Leaning In or Not Leaning Out? Opt-Out Choice Framing Attenuates Gender Differences in the Decision to Compete
Bibliographic record
Abstract
In most organizations, promotions often require self-nomination and competition among applicants. However, research on gender differences in preferences for competition suggests that this process might result in fewer women choosing to participate. We study whether changing promotion schemes from a default where applicants must opt in (i.e., self-nominate) to a default where applicants must opt out (i.e., they are automatically considered for promotion, but can choose not to be considered) attenuates gender differences. In our first experiment, although women are less likely than men to choose competitive environments under the traditional opt-in framing, in the opt-out system both women and men have the same participation rate as men in the opt-in system. The increase in participation of women into competition is not associated with negative consequences on performance or well-being. In our second experiment, we show that opt-out framing does not entail penalties from evaluators making decisions about whom to hire. These results support the promise of choice architecture to reduce disparities in organizations. More generally, our findings suggest that gender differences in attitudes toward completion may be context-dependent.
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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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".