the bread and butter of social activism long before capital-d-and-s Data Science came along.


Prompt: In March 2022, tech workers at The New York Times voted in favor of unionizing with the Communication Workers of America, 404 to 88, forming the largest white-collar tech union in the US with collective bargaining rights. The Times Tech Guild represents around 600 employees—software engineers, product managers, designers, data analysts, and others—based in cities throughout the US, on a team that has an entirely hybrid workflow.  Tech workers at the Times are not the only ones who have gone hybrid. Since the pandemic, 58 percent of job holders in the US can now work remotely for some part of their job. And when given the opportunity, 87 percent choose to work remotely, or in a mix of in-person and remote work. Big tech companies, in particular, have embraced hybrid and remote work: Apple allows workers to work remotely two days every week, Twitter allows them to work remotely for as much time as they want (even permanently), and Facebook expects that over half of their workforce will be remote over the next few years.

Response: How has the rise of remote and hybrid work changed the way tech workers organize? To answer this question, we interviewed twenty-nine labor organizers—including full-time employees, contractors, and gig workers—in the US tech industry. What we found is that the pandemic changed the way organizers think about digital safety.  Digital safety is an important consideration in any organizing campaign, not just those in the tech industry. How do you secure your texts, chats, and emails from the prying eyes of the boss? But as the pandemic upended the world of white-collar office work, and everything shifted online, organizers had to adapt. In a more digitized workplace, the role of digital safety changed. Organizers moved from thinking about digital safety in individual and technical terms towards thinking about it in collective and social terms.


Prompt: Organizing in the “Before” Times Political, grassroots, and union organizing have long prioritized face-to-face conversations, and with good reason: it works. Even in the age of mass phone-banking and Twitter, organizers know that meeting people one-on-one and really talking to them can change their minds and mobilize them for action.  Direct interaction can also be safer. The dangers of organizing a workplace are real: people risk losing their jobs, which means losing income, access to healthcare, and, for some, the right to remain in the country. In person, workers can strike a balance between candor and confidentiality. They might intuitively know when and where to carry out sensitive conversations out of earshot of the boss: at the watercooler, an offsite cafe, a local dive bar, or a colleague’s backyard. Chats can start with casual topics, amidst the regular day-to-day grind, and continue into a deeper conversation about collective action. By meeting regularly in person, coworkers can build the relationships that are pivotal to a successful collective action.  Before the pandemic, organizers in the tech industry applied these insights widely. For example, organizers at Kickstarter used to meet at a colleague’s place after work to discuss strategy for their union campaign and to form closer bonds with their colleagues: “We [would] pile into [our colleague’s] studio around the corner from Kickstarter. This would be a turning point, a critical step toward unionization.” — Alex Kennedy, former Kickstarter union organizer (quoted from Kickstarter United Oral History) In this model, since relationship-building happened in person, digital spaces were reserved for coordinating meetings and collaborating on documents. As a result, organizers tended to see digital safety in technical and individual terms. For instance, they might ask coworkers to avoid using their work devices for organizing conversations and to use an encrypted messaging app such as Signal instead. In many ways, they faced a classic information security problem: If my employer finds out I’m organizing, I could be disciplined. How do I keep that knowledge from my employer?

Response: Screen Time With the pandemic, this in-person-first model of organizing fell apart. For white-collar tech workers, work—and organizing—went online, blurring the boundaries between the two. Organizers lost the ability to talk to their colleagues in person. It became much more difficult for workers to do outreach or to form relationships.  “[We] didn’t have advice on how to approach remote organizing. Casual conversations became difficult—in the office, you could take a walk, get coffee or lunch. [With remote work,] relationships were less solidified—they were more work-focused and tenuous. People tend to be a bit cagey on work calls.” — Media strategist at a software development firm To make matters worse, as the pandemic normalized remote work, employer surveillance became increasingly common. Without the ability to shoulder-surf or eavesdrop at the water cooler in physical workplaces, bosses started to get concerned about drops in productivity amidst the shift to working from home. So, many employers started using productivity monitoring software—several interviewees mentioned that their employers installed tracking software on their work devices after the pandemic began. While this move was purportedly motivated by productivity concerns, organizers feared that the same software could be used to track union-related conversations.


Prompt: Even something as simple as using Zoom to meet with coworkers became complicated. For instance, if a colleague accidentally joined a union meeting from their work account, they would create a record that could potentially be tracked by management. These challenges forced organizers to go back to the drawing board to rethink their approach. They had to reassess how organizing could happen in a remote world, with no precedent to guide them. And as they did, their views on digital safety changed. The pre-pandemic way of thinking about digital safety was too narrow to address the risks that organizers faced in a remote world.

Response: Community Management What does it mean to organize “off work platforms” when you can only use work devices for your job? What if the only way to reach out to your coworkers is via chat platforms used at your workplace, or on your employer’s Zoom?  Setting standards and expectations for individual behavior was no longer reasonable. Organizers had to find ways to evade the newly extended reach of employer surveillance. They turned to their fellow tech workers to explain the capabilities that management might have when it came to spying on workers, and quickly learned how to fight back.


