Bibliographic record
Abstract
This paper evaluates the effectiveness of Canada’s Employment Equity Act (EEA) from 2009 to 2021, building on earlier research by Ng et al. (2014), which analyzed the Act’s impact from 1987 to 2009. The EEA aims to improve the representation of historically disadvantaged groups — women, Indigenous peoples, visible minorities, and people with disabilities — in federally regulated workplaces. Through a comparative analysis of employment data, this study highlights both successes and shortcomings of the EEA over the past decade. Findings suggest that while the EEA has been effective in increasing the representation of visible minorities in both management and overall workforce positions, significant challenges remain. Women, though making strides in senior and middle management roles, have experienced declining representation in the broader federally regulated workforce. Indigenous peoples and people with disabilities continue to face persistent underrepresentation, both in management positions and across the general workforce, despite legislative efforts. While the EEA has contributed positively to the representation of visible minorities and women in leadership roles, it has largely failed to achieve proportional representation for all designated groups, particularly Indigenous peoples and people with disabilities, highlighting the need for stronger and more targeted policy interventions. Keywords: employment equity, representation, workforce
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".