Exploring job seeker profiles through latent profile analysis
Bibliographic record
Abstract
Primarily using a variable-centered approach, job search research explores the connections between antecedents, processes, and outcomes. A person-centered approach, however, categorizes individuals based on personal and contextual elements. This study used CSM as a theoretical framework to identify job seeker profiles by exploring configurations of job search self-efficacy, conscientiousness, financial need, social pressure, and job search quality and intensity. We examined how these profiles correspond with sociodemographic variables and job search outcomes such as rumination, interviews, and job offers. In a sample of 300 job seekers, four profiles emerged: casual job search contemplator, financially burdened job seeker, financially secure job seeker, and multifaceted job search strategist. The contemplator profile correlated with the fewest interviews, while the financially burdened job seeker had the most. These findings suggest career counselors need to recognize distinctive job seeker patterns requiring tailored counseling approaches, underscoring the potential of the person-centered approach for further job search research.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".