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Record W1669522989 · doi:10.1002/job.1874

Acting professional: An exploration of culturally bounded norms against nonwork role referencing

2013· article· en· W1669522989 on OpenAlexaff
Eric Luis Uhlmann, Emily Heaphy, Susan J. Ashford, Luke Zhu, Jeffrey Sanchez‐Burks

Bibliographic record

VenueJournal of Organizational Behavior · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNegotiationNorm (philosophy)Social psychologyPsychologyFunction (biology)SociologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Summary This article presents three studies examining how cross‐cultural variation in assumptions about the appropriateness of referencing nonwork roles while in work settings creates consequential impressions that affect professional outcomes. Study 1 reveals a perceived norm limiting the referencing of nonwork roles at work and provides evidence that it is a U.S. norm by showing that awareness of it varies as a function of tenure living in the United States. Studies 2 and 3 examine the implications of the norm for evaluations of job candidates. Study 2 finds that U.S. but not Indian participants negatively evaluate job candidates who endorse nonwork role referencing as a strategy to create rapport and shows that this cultural difference is largest among participants most familiar with norms of professionalism, those with prior recruiting experience. Study 3 finds that corporate job recruiters from the United States negatively evaluate candidates who endorse nonwork role referencing as a means of building rapport with a potential business partner. This research underlines the importance of navigating initial interactions in culturally appropriate ways to facilitate the development of longer‐term collaborations and negotiation success. Copyright © 2013 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)0.000
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 score0.174
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.352
Teacher spread0.299 · 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

Citations53
Published2013
Admission routes1
Has abstractyes

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