The Student as Inadvertent Employee in Work-Integrated Learning: A Risk Assessment by University Lawyers
Bibliographic record
Abstract
An employment contract between the student and the host organization may be the unintended consequence of a work-integrated learning (WIL) placement. The student, as an ‘inadvertent employee’ of the host organization, can expose the university to risk. A case study involving thirteen Australian university lawyers identifies the legal and reputational risks associated with paid and unpaid WIL placements, and how university lawyers manage these risks through WIL agreements, legal advice and the support of external agencies. In Australia, the key themes which emerge from university lawyer experiences, and the existing literature, are the vocational placement exemption under the Fair Work Act 2009 (Cth), and scholarship payments. The article concludes with a series of lessons for WIL practitioners in terms of managing the labor-related risks of WIL programs.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".