You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


The gender binary-that is, the rule that everything (oh animals, jobs, food, kleenex, housework, sound, games, deordorant, love and sex, candy, vitamins, etc) gets split into male and female-is fractal. That means that, for every male or female version of something (say sports versus dance), there is a further gendered split that can be made. If we take sports, we might divide it into the masculine football and the feminine swimming. If we take swimming, we could probably divide it down further. Take education (which is, arguably, feminized): we can split it into physical sciences (masculine) and social sciences (feminine). And we can split the physical sciences into biology (dominated these days by women) and physics (dominated by men). So the gender binary has a fractal character.

This strongly resonates with me. It doesn’t mean that I think this is how things should work, but I think it’s often part of how they do work. Do you find this in your geekdoms of choice? How does it split up your geekdom? Have you seen your area of the fractal shift over time and do you have any theories about why; for example, did the arrival of more women or more men make something more feminine or masculine? How explicit is it? (In Free Software it can be very explicit and essentialist. “Women are good at words and other people, so documentation is more feminine. Women are bad at mathsâ€Šâ€”â€Šwhich is basically the same thing as computer programming right?â€Šâ€”â€Šand at isolated work, so coding is more masculine. QED.”)

Update: Just a note that the question is not precisely “where do the women cluster in your geekdom?” (although that’s interesting too), but “which parts of your geekdom are considered more suitable for women/more womanly/less manly?”

19 thoughts on “Quick hit: the gender binary fractal in geekdom”

I am a computational scientist at a large supercomputing facility, and in my experience, the applied math/numerical analysis field is more populated by women than are other related fields, like the development of supercomputer technology, tools, programming languages, etc., or the scientific fields that use supercomputers. And within the applied math/numerical analysis field, it seems from my perspective that women are well represented in numerical optimization, possibly because there have been some great women pioneers in this field such as Margaret Wright. I doubt that women represent anywhere near 50% of the practitioners but we do have good representation when it comes to the names of great optimizers working today.

This is an extremely rough/crude generalization, but my experience has been that the lower in the stack you go the fewer women you encounter. More women in application protocols than in, say, PHY, more women doing applications software than operating systems, etc.

Not only is it fractal, but the patterns tend to be aligned with traditional gender-stereotypes.

For example, you could say that technology-companies have few women overall, and that’d be true. But if you look closer, you notice that the ones who -are- there, tend to cluster around the more traditional female roles. We’re a tech-company, and at first glance appear not-so-bad, with around 35% females.

But most of them are in design or human resources. Amonth the ~third of the employees who are programmers, there’s -1- female. (and around 10 males)

I’m thus thinking the gender-gap is worse than it may appear, because numbers like “only 35% female students in natural sciences”, disguise the fact that even those that are there, tend to gravitate towards the stuff that’s more traditionally female, say biology rather than physics, user-interface-design rather than device-drivers.

You do need to consider feedback effects though. I don’t think, for example, that it was inevitable that biology was always going to be “girl science”, the rationalisations of it being feminine (say, “it’s to do with life! and so women who are caring, will care!”) are post hoc: explaining the presence/preponderance of women, not attempting to predict it before they arrived. Women weren’t supposed to be able to do science at all.

I’m involved in nordic style-LARP, and have noticed that a lot of the time male larp writers fall into the role of auteur, while women end up doing a lot of less glamorous behind the scenes work. They pretty much function as producers, but don’t gain the respect that that title would imply. This resonates with my experience from web advertizing school where the male students gravitated toward an identity of entrepeneur/creative, and the women somehow always ended up making sure everyone was on time for the meetings.
I’ve never felt actively pressured to stay away from the “boy’s stuff”, I think it’s a more subtle case of expectations – the times I have ventured into the “artiste” part of LARP geekdom I’ve just felt really VISIBLE and open to criticism.

The film industry tends to fall along these lines too: men direct, women produce. A more balanced but still skewed line happens in television. I’m not sure why this is–some combination of more opportunities/less fiscal responsibility, maybe, or perhaps it’s more subtle.

Salaried and consultant software technical writers are mostly women, at least judging by my previous employers. This holds true among API and programming writers, not just for the screen-shot GUI writers.

I think the split among book writers (O’Reilly and so on) may be different.

In chemistry, organic synthesis is a very male-heavy subfield; most flavors of biochem are relatively female-heavy. I believe that in most subfields, PhDs will be more likely to be male, and assistant/associate level workers will skew more female.

In fiber arts, it seems that making tools is masculine and using them is feminine. I know of no well-known women who make spinning wheels or drum carders, for instance. Raising and preparing fiber (I think) is more balanced.

In computer science, I’d say that operating systems is the canonical “boy stuff” subspeciality and AI “girl stuff”, together with HCI.

This is rather relative of course: probably the big names that occur to people from AI are all men, but within computer science I think if you meet a woman and she says she’s in AI, computational linguistics or similar subspecialties it’s a little more “oh, of course you are” than if she says she’s in operating systems. I’d love to hear how operating systems itself subdivides…

Sticking with HonoriaG. In my geekdom I’ve come across far more women who like LARPing as opposed to Table Top RPGs. I haven’t had a lot of conversations as to why that is, it could be the fact that creepy smelly basements are usually involved, but there are a couple of groups in Toronto where women play a large part in the playing.

However, as also was mentioned they don’t spent a lot of time with the Plot team again for a lot of the reasons stated.

My first thought is how women in bands are more likely to play bass than be the drummer. which doesn’t mean there aren’t kick-ass female drummers, but it’s totally expected when they play bass. I certainly count this among the many reasons I love to play drums in Guitar Hero, as well as wanting to learn to play real drums someday.

In my work environment, I am in a more technical group where we have 2 women including me which is I think the highest saturation to be found in the teams of the engineering section in our company. We spend a lot of time working with all OSes on various real and virtual machines. I find that the deeper level of hardware set-up is more picked up on by the males – so knowing that particular chipset serial number, or obscure filesystem is being used in the nether reaches of a mobile phone. There’s not an obviously female counter to this, but I’m often called the “outreach” person who is a bridge between our group and the other teams – so maybe that makes communication itself more female? I don’t believe this to be true since many people can and do communicate well, but it’s probably an offshoot of the “women write documentation” phenomenon where I’m often trying to make sure that we leave a clear understanding of what we’ve done behind us for the next folks who will try to do our jobs.

It’s not clearcut but in my workplace and considering the people I went to uni with there is a tendency for women to head for project management and testing whereas men tend more towards development. This is just a guestimate and by no means an actual head count, it may be that I just happen to know a lot of women who ended up in testing/project management (and I know quite a few men who ended up there as well now that I think about it).