Prompt: But the boss wasn’t the only source of risk in a remote workplace. Without being able to meet face-to-face with their coworkers, organizers also struggled to build relationships. Before the pandemic, such relationships had sustained collective action campaigns. With the lack of a shared physical space, many of these faded away.

Response: “It’s harder to do this over a video call than it is in person, because in person you’re gonna see them in the office again in the real world, and it continues to humanize and endear you… When it’s a video call, it’s more structured and a little less humanized.” — Employee at a marketing firm Online, organizers had to find a way to recreate the offline spaces that held collectives together. Gig workers were pioneers in this space, as they had been an all-remote workforce since long before the pandemic: “[Gig workers were] this remote, atomized workforce that had no shop floor or break room, no true ‘workplace,’ and really no clear, obvious way of connecting with fellow workers. We had to digitally recreate the infrastructure that exists in a traditional workplace, a place where workers could come together to communicate together and express outrage about things and their relationship with their labor, their relationship with their company.”  — Gig worker at an online delivery platform Organizers began creating online spaces for relationship-building on Slack or Discord. Knowhow on community management became essential; for instance, workers who had previous experience moderating large online communities had a major advantage. Codes of conduct, community standards, and other social expectations in these new spaces had to be established and enforced. Certain individuals acted as community moderators, warning colleagues about sharing sensitive details in the chat, as well as working to foster a sense of togetherness.


Prompt: “We have a policy of not mentioning names [in our online organizing spaces]… We’re trying to be impersonal, talk about systems and structures. Individuals are only actors in that.” — Engineer at a software development firm “I saw how important it was to build community in general. The place we can do it is the place we can spend most of our time. Since we don’t have a local community [due to the pandemic,] I wanted to dedicate my time to the one I had to be involved with. I felt more motivated at work, showing up for it, and getting to get to know people in this way as a personal thing.” — Employee at a non-profit organization For organizers, setting the tone for conversations became key to safeguarding the wellbeing of the collective. They tried to strike a balance between allowing people to vent about working conditions while also trying to foster an uplifting, hopeful tone.

Response: Finding Abundance in Moving Together The rise of remote work forced organizers to broaden their definition of digital safety. While technical considerations and individual behavior still mattered—using the right encryption, promoting the right privacy practices—their focus shifted to a more collective and more social understanding of digital safety. Building trust online required creating spaces where people felt safe together, and creating that feeling required, above all, community management: “It was more about a sense of trust with other people that contributed to a feeling of safety than the platform.” — Employee at a non-profit organization While white-collar tech workers are not working from home as much as they were during the height of the pandemic, the patterns of their professional life have been permanently altered. Remote and hybrid work are here to stay. This means that organizers will have to become adept at orchestrating collective action campaigns within distributed teams that may afford little opportunity for direct interaction. The tactics developed during the pandemic will be critical for this work, as organizers seek to build on successful campaigns at places like The New York Times and Kickstarter to push deeper into the sector. An expanded notion of digital safety, one that emphasizes the collective and the social, will play an important role. To borrow a phrase from one organizer, true safety means finding abundance in moving together.


Prompt: Saturday, February 15, 2003 was an unprecedented day in world history. From sunrise to sunset, an estimated thirty million people in nearly 800 cities across the globe hit the streets in a coordinated effort to stop the US-led war on Iraq. F15, as the anti-war protest became known, remains the largest global demonstration in history.  Like all protests, F15 was orchestrated by a movement. And this movement did something unprecedented for its time: it used the internet to coordinate a coalition of hundreds of popular organizations across the world. In fact, the real mouthpiece of F15 was a grassroots network called Indymedia, composed of around 200 local “independent media centers” (IMCs) that published citizen journalism on the web.  Indymedia had launched three years earlier in Seattle during the protests against the World Trade Organization in 1999. Within a few years, it grew to become a global collection of community-run newsrooms devoted to covering social and political issues from a left-wing perspective. Indymedia maintained a global website at indymedia.org, which aggregated content from the many place-based Indymedia sites that were run by the local IMCs, such as indybay.org, based in the San Francisco Bay Area. The project was volunteer-run and committed to anti-corporate journalism. And, prefiguring the explosion of social media, Indymedia emphasized user-generated storytelling, as captured by the slogan: Don’t hate the media, be the media.

Response: On the day of the F15 protests, the headline on the global Indymedia website read: “Millions March Worldwide to Denounce Bush’s War Plan.” The article listed more than eighty protests worldwide, with links to the associated stories from the local Indymedia sites. Following the initial story, there was a regional roundup, which went into greater depth about the protests in different parts of the world, alongside a collage with over 200 photos from across the globe.  “Indymedia, with reports for all of the biggest demonstrations and many of the smallest, wove hundreds of separate actions into a single story,” wrote New York City Indymedia activists Josh Breitbart and Mike Burke in a piece for Clamor magazine. “As popular uprisings from around the world begin to coordinate their actions, [I]ndymedia is proving to be an essential tool for imagining this new community.” As Breitbart and Burke suggest, F15 would not have been possible without Indymedia. It is not a stretch to say that Indymedia gave form to the global anti-war movement in 2003, just as it gave form to the anti-globalization movement a few years earlier.


