New and Emerging issues in vocational education and training research beyond 2010
Bibliographic record
Abstract
This paper is a cooperation between researchers from three areas of vocational education and training (VET) and lifelong learning research. It aims to identify several new and emerging issues that will be crucially relevant to VET research in the post-Lisbon decade. The paper begins by exploring the main drivers currently influencing European VET systems and draws up four scenarios for 2010-20, each of which may be a plausible outcome for future European governance, and by extension, VET governance. The contributors then explore the nature of uncertainty in the demand and supply sides of European and local labour markets, suggesting that research into the impact of these uncertainties will need to examine how individuals adapt to new situations, as well as the impact on the nature and structure of VET supply. The paper also attempts a forward looking analysis of innovative teaching and learning in VET - developing twin themes of supporting expert learners and a scholarship of teaching and learning in VET. There will clearly remain important and urgent items of unfinished business in European and national VET policy and implementation in 2010. This paper argues strongly that these are still important for Europe's future economic, social and environmental ambitions, that the achievement of currently agreed priorities will remain a vital political issue, and that research should have an important role in overcoming identified barriers and achieving success in these respects. The paper identifies several issues that remain underresearched. These include meeting the learning needs of older workers and diverse migrant communities. Finally, the paper identifies five new issues that VET research should concentrate on. Since so much policy and research attention at European level focused on the period from 2000 to 2010, this paper intends to make a contribution to opening the debate on European VET research priorities in the decade that will follow.
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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 | 0.062 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".