We are Not all the Same: Job Seeker Profiles Based on Agentic and Contextual Features
Bibliographic record
Abstract
Studies on job search typically use a variable-centered approach, which assumes job seekers come from one homogeneous population in terms of their job search activities, antecedents, and outcomes. Complementing this research approach, the current study used a person-centered approach to identify subgroups of job seekers with distinct profiles varying in attitudinal, individual, behavioral, and contextual factors (i.e., job search quality, job search intensity, conscientiousness, job search self-efficacy, social pressure, job search volition, and financial need). Further, we investigated whether these profiles help predict differences in job search outcomes. Utilizing latent profile analysis, we identified four qualitatively distinct profiles among employed and unemployed job seekers: the job search laggard, the financially burdened job seeker, the financially secured job seeker, and the job search champion. As predicted, these profiles differed in their relationships with job search outcomes (i.e., rumination, number of job interviews, and job offers). The results suggest that the person-centered approach is a useful, complementary method for identifying and analyzing subgroups of job seekers within larger populations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".