Perceived justice as predictors of a successful pay equity plan: a Canadian case study
Bibliographic record
Abstract
Purpose – This study aims to identify the predictors of successful implementations of pay equity plans. Drawing on the perspective of organizational justice, this study highlights the factors that lead to the establishment of perceived fair pay for female-dominated jobs. Design/methodology/approach – Qualitative data were collected from 107 respondents in a Canadian company that implemented a pay equity plan as required by the Quebec Pay Equity Act. Findings – Justice perceptions of employees are based on: uniformity of implementation, bias suppression with respect to the right to fair pay, reliability of information on job content, relevance of job evaluation criteria, qualifications and impartiality of the pay equity committee members and the quality of employees’ representation and process transparency. Research limitations/implications – This study was conducted at a single workplace and among relatively highly educated respondents. Replicating the study may allow to verify the transferability of the results by considering workers’ demographic characteristics and organizational culture. Practical implications – The study highlights the cornerstones that may guide the development of an assessment tool of the effectiveness of pay equity processes. These results will additionally help employers to circumvent difficulties which may otherwise thwart the implementation of pay equity plans. Originality/value – This study highlights how employees construct their perceptions of justice in a specific context. It sheds light on the salient features of the pay equity implementation, the sources of information involved and the justice rules used. This study also highlights the specific forms of the justice rules in this context.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".