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Record W2069941037 · doi:10.1177/0894845313486354

The Experience of Emotions During the Job Search and Choice Process Among Novice Job Seekers

2013· article· en· W2069941037 on OpenAlexaff
Silvia Bonaccio, Natalie Gauvin, Charlie L. Reeve

Bibliographic record

VenueJournal of Career Development · 2013
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSeekersPsychologyJob attitudeJob analysisSocial psychologyJob performanceProcess (computing)Job designJob satisfactionApplied psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The authors investigate the role of emotions in the job search and choice process of novice job seekers. Results of qualitative analyses of the first-person accounts of 41 job seekers indicate that participants whose recollections of their job search contained emotional language were more likely to display a haphazard job search strategy than those whose recollections did not. They were also more likely to engage in choice strategies that were not driven by concrete criteria. In comparison, participants whose recollections were not emotion-laden reported more criteria-driven choice strategies, and did not display the tendency to revise or lower their standards or to settle for a less desirable job than they had been seeking. Implications of these findings are discussed in terms of the role of emotions in job search and choice research as well as in terms of job search counseling for novice job seekers.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.039
GPT teacher head0.329
Teacher spread0.291 · 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 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

Citations32
Published2013
Admission routes1
Has abstractyes

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