Gender inequalities in the workplace: the effects of organizational structures, processes, practices, and decision makers’ sexism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gender inequality in organizations is a complex phenomenon that can be seen in organizational structures, processes, and practices. For women, some of the most harmful gender inequalities are enacted within human resources (HRs) practices. This is because HR practices (i.e., policies, decision-making, and their enactment) affect the hiring, training, pay, and promotion of women. We propose a model of gender discrimination in HR that emphasizes the reciprocal nature of gender inequalities within organizations. We suggest that gender discrimination in HR-related decision-making and in the enactment of HR practices stems from gender inequalities in broader organizational structures, processes, and practices. This includes leadership, structure, strategy, culture, organizational climate, as well as HR policies. In addition, organizational decision makers' levels of sexism can affect their likelihood of making gender biased HR-related decisions and/or behaving in a sexist manner while enacting HR practices. Importantly, institutional discrimination in organizational structures, processes, and practices play a pre-eminent role because not only do they affect HR practices, they also provide a socializing context for organizational decision makers' levels of hostile and benevolent sexism. Although we portray gender inequality as a self-reinforcing system that can perpetuate discrimination, important levers for reducing discrimination are identified.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it