Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the evolution, implementation and effectiveness of the Pay Equity Act in Ontario, Canada. Given that this Act is considered by many as the world's most progressive equal pay for work of equal value legislation, there are important implications for policy globally. Design/methodology/approach Through a review of relevant documents and the literature, the paper examines the need for the Pay Equity Act in Ontario, its origins, and with two decades of experience, analyze its effectiveness. A case study is also used to assess related procedures and effects of the law. Findings In spite of its limitations and the wide pay gap that still exists between men and women, many female workers have benefited from Ontario's progressive Pay Equity Act. In targeting the discriminatory aspect of women's work evaluations, the Act has resulted in pay increases for thousands of women, especially in the public sector. Practical implications There are many practical and social implications for jurisdictions across the globe, as they try to grapple with gender pay equities. Policy makers can learn from the successes and challenges experienced in Ontario. Pay equity legislation will unlikely achieve any significant progress in reducing the wage gap if it relies on workers to complain about the inequity in their pay. A proactive pay equity law, such as that in Ontario, will force employers to make more focused efforts to deal with gender pay discrimination. Ontario's bold “experiment” with pay equity holds valuable lessons for jurisdictions globally. Originality/value While there has been some research on the Ontario Pay Equity Act, there is a paucity of scholarly work that examines the details of the pay system that the Act has spawned. There is also little work in assessing the effectiveness of the legislation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".