When Being Helped is Unhelpful: Gender, Sexism, and Autonomy-Oriented Help
Bibliographic record
Abstract
Drawing on the literatures on workplace helping, types of help, and benevolent sexism, we argue that women who receive autonomy-oriented help, i.e., help which involves providing the recipient with the tools to solve a problem independently in the future, incur negative workplace evaluations from benevolently sexist evaluators. In particular, we argue autonomy- oriented help threatens traditional gender roles by providing women with the ability to independently solve similar problems in the future. Using an experimental study where participants evaluated a female applicant for promotion to an executive-level position, we found women who received autonomy-oriented help (vs. dependency-oriented help, which involves solving the recipient’s problem in a manner which keeps the recipient dependent on the provider for further help) were perceived as less competent and received lower hireability and reward recommendations from evaluators high (vs. low) in benevolent sexism. Further, perceived competence mediated the moderating effect of benevolent sexism on the relation between autonomy-oriented help (vs. the control condition) and hireability and reward recommendations. Our results suggest being helped is not necessarily helpful and, in conjunction with benevolent sexist attitudes, may contribute to workplace gender inequality. Contributions to theory and practice are discussed.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".