Strategies of job seekers related to age-related stereotypes
Bibliographic record
Abstract
Purpose – Given the increasing diversity in the age of job seekers worldwide and evidence of perceptions of discrimination and stereotypes of job seekers at both ends of the age continuum, the purpose of this paper is to identify how perceptions of age-related bias are connected to age-related identity management strategies of unemployed job seekers. Design/methodology/approach – Data were collected from 129 unemployed job-seeking adults who were participants in a career placement service. Participants completed paper-and-pencil surveys about their experiences of age-related bias and engagement in age-related identity management strategies during their job searches. Findings – Older job seekers reported greater perceptions of age-related bias in employment settings, and perceptions of bias related to engaging in attempts to counteract stereotypes, mislead or miscue about one's age, and avoid age-related discussions in job searching. Individuals who were less anxious about their job search were less likely to mislead about age or avoid the topic of age, whereas individuals with higher job-search self-efficacy were more likely to acknowledge their age during their job search. Older job seekers higher in emotion control were more likely to acknowledge their age. Originality/value – Little is known about how job seekers attempt to compensate for or avoid age-related bias. The study provides evidence that younger and older job seekers engage in age-related identity management and that job search competencies relate to engagement in particular strategies.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".