Gender equality in the workplace in Quebec: strategic priority for employers or partial response to institutional pressures?
Bibliographic record
Abstract
Purpose This paper examines the challenges posed for employers by gender equality in the workplace, in a seemingly favourable institutional context (the province of Quebec, Canada), and the reasons why employers adopt (or not) gender equality measures (GEMs) exceeding legal requirements. Design/methodology/approach The approach draws on both institutional theory and the strategic human resource management (SHRM) approach. Our research is based on a quantitative study involving human resource management professionals in Quebec (n = 296). Findings The results allow us to link GEMs with certain SHRM orientations (Yang and Konrad, 2011) and institutional pressures (Lawrenceet al., 2009). The findings show that, for approximately two-thirds of the employers in the sample, gender equality was not a strategic priority. Consistent with our hypothesis, a greater number of GEMs were found when gender equality was a strategic priority for the employer. Unionization and legal requirements were also positively correlated with the presence of GEMs. Originality/value The findings indicate a combined effect of SHRM and institutions on GEMs. They point out the relative dependency of employers on the pressures stemming from the institutional framework, and it captures some of the current challenges involved in adopting a SHRM approach with a view to achieving gender equality.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".