Profiling the “big fish in a small pond” and examining which one swims the most happily
Bibliographic record
Abstract
Purpose To disentangle the impact of each type of overqualification, the author created four profiles of overqualified workers based on the metaphor of the big fish in a small pond: “the fish that fits the pond,” “the unaware big fish in a small pond,” “the fish fitting the pond, but feeling cramped” and “the aware big fish in a small pond.” Design/methodology/approach Using a Canadian representative survey, the author examined the distinctive effect of objective and subjective overqualification on job satisfaction among recent graduate workers. The subjective measure is based on the individual's perception of the match of his/her education level, training and experience with the requirements of his/her job; and the objective measure assesses the match between the individual's educational attainment and the skill level associated with his/her occupational group. Findings The results show that only the “the fish fitting the pond, but feeling cramped” and “the aware big fish in a small pond” profiles of overqualified workers lead to a lower probability of being satisfied with their job compared to “fish that fits the pond.” Originality/value The current study is original because the findings reveal that being objectively overqualified without feeling cramped has no consequence on workers' job satisfaction, while feeling cramped without being objectively overqualified leads to lower job satisfaction. Recruiters should therefore avoid to focus on overeducation since it has no impact on their job satisfaction. They should pay more attention to the feeling of being cramped when they look for the best candidates. Even if the candidate's diploma corresponds to that required by the position, this feeling reduces their chances to be satisfied with the job.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".