2 nd Annual Workshop on Collaboration Agents
Bibliographic record
Abstract
The world is changing, and Agents seem to be on everyone’s lips. The world is changing, collaborative technologies and collaboration in general, especially across distance, is becoming more and more important. Combining the power of autonomous, pseudointelligent agents and collaborative systems is not only a good idea, we think it’s vital to making the coordination, collaboration, and communication work for people. Agents can help people share information, schedule people’s time, negotiate privacy preferences, find information, and find people, amongst other things. It is these agents that we call ‘Collaboration Agents.’ COLA 2004, following on from a very successful COLA 2003 in Halifax, will examine collaboration agents from various angles – from how information can be shared to how agents can help in surveillance. As last year, we have had several excellent contributions and look forward to some active discussion. The workshop will not focus on, for example, the development of interface of search agents- forums exist to bring this research to the fore. Our interest is in the building of a community of researchers in the area to allow the growth of a highly exciting field, one in which agents help people come together. A special issue of Computational Intelligence has been negotiated, and all of the papers from COLA 2003 and 2004 are eligible to be revised and submitted. Please bear this in mind when you continue your work. COLA 2004 has a website, and all submissions will be online:
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 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 | 0.000 | 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".