Special Edition / Edition Speciale Crossing Borders / Crossing Boundaries Editors’ Remarks
Bibliographic record
Abstract
In October 2005, one of us (Cousins) had the distinct pleasure of serving as co-program chair with Michael Patton for "Crossing Borders, Crossing Boundaries," the joint meeting of the Canadian Evaluation Society and the American Evaluation Association.With 2,400 delegates, this meeting in Toronto turns out to have been the single largest evaluation conference ever held.Cousins and Patton saw the conference theme as an opportunity to dialogue with evaluation colleagues from around the world and as an invitation to refl ect on our emergent global community of practice.It was an occasion to examine our larger societal responsibilities as evaluators and to delve deeply into the diverse and complex challenges of twenty-fi rst century evaluation practices, methods, models, theories, and contexts.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.056 | 0.058 |
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".