Debating Professional Designations for Evaluators
Bibliographic record
Abstract
This paper provides a reflective account of a consultation process on professional designations for evaluators initiated and coordinated by the Canadian Evaluation Society (CES). Described are (1) the forces leading CES to generate discussion and debate about professional designations for Canadian evaluators, (2) the process of developing and implementing a request for proposals for a tentative action plan that would serve as a basis for debate, and (3) the subsequent development and implementation of a pan-Canadian, tripartite consultation process that involved CES members, CES Chapter organizations, and partners of the Society (including evaluation users). The paper concludes with reflections on the process and a summation of lessons learned.
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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 | 0.489 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.038 | 0.061 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".