Bibliographic record
Abstract
List of Illustrations Acknowledgements Chapter One: Vocational Training: International Perspectives Gerhard Bosch and Jean Charest Chapter Two: Vocational Education and Training in Australia: The Evolution of a Segmented Training System Richard Cooney and Michael Long Chapter Three: Vocational Training in Canada: The Poor Second Cousin in a Well-Educated Family Jean Charest and Ursule Critoph Chapter Four: The Vocational Education and Training System in Denmark: Continuity and Change Susanne Wiborg and Pia Cort Chapter Five: Vocational Training in France: Towards a New 'Vocationalism'? Philippe Mehaut Chapter Six: The Revitalization of the Dual System of Vocational Training in Germany Gerhard Bosch Chapter Seven: The Transformation of the Government-led Vocational Training System in Korea Jin Ho Yoon and Byung-Hee Lee Chapter Eight: The Vocational Training System in Mexico: Characteristics and Actors, Strengths and Weaknesses Arnulfo Arteaga Garcia, Sergio Sierra Romero and Roberto Flores Lima Chapter Nine: Vocational Training in Morocco: Social and Economic Issues for the Labour Market Brahim Boudarbat and Mehdi Lahlou Chapter Ten: Vocational Education and Training in the United Kingdom Helen Rainbird Chapter Eleven: The Vocational Education and Training System in the United States Thomas Bailey and Peter Berg Contributors References Index
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
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".