Group Recruiting Events and Gender Stereotypes in Employee Selection*
Bibliographic record
Abstract
ABSTRACT This paper reports the results of multiple studies that together provide converging evidence in support of the theory that gender stereotypes bias employee selection during group recruiting events. Specifically, we predict and find that female (male) job candidates who exhibit stereotypically male behaviors receive lower (higher) evaluations during group recruiting events, particularly among male recruiters. Prior research suggests gender stereotypes do not bias employee selection during one‐on‐one interviews. However, our results suggest that evaluating job candidates in the more social context of group events can have important unintended consequences on employee selection, a key component of the accounting control environment. Given the importance of group recruiting events to inform hiring decisions across organizations such as investment banks and public accounting firms, our results contribute to a better understanding of survey and field evidence suggesting that entry‐level male and female employees have different personalities at these organizations, which appear to influence their career trajectories.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".