Modeling Completion of Vocational Education: The Role of Cognitive and Noncognitive Skills by Program Type
Bibliographic record
Abstract
Abstract This study provides evidence of the importance of cognitive and noncognitive skills to completion of three types of vocational training (VET): education and health, technical, and business. Math and language exam scores constitute the key measures of cognitive skills; teacher-assigned grades the key measure of noncognitive skills. The data consist of two 9-year panels of youth completing compulsory education in Denmark. Estimation of completion proceeds separately by gender and VET type, controlling for selection and right censoring. The authors find that all skills are inversely related to VET enrollment, even controlling for family-specific effects. Estimates for completion vary considerably by program type, demonstrating the methodological importance of distinguishing among different VET courses. Math scores are positively related to certification for all VET tracks, though with very different magnitudes; language skills are inversely related for the nonbusiness tracks; and noncognitive skills appear important primarily for the business track.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".