The role of career history in gender based biases in job selection decisions
Bibliographic record
Abstract
Purpose The purpose of the present research is to test the hypothesis that hiring decisions are influenced by the perceived femininity and masculinity of candidates as inferred from their career history. Design/methodology/approach Two job selection simulation studies were conducted in which students with and without personnel selection experience assessed the suitability of male and female job candidates for male and female sex‐typed jobs. The candidate's CVs varied with regard to the gender typicality of the candidate's career history. Findings As predicted, when they previously had occupied another gender atypical job, both men and women were perceived as more suitable for a job that is more typical of the opposite gender. These decisions were mediated fully for women and partially for men by the impact of the gender typicality of the candidate's career on their perceived masculinity or femininity. In addition, men who had a gender atypical career history were perceived as less suitable for gender typical jobs. Thus, for men a gender atypical career history can serve as a “double edged sword.” Importantly, experienced and inexperienced decision makers were equally subject to this effect. Originality/value Career history provides individuating information about a candidate over and above the skills and experiences they are likely to have. Gender type is one such information that is pertinent in a job market that divides jobs into male and female typical and makes hiring decisions on this basis. Previous research has largely ignored this aspect of career history.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".