The Occupational Context of Mismatch: Measuring the Link Between Occupations and Areas of Study to Better Understand Job-Worker Match
Bibliographic record
Abstract
Scholars have long been interested in the prevalence, causes, and consequences of workers being well matched or poorly matched to their jobs. When studying match along dimensions of education or skill, mismatch has been defined as a deficit or surplus in the level of skill or education, without necessary regard for the relevance of either. More recently researchers have moved beyond this deficit/surplus approach to studying mismatch to ask if workers’ skills or education are relevant to their jobs. In this article we argue that the next step for workers studying job-worker match is to consider the relevance of relevance. We argue in studying the relevance of workers’ education it is necessary to consider and measure the extent to which occupations draw broadly from across educational specialties or hire primarily from pools of workers trained in specific areas. The causes and consequences of not having relevant education will be different in occupations that are closely tied to particular fields of study than in those not linked to any field of study. To facilitate this research agenda we develop seven measures of the link between occupations and fields of study in the Canadian labour market. We test the validity and robustness of these measures. We discuss when each measure is most appropriate and provide an appendix listing values for the three best-performing measures, calculated for Statistics Canada 4-digit occupational codes.
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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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".