Skills System International Case Studies
Bibliographic record
Abstract
This paper presents five country case studies - Canada, France, Japan, the Netherlands and Sweden - to identify the key features and challenges of different international skills systems. Information is also provided on the German skills system (Annex 4) following a study visit. With the exception of Japan, each of the countries profiled has higher levels of productivity than the UK. The report has been designed to identify successful aspects of the different systems and provide policy learning relevant to the United Kingdom. The skills systems profiled differ from the UK in a number of key aspects. Firstly, they have experienced less flux in their skills system. Secondly, almost all have stronger employment legislation than the UK. Thirdly, many of the countries’ skills systems involve a greater role for social partners (employer representative and employee representative organisations) than exists in the UK. Key strengths of the skills systems profiled relate to flexibility of provision, the role of social partners and business engagement with training, and the value placed on Vocational Education and Training (VET) within society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".