Evaluating Collaborative Enterprises - A Workshop Report
Bibliographic record
Abstract
As part of the the 10th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaboration conference (WETICE 2001), the Workshop on Evaluating Collaborative Enterprises continued from its debut last year exploring the issues surrounding the evaluation of collaborative enterprises. This exploration included methods and tools for evaluating collaborative software as well as application-specific evaluation experiences. This paper describes the workshop's mission, summarizes the nine workshop presentations and one demonstration, and presents the main themes emanating from this workshop. These include the need for integrated theories, the need for advances in evaluation techniques and tools, and the need to confront non-controlled variables in case studies. 1. Participants In addition to the co-chairs, the following people participated in this workshop and contributed to the contents of this report: Kristina Buckley, The MITRE Corporation; Jeffrey Campbell, University of Maryland, Baltimore County; Jill Drury, The MITRE Corporation; Mark Klein, Massachusetts Institute of Technology; Julian Newman, Glasgow Caledonian University; David Pinelle, University of Saskatchewan; Elaine Raybourn, Sandia National Laboratories; Diane Sonnenwald, University of North Carolina, Chapel Hill; and Brent Stewart, University of Washington, School of Medicine. In addition to the co-chairs, the following people served on the program committee. We gratefully acknowledge their time and energy: Janet Allen, Georgia Tech; Jeffrey Campbell, University of Maryland, Baltimore County; Jill Drury, The MITRE Corporation; Saul Greenberg, University of Calgary; Carl Gutwin, University of Saskatchewan; Doug Johnson, Hewlett Packard; Amy Knutilla, Knutilla Technologies; Emile Morse, NI...
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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 | 0.080 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".