Job requirements and workers' learning: formal gaps, informal closure, systemic limits
Bibliographic record
Abstract
There is substantial evidence that formal educational attainments increasingly exceed the educational job requirements of the employed labour force in many advanced market economies – a phenomenon variously termed ‘underemployment’, ‘underutilisation’, or ‘overqualification’. Conversely, both experiential learning and workplace case studies suggest that workers continually negotiate such ‘gaps’. This paper summarises results of recent national labour force surveys and workplace case studies in Canada to further assess the relations between workers and their jobs. Underemployment is found to be increasing among all types of employees. Underemployment is found to decline with work experience but persists in virtually all categories of employees – most notably service and industrial working classes and among non‐white immigrant workers. Case studies of teachers, computer programmers, clerical workers, autoworkers and disabled workers demonstrate how underemployed workers as well as others engage in continual learning and try to reshape their jobs. Implications of these findings are identified in terms of the incompatibility of narrow economic market objectives with wider social objectives of democratic education, and of the systemic limits of appeals for still greater formal educational efforts by already highly educated and continually learning labour forces.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".