Sex bias in the selection interview: Does evaluation structure make a difference?
Bibliographic record
Abstract
Women make up a large segment of the workforce but continue to be underrepresented in senior leadership positions. Research demonstrates that sex bias can emerge in the hiring process. In order to reduce the likelihood of sex bias in evaluation, selection procedures should reduce the potential for individual decision maker bias to contaminate the process. One method of introducing control and objectivity into the hiring process is through the structured interview. This study investigated one aspect of structure, interview evaluation, and its effect on the perception of job candidates and assessments of their suitability. It was hypothesized that increased structure in candidate evaluation would increase the extent to which job-based rather than sex-based schemas are activated and lead to equivalent assessments of the suitability of a man and woman candidate for the position of Dean of a business school. The results suggest rating structure had no effect on reducing sex bias; the two candidates were perceived as equivalent in terms of behaviors and characteristics, and were rated as equally suitable on a measure of Overall Suitability. The woman candidate was rated as less suitable on a measure of Interpersonal Suitability, and the interpersonal criteria were found to be more important in considering the woman candidate. The results suggest that a job-based schema was activated for both candidates, but that differential expectations were applied to the man and woman candidates with respect to the importance of the evaluation criteria. The failure to recognize female-typed interpersonal behaviors in the case of both candidates combined with their perceived importance for those rating the female candidate may have contributed to differential ratings of Interpersonal Suitability. Implications and limitations of the study are discussed.
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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 | 0.131 | 0.282 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".