Explaining Education-to-Work Transitions: Thinking Backwards, Situating Agency and Comparing Countries
Bibliographic record
Abstract
This paper argues that explanations must start at the end of young people’s education-to-work transitions, with employers’ recruitment behaviour and preferences, which then govern the content of and recruitment to preceding education and training. Young people themselves exercise agency: this propels their careers forward biographically, but necessarily consolidates opportunity structures (variously called routes, pathways or trajectories) that have been pre-built from above. It is also argued that ultimately the transition regime in every country, and sometimes in each region and business sector, needs to be treated as a unique case study. However, these regimes can be divided into recognisable types which are most easily identified by starting in an economy and its labour markets. Finally, it follows that attempts which start in earlier life, prior to young people entering the labour market, to modify links between social origins and occupational destinations will invariably fail. Effective interventions can be envisaged only by starting at the end of young people’s transitions, then thinking backwards.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".