Two institutional responses to work-integrated learning in a time of COVID-19: Canada and Australia
Bibliographic record
Abstract
As the world reacts to the impact of COVID-19, work-integrated learning (WIL) programs globally are similarly affected. Across Canada and Australia, thousands of WIL students either shifted to working remotely or dismissed from their WIL experience. This disruption impacted student learning, program delivery, risk management, staff capability, and industry engagement, and posed significant challenges for institutions. This paper presents the responses to COVID-19 by the University of Waterloo, Canada, and RMIT University, Australia, each guided by quality WIL principles and different WIL organizational structures. This paper outlines how each institution: mobilized staff, introduced program changes while maintaining quality, engaged industry partners and presented WIL program-based solutions to COVID-19 challenges. The paper concludes with discussion on challenges and opportunities that events such as COVID-19 has upon WIL programs, implications for other institutions and student outcomes. Consideration is given to post-COVID scenarios, and how WIL might need to be re-imagined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| 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".