Internships and the PhD: Is This the Future Direction of Work-Integrated Learning in Australia?.
Bibliographic record
Abstract
In the ten years since Australia's first large-scale scoping study of Work-Integrated Learning (WIL) there has been a rapid increase in WIL research and undergraduate WIL opportunities. Though well-established in undergraduate degrees, WIL in postgraduate research degrees is relatively unexplored. Less than half of PhD graduates in Australia are employed by the higher education sector, therefore transferable skills and industry experience are increasingly important. The last few years have seen several Australian peak bodies call for further investment in the employability of PhD graduates. The Australian Government recently provided funding aimed at encouraging doctoral students to undertake internships and placements. Drawing on seven qualitative interviews with past and present PhD students at Griffith University, this exploratory paper explores how PhD students view the potential role of WIL in higher degree research programs in Australia and the challenges they see as facing the broader implementation of WIL across PhD programs. This has broader implications for how WIL may be utilized to equip doctoral graduates with the industry experience and training to improve their employability outside the higher education sector.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".