Ethical Risk Management in Co-operative Education Programs
Bibliographic record
Abstract
Work-integrated learning (WIL) practitioners and higher education institutions (HEIs) regularly encounter ethical issues, dilemmas, or conflicts (‘risks’) in delivering WIL programs. Ethical risks that are not properly identified and managed can result in negative financial, legal and reputational consequences for the HEI. A case study of 10 Canadian WIL practitioners reported in this article identifies practices that reduce, transfer, control or eliminate ethical risk in co-operative education, a popular type of WIL program in Canadian HEIs. The findings are presented as a framework of risk management practices involving education and training, institutional support, policies and processes, collaboration with the WIL community, and student communication. A key theme underpinning the ethical risk management practices is the complexity of maintaining productive, quality relationships between three categories of WIL stakeholders- students, employers and the HEI. This study builds on earlier research revealing characteristics of ethical risk in WIL, with the subsequent findings intended to educate WIL stakeholders and assist them with evaluating and improving ethical risk management.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".