The Diversity Challenge for Higher Education in Canada: The Prospects and Challenges of Increased Access and Student Success
Bibliographic record
Abstract
With the shifting demographics of societies such as the United States and Canada, access to higher education presents a variety of challenges to ensure that universities are able to meet the challenges associated with increased student diversity on campus. The current paper reviews first the literature on the linkages between social inequality and education, before turning to an examination of Canadian data with respect to access issues and the possible barriers to increasing diversity among postsecondary institutions. The evidence reveals that first-generation students and those whose parents did not attend university, Aboriginal peoples, and students with disabilities (among others) continue to be underrepresented in postsecondary education. At the same time, the paper argues that while institutions of higher learning can facilitate improved access, they must commit to developing support services and a more welcoming and inclusive environment in order to ensure student retention and success among an increasingly diverse student population. The paper concludes with a discussion of “best practices” from the perspective of a predominantly undergraduate, liberal arts institution in southern Ontario.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".