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Record W2095994417 · doi:10.1027/1866-5888/a000034

Self-Promoting and Modest Job Applicants in Different Cultures

2011· article· en· W2095994417 on OpenAlexaboutno aff
Marianne Schmid Mast, Denise Frauendorfer, Laurence Popovic

Bibliographic record

VenueJournal of Personnel Psychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)PsychologySocial psychologyValue (mathematics)Job interviewPolitical scienceLaw

Abstract

fetched live from OpenAlex

The goal of this study was to investigate the influence of the recruiter’s cultural background on the evaluation of a job applicant’s presentation style (self-promoting or modest) in an interview situation. We expected that recruiters from cultures that value self-promotion (e.g., Canada) will be more inclined to hire self-promoting as compared to modest applicants and that recruiters from cultures that value modesty (e.g., Switzerland) will be less inclined to hire self-promoting applicants than recruiters from cultures that value self-promotion. We therefore investigated 44 native French speaking recruiters from Switzerland and 40 native French speaking recruiters from Canada who judged either a self-promoting or a modest videotaped applicant in terms of hireability. Results confirmed that Canadian recruiters were more inclined to hire self-promoting compared to modest applicants and that Canadian recruiters were more inclined than Swiss recruiters to hire self-promoting applicants. Also, we showed that self-promotion was related to a higher intention to hire because self-promoting applicants are perceived as being competent.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.219

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 communication0.0000.000
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 head0.070
GPT teacher head0.380
Teacher spread0.310 · 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

Citations27
Published2011
Admission routes1
Has abstractyes

Explore more

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