Education and occupational status in 14 countries: the role of educational institutions and labour market coordination
Bibliographic record
Abstract
This article explores the role of national institutional factors--more specifically, the level of skill transparency of the education system and labour market coordination--in accounting for cross-national differences in the relationship between education and occupational status. Consistent with previous research, our findings suggest that skill transparency is the primary moderator. Countries with a highly transparent educational system (i.e., extensive tracking, strong vocational orientation, limited tertiary enrolment) tend to be characterized by a strong relationship between education and occupational status. These findings hold even after controlling for the level of labour market coordination. Nevertheless, we also find that labour market coordination plays an independent role by dampening the effect of education on occupational status. Taken together, these results suggest two quite different policy implications: (1) strengthening the skill transparency of the education system by increasing secondary and tertiary-level differentiation may strengthen the relationship between education and occupation, regardless of the level of coordination, and (2) increasing labour market coordination could lead to improved social inclusion and a reduction in inequalities related to educational attainment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".