The Malevolent Mask of Meritocracy in Perpetuating Gender Disparities within the Canadian Transit Industry
Bibliographic record
Abstract
Gender leadership and pay differentials continue to plague women employed in the male-dominated Canadian transit industry despite focusing on equal pay and gender equity strategies. We conducted a sequential mixed-method study of Canadian women within the transit industry to help elucidate the hidden contextual, social, and organizational factors contributing to persistent gender disparities. For the qualitative phase of the research, women in senior leadership positions (n = 9) participated in semi-structured interviews guided by and analyzed using grounded theory (Charmaz, 2014). The interview results informed the quantitative phase where women in various roles within the transit industry (n = 50) completed online surveys measuring experiences at work, performance evaluations, and opportunities for professional growth. Our results support the exacerbating role of meritocracy that helps explain continued constraints and barriers for women from attraction and retention to promotion and leadership. Women are pressured to conform and perform, often at the cost of authenticity, opportunities for advancement, and well-being to survive within meritocratic establishments in order to ascend into C-Suite jobs. The results of this study have practical implications for transit service organizations that are enacting Equity, Diversity, and Inclusion strategic plans.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".