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Record W2465230345 · doi:10.1037/apl0000055

The long road to employment: Incivility experienced by jobseekers.

2015· article· en· W2465230345 on OpenAlexaff
Abdifatah A. Ali, Ann Marie Ryan, Brent J. Lyons, Mark G. Ehrhart, Jennifer Wessel

Bibliographic record

VenueJournal of Applied Psychology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsSimon Fraser University
FundersNational Science Foundation Graduate Research Fellowship Program
KeywordsIncivilityPsychologySocial psychologySeekersJob performanceAttributionJob designContext (archaeology)Job attitudeJob analysisSocial cognitive theoryJob satisfactionApplied psychology

Abstract

fetched live from OpenAlex

This study addresses how job seekers' experiences of rude and discourteous treatment--incivility--can adversely affect self-regulatory processes underlying job searching. Using the social-cognitive model (Zimmerman, 2000), we integrate social-cognitive theory with the goal orientation literature to examine how job search self-efficacy mediates the relationship between incivility and job search behaviors and how individual differences in learning goal orientation and avoid-performance goal orientation moderate that process. We conducted 3 studies with diverse methods and samples. Study 1 employed a mixed-method design to understand the nature of incivility within the job search context and highlight the role of attributions in linking incivility to subsequent job search motivation and behavior. We tested our hypotheses in Study 2 and 3 employing time-lagged research designs with unemployed job seekers and new labor market entrants. Across both Study 2 and 3 we found evidence that the negative effect of incivility on job search self-efficacy and subsequent job search behaviors are stronger for individuals low, rather than high, in avoid-performance goal orientation. Theoretical implications of our findings and practical recommendations for how to address the influence of incivility on job seeking are discussed.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.463
Teacher spread0.378 · 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 designNot applicable
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

Citations42
Published2015
Admission routes1
Has abstractyes

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