Maximizing Opportunity, Minimizing Risk: Aligning Law, Policy and Practice to Strengthen Work-Integrated Learning in Ontario
Bibliographic record
Abstract
A broad consensus is emerging in Ontario and at the federal level in favour of expanding postsecondary students’ access to experiential or “work-integrated learning” (WIL) opportunities. One of the challenges in implementing this vision is navigating the complex legal status of students as they leave campus and enter workplaces in a wide range of industries and roles. This study aims to support these efforts by mapping the current legal landscape for WIL to identify both risks and opportunities for students, post-secondary institutions (PSIs) and placement hosts alike (referred to collectively in this study as “WIL participants”). It makes recommendations to streamline, clarify and strengthen key legal frameworks and improve institutional practices in managing WIL programs and their legal implications.\nWIL includes “a variety of applied and work-based experiences through which students are able both to contextualize their learning and gain relevant work experience” (PhillipsKPA, 2014), including co-op, internships and applied research projects. This study focuses on the law with respect to off-campus placements completed as part of a university or college program, as distinct from broader questions about the regulation of internships or training positions in the labour market as a whole.\nThe potential benefits of WIL are often framed in terms of human capital development. WIL is identified as a means of building workforce capabilities, as well as the skills and individual prospects of students as members of the labour force (Australian Collaborative Education Network [ACEN], 2015). However, not all those who have studied WIL are equally convinced of its benefits, at least as it is currently delivered. The human capital perspective stands in contrast with a more critical stream of analysis that associates WIL with the rise of precarious employment. A further concern is that WIL opportunities are distributed unequally among students in ways that reflect and reinforce larger labour market inequities. This report keeps both perspectives in mind and analyzes the legal frameworks surrounding WIL in Ontario to identify ways of ameliorating these concerns and promoting WIL programs that deliver real benefits.\nThe study examines two primary research questions: (1) How are legal issues currently impacting WIL programs in Ontario? (2) What steps could be taken to help legal frameworks and processes align more closely with the goal of expanding the availability of quality WIL programs and opportunities?\nWe addressed these questions through a combination of in-depth qualitative interviews with WIL experts in both legal and non-legal roles and a review of relevant provincial and federal legislation and regulations, as well as legal cases dating back to 1990. We also reviewed secondary literature on WIL in Canada and in the United States, the United Kingdom and Australia. As well, the report analyzes Canadian tax expenditures designed to support WIL to assess the size and scope of tax-delivered investments in these 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".