Canada's Employment Equity Acts and the communications industry: effective social regulation in a neo-liberal era.
Bibliographic record
Abstract
"Canada's Royal Commission on Equality and Employment drafted in the early 1980s and the two versions of the Employment Equity Act it later inspired can be understood within this shift towards social regulation as defined by Nementz et. al. To appreciate how Canadian corporations are now mandated to achieve progress towards employment equity, it is critical to its history, its incarnations and its impact on corporate Canada. Curiously, while there was a sizeable amount of quantitative and qualitative research endorsing legislated employment equity written prior to the initial Act, there is only a handful of academic research evaluating its success. Academic space devoted to employment equity has existed mainly as a sidebar in a more extensive analysis of other policies such as the key works of Judy Fudge, Anver Saloojee, Patricia McDermott and Annis May Timpson which appraise employment equity, but as a benchmark against which to compare to other policies such as child care and pay equity. Through a literature review of the primary and secondary documents, which respectively shaped and critiqued the Act's two manifestations as well as case studies of communications companies, I will show that this legislation - an example of social regulation in a neo-liberal era - was particularly effective once an audit component was added."--Page 3.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".