Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the experiences of marginalized women in achieving equal pay for work of equal value. The research focuses on Ontario, Canada, as this is a leading jurisdiction globally in implementing legislation on pay equity. It provides an opportunity to understand the lived experiences of women whom scholars have identified as particularly vulnerable in workplaces. Design/methodology/approach This is a qualitative research study. Twenty-three interviews were conducted with women defined as marginalized. Thematic analysis was used to analyze the data. Findings Three themes resulted from the analysis: early employment experiences, cultural challenges at work and inequities in pay. The authors found that not only do structural and organizational barriers limit the ability of marginalized women to achieve parity in the workplace but there also is a hidden social element that requires further investigation. Originality/value The gender pay gap is wider for marginalized women, even after three decades since pay equity legislation was implemented in Ontario. There is a dearth of research on why this is the case. This study adds to the literature by focusing on a broader set of factors, in addition to legislation, that must be considered when focusing on solutions to the gender pay gap.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".