The Glass Cliff: Is It Only About Exceptionally Talented Women?
Bibliographic record
Abstract
The glass cliff phenomenon, which suggests women are more likely to be promoted to leadership positions during crises, has garnered considerable academic interest, though the empirical support is mixed. Surveying the literature highlights critical missing elements needed to explain the inconsistency in the findings. Limited attention has been given to the role of candidates’ characteristics, which leads to the question: which women are promoted into leadership positions during crises? In this paper, we examine the critical roles of candidates’ talent and leadership levels, given their impact in addressing crises. We argue that top-talent women are overlooked during prosperous times but are turned to during crises, given their potential impact on firm performance, and this effect is strongest at the senior management level. We test these arguments using random-effect logistic regressions on a dataset of nearly 60,000 employees in the Canadian oil and gas industry. We find the glass cliff effect is prominent among top-talent women (but not non-top talent) of all leadership levels as evidenced in their (relative to male counterparts) increased promotion likelihood during downturns as compared to upturns. The findings show that women are promoted to leadership positions more during crises, but only if they are exceptional.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".