Maximizing Opportunity, Minimizing Risk: Aligning Law, Policy and Practice to Strengthen Work-Integrated Learning in Ontario
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,026 | 0,013 |
| Communication savante | 0,011 | 0,004 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».