Comparing Equity Policies in Canada and Northern Ireland: Policy Learning in Two Directions?
Bibliographic record
Abstract
Employment equity has existed in Canada for 20 years and fair employment in Northern Ireland, in its strengthened form, almost that long. A comparison of the policy frameworks begins with Northern Ireland’s adoption of Canada’s legislation as its model. Canada’s policy covers a limited proportion of its workforce and addresses disadvantage affecting women, racialized minorities, Aboriginal people, and persons with disabilities, while Northern Ireland’s policy covers most employees and targets inequality between Catholics and Protestants. Implementation and enforcement of Canada’s and Northern Ireland’s policies differ. In Northern Ireland substantial progress has been made toward employment equality between Catholics and Protestants, while in Canada the four target groups continue to face significant disadvantage. Policy learning was initially from west to east, but it is timely now to consider the case for policy learning from east to west.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".