Proceedings of the 2010 ACM conference on Computer supported cooperative work
Bibliographic record
Abstract
Welcome to the 2010 ACM Conference on Computer Supported Cooperative Work! We hope that this conference will be a place to hear exciting talks about the latest in CSCW research, an opportunity to learn new things, and a chance to connect with friends in the community. We are pleased to see such a strong and diverse program at this year's conference. We have a mix of research areas represented -- some that are traditionally part of our community, and several that have not been frequently seen at CSCW. There are sessions to suit every taste: from collaborative software development, healthcare, and groupware technologies, to studies of Wikipedia, family communications, games, and volunteering. We are particularly interested in a new kind of forum at the conference this year -- the 'CSCW Horizon' -- which will present novel and challenging ideas, and will do so in a more interactive fashion than standard paper sessions. The program is an exciting and topical mix of cutting-edge research and thought in CSCW. A major change for CSCW beginning this year is our move from being a biennial to an annual conference. This has meant a change in the time of the conference (from November to February), and subsequent changes in all of our normal deadlines and procedures. Despite these changes, the community has responded with enormous enthusiasm, and we look forward to the future of yearly meetings under the ACM CSCW banner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.113 | 0.070 |
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 source (direct Gemma or distilled Codex), 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".