Prompt: History has sped up since 2003. In the decades since, the world has witnessed multiple interlocking calamities—from the financial crisis of 2008 and climate change-related disasters to worsening economic inequality, a failed response to Covid-19, and the growing authoritarianism and instability of our flawed democracy. We have also witnessed a powerful organized response to these crises across the world—from the Arab Spring and Occupy Wall Street to Black Lives Matter and the Umbrella Movement, as well as the growing militancy of the US labor movement.

Response: Yet, in the current conjuncture—with mobilizations proliferating—a visionary, graspable, commonsense alternative to neoliberalism seems out of reach. Why? And why, twenty years ago, was the antiwar movement of 2003 more unified in its opposition, more coordinated and connected in its resistance, and more coherent in its analysis than contemporary movements? A large part of the answer is that movements are no longer reported on, and sustained by, a vibrant grassroots media network like Indymedia. Instead, organizers have given up on building their own media infrastructure and embraced corporate social media platforms. In fact, two of the biggest US movements of the last decade—Occupy Wall Street and Black Lives Matter—are known best by their Twitter hashtags.  This reliance on corporate platforms has transformed movement media strategy. Rather than devoting energy to a collective enterprise, individual movement organizations prioritize “getting their message out,” a strategy governed by social capital, competition, and a political economy of clicks. This strategy may sometimes be necessary to achieve certain short-term ends, but it is deeply detrimental to the long-term project of building a meaningful and shared resistance. While we have seen a great deal of movement activity over the last decade, movements have generally been siloed from one another, internally fragmented, and fleeting. This is a direct outcome of their overreliance on corporate social media platforms.  The stark reality is that the media strategy of our social movements has regressed over the past decades. Indymedia represented a far more effective way to use the internet to advance political struggles. While we can’t simply go backwards, any strategy for building a better media strategy requires learning the lessons of the Indymedia era and understanding what was lost in the transition to corporate social media.


Prompt: Don’t Hate the Media, Be the Media (1994–2005) The roots of Indymedia lay in Chiapas. It was there, in southern Mexico, that a Mayan peasant movement called the Zapatista National Liberation Army (EZLN) announced its “dignified rebellion” to the world in 1994. While waging a guerilla war against the Mexican state over land and life, the Zapatistas and their supporters also began experimenting with the internet.  In particular, they utilized email lists called listservs that had been created for the movement against the North American Free Trade Agreement (NAFTA) to publicize their message. Soon, they created their own listservs, which they used to circulate stories of their struggle across communities and borders, gaining global support, and successfully shifting the balance of power away from the Mexican state. In fact, the online activities of the Zapatistas were so successful that the RAND Corporation published a report for the US military entitled “The Zapatista Social Netwar in Mexico” (1998). In the report, the authors warned that the Zapatistas were redefining conflict in the information age and that it could have dire consequences, possibly threatening US dominance.

Response: Unlike the RAND Corporation, which was concerned with the disruptive effects of “social netwar,” the Zapatistas saw the internet as a new participatory medium that could create linkages across space and struggle, forging a singular front of resistance. The Zapatista strategy was captured by the phrase “one no, many yeses.” This principle—centered on the idea that a diversity of movements and visions of emancipation could be connected into a unified opposition to capitalism—offered a revolutionary way to think about the internet. Movements could attract support and build alliances by telling their stories online.  To awaken others to these radical possibilities, the Zapatistas hosted an ambitious global “encounter” (encuentro) in Chiapas in the summer of 1996. Movement people from across the world attended, and, in their closing statement, Zapatista leaders famously declared: “We will make a network of communication among all of our struggles and resistances… This intercontinental network of alternative communication will be a medium by which distinct resistances communicate with one another.”  Activists and media-makers across North America and Europe began trying to bring this vision to life. In 1997, the Freeing the Media Teach-In was held in New York City, which included a virtual keynote address from Zapatista spokesman Subcommandante Marcos, where he reiterated the call to build a “network of independent media.” Two years later, in 1999, the Grassroots Media Conference was held in Austin, Texas. It became a strategy session for launching Indymedia, which went online later that year with the first IMC in Seattle.  The Seattle IMC included a newspaper, radio station, and a video collective. But the cornerstone of the IMC was an “open-publishing website” where media-makers and activists could post their stories to the website, and see those stories appear in real time. Today, such user-generated content is ubiquitous, but at the time, Indymedia’s use of it was transformative. As Matthew Arnison, an Australian software engineer who played a pivotal role in building the site, explained, “It seemed like we were opening this huge flower, and all this passion and energy and stories were just pouring through because they had been locked for so long.”