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Record W4245922545 · doi:10.24908/iqurcp.8907

Relationship between Gender Stereotypes and Anxiety and the Effects on Negotiation Performance

2018· article· en· W4245922545 on OpenAlexvenueno aff
Catherine Oppedisano

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationStereotype threatAssertivenessStereotype (UML)PsychologyConstructiveSocial psychologyAnxietyPersonalityPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Women with comparable education and experience typically negotiate significantly lower starting salaries and subsequent raises than their male counterparts. The purpose of this study is to further investigate gender differences in negotiation performance by examining the link between stereotype threat and anxiety. Stereotype threat occurs in situations that invoke stereotype-based expectations for poor performance; because men typically enjoy a positive stereotype advantage in negotiation settings (where effective negotiators are believed to be assertive, rational, constructive, effective and decisive), they typically outperform women. Recent work by Kray, Galinsky & Thomas (2001) observed in mix-gender dyads that redefining female stereotypes as positive or enhancing to negotiating, women actually outperformed their male counterparts. To further understand the underlying mechanisms behind these findings, the current ongoing study compares levels of anxiety and negotiation performance under three experimental conditions that differ in terms of engendering stereotype threat.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.244
GPT teacher head0.389
Teacher spread0.146 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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