The future of Australian vocational education qualifications depends on a new social settlement
Bibliographic record
Abstract
This article argues that the current social settlement underpinning vocational education and training (VET) in Australia is fractured. The current settlement is low trust and consists of qualifications based on competency-based training models of curriculum and competitive markets. The result is narrow qualifications that do not prepare people for jobs associated with the qualifications, and the decimation of technical and further education (TAFE) institutes which are the public providers of VET. The article develops a conceptual framework by integrating various literatures that are broadly consistent with institutionalist theories, including the Varieties of Capitalism literature, Raffe’s and colleagues model of intrinsic and institutional logics, and literatures on skills ecosystems and educational and labour market transitions. This analysis shows why VET has such a low status in Anglophone liberal market economies. A new social settlement is needed that recognises the diverse purposes played by VET qualifications, underpinned by a differentiated model of VET qualifications that does not tie the outcomes of learning so tightly to particular occupations. Such a model would recognise that some qualifications will have tighter links to occupations than others.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".