The long road to employment: Incivility experienced by jobseekers.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".