A meta-analytic review of gender composition influencing employees’ work outcomes: implications for human resource development
Bibliographic record
Abstract
Drawing from Kanter’s tokenism theory, the current meta-analysis provides a statistical synthesis of the research linking gender composition of the workplace to men and women’s evaluative (leadership, rewards, and performance) and affective (interpersonal relationships, stress, and attitudes towards women) outcomes. In addition, we examine the moderating effect of task gender-type on these relationships. Evidence for simple gender composition effects was weak, with only men’s interpersonal outcomes being associated with gender composition. In contrast, we found strong evidence supporting the moderating effect of task gender-type on these relationships for both sexes, across several of the outcomes. Notably, the strongest moderator effect was shown for men’s leadership, with a clear pattern demonstrating that gender composition has a stronger positive effect on this outcome for men performing gender-neutral tasks, compared to men performing masculine tasks. This underscores the importance of task gender-type as a more powerful indicator of workplace gender norms than a numerical representation of men and women. Despite progress towards gender parity in the workplace, gender hegemony remains strong in male-typed tasks as they stand impervious to the effects of gender composition. Results are discussed in light of tokenism theory and its implications on designs of HRD interventions.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".