Skills Development and Training in SMEs
Bibliographic record
Abstract
The report discusses the results of the OECD "Leveraging Training and Skills Development in SMEs" (TSME) project which examines access to training by SMEs across seven regions in six OECD countries: New Zealand, Poland, Belgium, UK, Turkey and Canada. The book analyses the policy issues related to both low access by SMEs, and how to recognise the increasing importance of informal training and skills development methods. The book looks at how both formal and alternative ways of training and skills development interact and identifies impacts at three levels; for the firm and employees; for the industry; and for the local area where the firm is located. The report pays special attention to the development of entrepreneurial skills and the emerging area of "green skills". This focus is not just because ‘green skills’ represent the next new training opportunity – the de-carbonisation of economies that will occur over the coming decades represents an industrial transformation on the scale of the microelectronics revolution - but in many ways the response to the green economy is at an emerging stage- this means we have the opportunity to implement lessons from previous successful practices into a skill development area that will have enormous reach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".