Translating Practice into Research: How We Have Come To Define and Structure "Vocational" Education.
Bibliographic record
Abstract
Translating practice into research: how we have come to define and structure ‘vocational’ education Gavin Moodie Victoria University of Technology ‘Technical and further education’ has a well-understood meaning in Australia, and ‘vocational education and training’ is usually defined as TAFE plus private providers. But what is the rationale for considering some courses ‘technical’ or ‘vocational’ and others higher education? This paper reports an investigation of what we mean by ‘technical’ education, how TAFE came to have its common Australian meaning and how this has structured post-compulsory education. This is compared with the meaning and structure of vocational/technical education in two other federations, the US and Canada. The paper argues, first, that there is no logical definition of technical or vocational education in Australia: TAFE and vet came to be defined incrementally over several years as ‘not elsewhere included’, or the sector of formal education left over after the other sectors of formal education – school and higher education – had been defined and established. The second argument is that US and Canada have quite different understandings of vocational/technical education, that these arose quite differently to the way the sector was defined in Australia, and that therefore vocational/technical education is structured quite differently in the US and Canada. Or at least the US States and Canadian provinces chosen for close study: one of the interesting incidental findings is that for all the differences we notice between jurisdictions within Australia, these are minor in comparison with the differences between jurisdictions in the US and Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.423 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.019 | 0.225 |
| Scholarly communication | 0.051 | 0.065 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".