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Record W1879388270 · doi:10.1002/job.1917

Applying models of employee identity management across cultures: Christianity in the USA and South Korea

2014· article· en· W1879388270 on OpenAlexaff
Brent J. Lyons, Jennifer Wessel, Sonia Ghumman, Ann Marie Ryan, Sooyeol Kim

Bibliographic record

VenueJournal of Organizational Behavior · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCentralityDistancingReligious identityIdentity (music)Social psychologySocial distancePsychologySociologyPublic relationsPolitical scienceCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Summary Identity management refers to the decisions individuals make about how they present their social identities to others. We examined cross‐cultural differences in distancing and affirming identity management strategies of Christian‐identified employees utilizing samples from the USA and South Korea. Religious centrality, risks of disclosure, pressure to assimilate to organizational norms, and nation were key antecedents of chosen identity management strategies. Risks of disclosure and pressure to assimilate related to more distancing and less affirming strategies when religious centrality was low, but nation served as a boundary condition for the moderating effects of religious centrality. Distancing strategies related to negative outcomes regardless of religious centrality, but affirming strategies only related to positive outcomes when religious centrality was low. We discuss how this work contributes to theoretical and practical understanding of identity management in the workplace and across cultures. Copyright © 2014 John Wiley & Sons, Ltd.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.275
Teacher spread0.254 · 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 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

Citations47
Published2014
Admission routes1
Has abstractyes

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