Implementing proactive gender pay equity legislation : a study of Icelandic and Canadian pay equity legislation and its future impacts on the gender wage gap
Bibliographic record
Abstract
This study will be looking into the primary factors that influence the implementation of gender pay equity legislation. The central focus will be on Icelandic companies, however to understand the universality of these implementation factors, strategies, and barriers, Canadian insights will also be included in the analysis. \nThe key elements investigated in this study include, organizational gender demographics, implementation strategies, implementation barrier, key success factors, and potential long-term implications of gender pay equity legislation. The purpose of this study is to examine how organizations are applying gender pay equity legislation, and whether or not this legislation will successfully address the factors contributing to the gender wage gap. \nThe findings of this study suggest that occupational gender segregation continues to play a large part in the gender wage gap. It was also found that there are similar techniques being used to implement gender equity legislation in both Iceland and Canada. In particular, having well-structured documentation and the use of outside consultants have been identified as important factors in the implementation process. In terms of barriers and challenges, Iceland presented with fewer internal barriers than Canada, partly due to higher levels of internal support and stricter legislative enforcement. \nThe current legislative movement towards achieving equitable compensation for women and men in Iceland is a positive change, however, it is too soon to tell if it will be effective enough to eliminate the wage gap. The dominant sentiment amongst participant was that through the new legislation is a step in the right direction, it will not have a major impact on their businesses or industry as a whole. It was concluded that larger societal changes are needed to correct the underlying causes of wage discrepancies.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".