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When Being Helped is Unhelpful: Gender, Sexism, and Autonomy-Oriented Help

2018· article· en· W2860246822 on OpenAlexaff
Christianne T. Varty, Ivona Hideg, Lance Ferris

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAutonomyCompetence (human resources)PsychologySocial psychologyPromotion (chess)InequalityGender inequalityPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.307
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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