Evidence and equity: Struggles over federal employment equity policy in Canada, 1984-95
Bibliographic record
Abstract
Abstract: This article traces debates about federal employment equity policy in Canada in the 1980s and 1990s, focusing specifically on the role of data and statistics in policy-making. The authors interpret policy-makers' extensive use of evidence-based policy instruments in the implementation of employment equity as an attempt to offer a technical solution to the deeply politicized problem of workplace discrimination. By exploring policy debates from the Royal Commission on Equality in Employment (the Abella Commission) (1984) to the passage of the reformed Employment Equity Act in 1995, the authors show how recourse to evidence-based deliberation failed to contain political conflict, because the meaning and use of statistical data became the object of political struggle among the main policy stakeholders. The article concludes by considering the implications of this case study for the broader comparative debate on the role of evidence-based methods in policy-making.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".