In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conducted in three parts: first, an initial survey provided broad insights into how calendars were used; second, this was followed up with personal interviews of a few participants which were transcribed and content-analyzed; and third, examples of calendar artifacts were collected to inform our analysis. Findings from our study include the use of multiple reminder alarms, the reliance on paper calendars even among regular users of electronic calendars, and wide use of calendars for reporting and life-archival purposes. We conclude the paper with a discussion of what these imply for designers of interactive calendar systems and future work in PIM research.
Jones @cite_33 framed the problems in PIM in terms of the canonical tasks of , , and . Keeping any kind of information involves a tradeoff between the likelihood it will be recalled in the future, and the costs of capturing and retaining it. Organizing involves filing it away such that it can be retrieved easily in the future, and while keeping the cost of organizing less than the cost of finding. Re-finding is a different problem from finding, since there are aspects to encountered information that make it personal.
@cite_33
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To keep or not to keep? People continually face variations of this decision as they encounter information. A large percentage of information encountered is clearly useless — junk e–mail, for example. Another portion of encountered information can be "used up" and disposed of in a single read — the weather report or a sports score, for example. That leaves a great deal of information in a middle ground. The information might be useful somewhere at sometime in the future. Decisions concerning whether and how to keep this information are an essential part of personal information management. Bad decisions either way can be costly. Information not kept or not kept properly may be unavailable later when it is needed. But keeping too much information can also be costly. The wrong information competes for attention and may obscure information more appropriate to the current task. These are the logical costs of a signal detection task. From this perspective, one approach in tool support is to try to decrease the costs of a false positive (keeping useless information) and a miss (not keeping useful information). But this reduction in the costs of keeping mistakes is likely to be bounded by fundamental limitations in the human ability to remember and to attend. A second approach suggested by the theory of signal detectability is relatively less explored: Develop tools that decrease the likelihood that "keeping" mistakes are made in the first place. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Scientific research performed with the involvement of the broader public (the crowd) attracts increasing attention from scientists and policy makers. A key premise is that project organizers may be able to draw on underused human resources to advance research at relatively low cost. Despite a growing number of examples, systematic research on the effort contributions volunteers are willing to make to crowd science projects is lacking. Analyzing data on seven different projects, we quantify the financial value volunteers can bring by comparing their unpaid contributions with counterfactual costs in traditional or online labor markets. The volume of total contributions is substantial, although some projects are much more successful in attracting effort than others. Moreover, contributions received by projects are very uneven across time—a tendency toward declining activity is interrupted by spikes typically resulting from outreach efforts or media attention. Analyzing user-level data, we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work. Although top contributor status is earned primarily through higher levels of effort, top contributors also tend to work faster. This speed advantage develops over multiple sessions, suggesting that it reflects learning rather than inherent differences in skills. Our findings inform recent discussions about potential benefits from crowd science, suggest that involving the crowd may be more effective for some kinds of projects than others, provide guidance for project managers, and raise important questions for future research. Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.