Linking Educators and Employers: Taxonomies, Rationales, and Barriers
Bibliographic record
Abstract
Abstract Despite their many overlaps and areas of common interest, the domains of education—particularly school-based education—and employment can seem far apart, with different structures, incentives, and experts. Policymakers and commentators have pointed to this distance as exacerbating economic and social issues, ranging from underemployment and unsatisfactory productivity to job satisfaction and social mobility. This chapter draws on examples from Organization for Economic Co-operation and Development (OECD) countries of the diverse mechanisms proposed to narrow this distance in a new taxonomy established across seven aspects of the school-based education process: education policy, curriculum development, institutional management, curriculum delivery, non-curriculum skills development, career guidance, and graduation. The benefits and barriers to partnership working between employers and educators are discussed and linked to the debate regarding the need for state subsidy to support partnership working.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.029 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".