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Reminders of Past Injustices Against Women Undermine Support for Workplace Policies Promoting Women

2018· article· en· W2867490335 on OpenAlexaffabout
Ivona Hideg, Anne Bigane Wilson

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGender equalityPolitical scienceGender studiesPsychologySociology

Abstract

fetched live from OpenAlex

Understanding past injustices endured by traditionally disadvantaged groups in our society (i.e., women, racial minorities) is crucial for understanding inequality today and, moreover, such injustices are commonly used as a rationale for contemporary equality policies. In this paper, we draw on the literatures on social identity, history and identity, and employment equity, to suggest that reminding people about social injustice and discrimination against a disadvantaged group (e.g., women) can invoke social identity threat among advantaged group members (e.g., men) and consequently undermine support for contemporary employment equity (EE) policies by fostering the belief that inequality no longer exists. We find support for our hypotheses in three studies in the context of Canadian (Study 1 and 3) and American (Study 2) EE policies. In a sample of student job applicants (Study 1) and employees (Study 2), we found that past reminders of injustice toward women undermine men’s support for a gender-based EE policy due to denial of current gender discrimination, whereas they do not undermine women’s support for EE policies. In Study 3 we found that providing additional evidence about the advancement of women’s right mitigates men’s negative reactions to EE policy by deflecting threat to their social identity.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.490

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.341
Teacher spread0.303 · 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 designQualitative
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

Citations1
Published2018
Admission routes2
Has abstractyes

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