Bibliographic record
Abstract
Over the past 16 years, a legislative and policy framework has evolved in Canada to address systemic discrimination in employment in the federal jurisdiction, and in organizations that sell goods or services to the federal government. Data collected pursuant to the Employment Equity Act, as well as published literature and government documents, are reviewed in order to provide a critical analysis of the federal policy framework as set out in 1987 and revised in 1996. This review is the basis for assessing both progress and lack of improvement in the employment status of racial minority, aboriginal, and disabled women and men, as well as white women, within the federal sector. Reasons for limited results are proposed, and issues posed by contemporary labour market trends are identified. It is argued that the results of employment equity policy are disappointing because the policy is not being implemented by employers and effectively enforced so that there are consequences for employers’ failures to comply. In other words, there is a persisting gap between employment equity policy and practice. This gap presents difficulties in evaluating the content of employment equity policy, since it is not possible to evaluate a policy that is not implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".