Gender-Based Pay Inequality in the Canadian Insurance Companies
Bibliographic record
Abstract
AbstractThe problem in this study was gender-based pay inequality among women at Canadian insurance companies. The purpose of this hermeneutic phenomenological study was to understand gender-based pay inequality for women at Canadian insurance companies. Aigner's, Cain's, Arrow's, and Phelps' statistical discrimination theory guided the study. The theoretical foundation also included the human capital theory by Becker and Schultz. The research questions focused on understanding C-suite and V-level executives’ lived experiences of gender-based pay inequality and identifying effective strategies that should be adopted to overcome the problem at Canadian insurance companies. This study used purposeful sampling to recruit 15 C-suite and V-level executives from Canadian insurance companies. Twelve participants were necessary to reach saturation. Inductive coding started after the transcript verification. A thematic analysis of the 12 transcripts helped to establish six themes: statistical discrimination; limited efficacy; gender equality policies; standard, fair, and transparent hiring and promotion practices; actively seeking women in leadership positions; and antioppression training. Participants stated that the women’s remuneration was not congruent with their experience, education, training, or qualifications because of statistical discrimination. The implications for positive social change are that policymakers should ensure that gender pay equity laws get universally implemented in public and private companies on the federal and provincial levels. The policymakers at companies also need to adopt fair and transparent hiring and promotion strategies, implement antioppression training, and increase the quota representation of women in top-level positions and committees to influence positive social change further.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".