Bibliographic record
Abstract
Purpose The two main purposes of the paper are: first, to provide an empirical test of the widely‐held view among employers that overqualified workers are less committed as evidenced by heightened levels of job search, and second, to evaluate the three explanations of overqualification (matching theory, the theory of differential overqualification, and the career mobility hypothesis) in which job search plays a central role. Design/methodology/approach Maximum likelihood probit estimation is conducted on a sample of employed Canadians aged 18 and over who were surveyed in 2000. Predictors of job search are derived from the economic assumption that the employee's decision to undertake job search depends on a cost‐benefit assessment. Findings The empirical results indicate that overqualified workers are more active job searchers, and lend support to the matching theory view that overqualification is sub‐optimal from the worker's perspective. Originality/value This paper adds to the small number of European studies exploring the connection between overqualification and job search. The impacts of overqualification are especially important for Canadian employers given the high incidence of overqualification of the Canadian work force.
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 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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".