Examining the boundary conditions of tokenism: within-occupation gender wage gaps and female representation in the Canadian labor market
Bibliographic record
Abstract
Purpose The purpose of this study was to examine the boundary conditions of Kanter's (1977) tokenism theory as applied to the gender wage gap. The authors aimed to discover if there was a point where the relationship between the percentage of women in a job category and the gender wage gap changed, and, if so, where the threshold was located and what was the nature of the shift in relationship. Design/methodology/approach The authors used the Andrews’ (1993) threshold effects technique. Using 22 separate years of publicly available Canadian wage data, they examined the relationship between the percentage of females in 40 unique occupational categories and the female-to-male earnings ratio (for a total of 880 observations). Findings The results showed the existence of a threshold point, and that early gains in percent female within an occupation, up to approximately 14% female in the occupation, associate with strong gains in the female-to-male wage ratio. However, beyond that point, further gains in percent female associate with smaller improvements in the female-to-male wage ratio. Practical implications The findings are useful in understanding the dynamics of occupational group gender composition, potential theoretical reasons for the nuances in relationship, as well as opportunities that may facilitate more equitable outcomes. Originality/value The results show that, though improvements were made above and below the threshold point, enhancements in the wage gap are actually larger when there are less women in the job category (e.g. tokens).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".