The changing professional development needs of the international work-integrated learning community
Bibliographic record
Abstract
The practice of work-integrated learning (WIL) continues to expand across the higher education sector, with many universities introducing or expanding their WIL offerings to align curriculum more closely to employability outcomes (Rowe & Zegwaard, 2017). Universities in Australia have rapidly developed WIL, with all universities offering WIL in almost all the disciplines (Universities Australia, 2019). In New Zealand, WIL has been given increasing attention with the Universities NZ, the peak body for NZ universities, DVCA’s Committee creating a WIL sub-committee to develop national strategy, and with the University of Waikato introducing compulsory WIL for all undergraduates degrees (Muller et al., 2021). The Canadian government recognised WIL as crucial to economic advancement and provided CAD$150 million to support work placement opportunities (Beaulne-Stuebing, 2019).
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".