The Vulnerable Worker? A Labor Law Challenge for WIL and Work Experience
Bibliographic record
Abstract
The Fair Work Act (2009) in Australia deregulates "work" in work-integrated learning (WIL) by distinguishing "vocational placement" from "employee". Following concerns about the legal position of WIL and work experience, the Fair Work Ombudsman (FWO) published a fact sheet and commenced a joint research project into unpaid work practices. Nevertheless, the student remains vulnerable to exploitation. This article examines, through the lenses of flexibility and worker protection, the labor regulation of WIL and work experience in Australia and the United States. In particular, the author argues that deregulation in Australia and the legal uncertainty surrounding work experience is inconsistent with the protective function of labor law. Drawing on this examination as well as Australian migration law, the author recommends that the Fair Work Act (2009) be amended to strengthen the criteria for "vocational placement" and to provide a definition of "work experience" in the interests of a balanced regulatory framework. (Asia-Pacific Journal of Cooperative Education, 2013, 14(3), 135-146) Keywords: Work-integrated learning, Work experience, Labor regulation, Fair Work Act, Fair Labor Standards Act
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.029 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.018 | 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".