Small firms and the failure of skills policy: adopting an institutional perspective
Bibliographic record
Abstract
Both skills and small firms have been increasingly prominent in policy agendas across the world in recent years. Skills are now seen as being crucial to economic prosperity, yet evidence consistently shows much lower levels of training, on average, in small firms than in larger businesses. Policy makers in various countries have sought to address this perceived problem and to stimulate skills development in small firms, but have attempted to do so in different ways and with varying degrees of success. It is this divergence in national skills policies, as well as its causes and implications for skill formation in small firms, that this paper seeks to illuminate. In doing so, it adopts an ‘institutional’ perspective that advances current understanding of how and why skills policies adopted in different countries appear to have varying effects on small firms. Through employing this institutional analysis, the paper promotes an awareness of how historical, social and economic forces in the ‘corporatist’ systems, found for example in Germany and Scandinavia, tend to provide a more supportive context for skills development in small firms than the liberal free market systems found elsewhere in the world, such as in the USA, Canada and the UK – which is highlighted as an illustrative case in this paper.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".