Bibliographic record
Abstract
Information and knowledge have become a crucial resource in our knowledge-based, computer-mediated economy. But knowledge is primarily a social phenomenon, on which computer processing has had only a limited impact so far, in spite of impressive advances. In this context have recently appeared various collaborative systems, that promise to give access to socially-situated information. We argue that a prior analysis of the social context is necessary for a better understanding of the whole domain of collaborative software. We will examine the variety and functions of information in modern society, where collaborative information management is now the dominant type of occupation. In fact, real information is much more complex than its usual technical sense: one should distinguish between information and knowledge, as well as between explicit and tacit knowledge. Because of the importance of tacit knowledge notably, social networks are indispensable in practice for locating relevant information. We then propose a typology of collaborative software, distinguishing between explicit communities supported by groupware systems, task-oriented communities organized around a common data structure, and implicit links exploited by collaborative filtering and social information retrieval. The latter approach is usually implemented by virtually grouping similar users, but there exist many possible variants. Yet much remains to be done by extracting, formalizing and exploiting implicit social links.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".