Program-Evaluation Criteria Applied to Pay Equity
Bibliographic record
Abstract
Nous évaluons l’expérience ontarienne de l’équité salariale en fonction d’un groupe de critères établis pour l’évaluation de programmes. Les critères eux-mêmes sont instructifs mais ils fournissent en outre un moyen systématique de catégoriser les questions qu’implique l’évaluation de l’équité salariale en général, et l’expérience ontarienne a le mérite de fournir une illustration des principes d’évaluation. Les critères d’évaluation sont d’abord esquissés, puis appliqués à l’équité salariale en général et à l’expérience ontarienne en particulier. Des illustrations provenant d’études de cas sont utilisées pour mettre en lumière quelques unes des questions que pose l’évaluation de programmes. Nous concluons par un examen des leçons qui peuvent être tirées de l’expérience ontarienne. The Ontario experience with respect to pay equity is evaluated based on a set of program-evaluation citeria. Such criteria are informative in their own right, but they also provide a systematic way of categorizing the issues that are involved in evaluating pay equity in general, with the Ontario experience providing a useful illustration of the evaluation principles. The program-evaluation criteria are first outlined, with an application to pay equity in general and the Ontario experience in particular. Illustrations from some case studies are used to highlight some of the program-evaluation issues. The paper concludes with a discussion of lessons that can be learned from the Ontario experience.
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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 | 0.048 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".