Falling over a glass cliff: A study of the recruitment of women to leadership roles in troubled enterprises
Bibliographic record
Abstract
Abstract Are women breaking through the glass ceiling only to arrive at a glass cliff—that is, being preferentially appointed to leadership roles where the chances of failure are higher? This study investigates the concept of the glass cliff, both by seeking evidence for its existence and by examining its implications. Focusing specifically on the impact of the recruiter's gender, the researchers asked professionals from a range of backgrounds to evaluate candidates for a post in a hypothetical company that was portrayed either as a success or as in decline. Taken as a whole, the results support the existence of a glass cliff. When the results from male and female recruiters were analyzed separately, a different picture emerged, however. Male recruiters showed no gender preference in the failing company context but favored the male candidate for the low‐risk position. In contrast, female recruiters consistently favored a female candidate, with this preference being more marked for a high‐risk role. The study concluded by looking into the possible motivations for these biases and examining their implications in informing recruitment and career decisions. © 2012 Wiley Periodicals, Inc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".