ENSURING EQUITABLE ACCESS TO WORK-INTEGRATED LEARNING IN ONTARIO, CANADA
Bibliographic record
Abstract
This research aims to investigate and evaluate the barriers to work-integrated learning (WIL) faced by underrepresented postsecondary students in Ontario, Canada. WIL is in demand by universities to improve employment outcomes and produce “work-ready” students. However, a diversity lens is rarely used when evaluating such programs even though diversity is considered by many employers. Using semi-structured interviews, this two-year study has identified: 1) the barriers and challenges encountered by WIL offices in Ontario universities and 2) employer’s perceptions regarding the WIL program and WIL students. Additionally, we conducted quantitative data analysis to examine differences in students’ access to WIL programs when factors including intersections of gender, visible minority, disability, parents’ educational level, and citizenship status are taken into consideration. We interviewed 25 staff from WIL offices of universities across Ontario, including Executive Directors and Directors of Co-op or Experiential Education. Our analysis produced numerous insights relevant to the current state of diversity and inclusion within the WIL sector in Ontario universities. First, we found the presence of multiple university- and employer-level “sorting mechanisms” that unintentionally, but systematically, excluded students of certain social groups. Second, we our analysis suggests that staff at WIL offices were generally unaware of any kind of inequities/discrimination faced by historically marginalized students in their programs. Finally, the analysis shows that WIL offices across many Ontario universities lacked formal procedures to address diversity and inclusion related complaints raised by WIL participants; instead, the offices relied on informal mechanisms to handle these situations. We also conducted in-depth interviews with employers to better understand their perceptions for WIL programs. Using thematic analysis, we identified five recurring themes: 1) government funding & employer budgeting, 2) recruitment & selection, 3) skills gaps & employer expectations, 4) evaluation criteria, and 5) underrepresented groups. Although Ontario’s postsecondary institutions have started to pay more attention to diversity, equity, and inclusion, WIL offices did not operate the program using a diversity and inclusion lens, neither did employers, who hired WIL students, apply such lens. The recommendations for further research include conducting diversity and inclusion-related studies within this sector, which are to be oriented around the six principal components of the Diversity Assessment Tool (DAT) developed by Diversity Institute. In addition, there is a need to address recruitment and hiring restrictions encountered by underrepresented groups in WIL. We also recommend working on strengthening partnerships between employers and WIL programs in postsecondary institutions to bridge the skills gap and enhance mutual understanding.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".