Proceedings of the 2007 international ACM conference on Supporting group work
Bibliographic record
Abstract
We are pleased to host Group 2007, a conference that combines world-class research contributions with a beautiful conference location. Group 2007 is part of a series of bi-annual high-impact conferences on computer-based systems that have an impact on groups, organizations, and social networks. For the third time, the conference is being held on beautiful Sanibel Island, on the southwest coast of Florida. Group 2007 will foster a discourse on collaborative technology that bridges the fields of Computer-Supported Cooperative Work (CSCW) and Information Systems (IS). Group 2007 brings together researchers and practitioners from different areas working on the development, introduction, management, deployment, and analysis of computer-based collaborative systems. In these proceedings you will find contributions from academia and industry, including 38 papers (full-length contributions) and 9 notes (papers with smaller yet equally innovative contributions) selected from a record number of submissions (132 papers / 25 notes). This year's program will feature topics including theories of cooperative work and analysis of complex work settings, general questions of interactions in groups for working and learning, awareness and sharing support, technical issues, as well as social computing including social tagging and wikis. Beyond papers and notes, we are excited to host the first ever Doctoral Consortium at Group, sponsored by NSF and Microsoft Research. Thirteen PhD students from five different countries have been selected to participate in a full-day workshop with leading experts in the field. Continuing from previous years, Group 2007 is pleased to also offer workshops, posters, and an opening plenary, not to mention lots of fun in the sun.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.149 | 0.088 |
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".