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Record W2007490783 · doi:10.1177/1948550610379921

Talking Shop and Shooting the Breeze

2010· article· en· W2007490783 on OpenAlexaff
Shannon E. Holleran, Jessica Whitehead, Toni Schmader, Matthias R. Mehl

Bibliographic record

VenueSocial Psychological and Personality Science · 2010
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDisengagement theoryPsychologySocial psychologyIdentity (music)Face (sociological concept)Point (geometry)Developmental psychologyGerontologyMedicineSociology

Abstract

fetched live from OpenAlex

Past research has examined women’s subjective satisfaction in science, technology, engineering, and math (STEM), but the actual events that correlate with disengagement have not been identified. In this study, workplace conversations of 45 female and male STEM faculty were sampled using the Electronically Activated Recorder, a naturalistic observation method, coded for research or socializing content, and correlated with self-reported job disengagement. Both men and women were less likely to discuss research in conversations with female as compared to male colleagues, and when discussing research with men, women were rated as less competent than men. Consistent with the idea that women in STEM experience social identity threat, discussing research with male colleagues was associated with greater disengagement for women, whereas socializing with male colleagues was associated with less disengagement. These patterns did not hold for men. These findings point to the unique challenges women face in STEM disciplines.

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)0.004
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.044
GPT teacher head0.382
Teacher spread0.338 · 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

Citations94
Published2010
Admission routes1
Has abstractyes

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