Lean In or Don’t Lean Out? Opt-Out Framing Attenuates Gender Differences in the Decision to Compete
Bibliographic record
Abstract
Research has documented a persistent gender gap in the upper echelons of organizations, which is in part attributed to gender differences in the propensity to compete and apply for promotions. In this dissertation, I argue that, rather than being the product of “dispositional” differences between men and women, this gender gap is due to the organizational contexts or organizational designs in which competitions take place. Most competitive selection processes (e.g., promotions) require self-nomination via an application (opt-in frame), which could disadvantage women. I propose that utilizing an opt-out frame for competitive selection processes will attenuate gender differences in potential candidates’ likelihood of applying. To test the theoretical underpinnings and practical implications of employing opt-out interventions to mitigate gender inequality, I develop a comprehensive set of laboratory and field-based experiments. The findings of these experiments provide compelling evidence that this gender difference can be eliminated by changing the default framing of a competitive task from one wherein applicants must actively choose to compete to one wherein applicants are automatically enrolled in competition, but can choose to opt out. Changing the default framing affects perceptions of prevailing social norms relating to gender and competition, as well as perceptions about the performance or ability threshold that is required in order to apply. This research contributes to theory by suggesting that gender differences in competition are not absolute, and by building on the conceptualization of inequality as a macro, system-level problem. On a practical level, the findings of this research have relevance for organizations aiming to close the gender gap in career advancement.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".