Excellence and Access for Ontario’s Higher Education System: How Universities Commit to Both
Bibliographic record
Abstract
This thesis describes two phases that together investigate how the 20 Ontario universities commit to the values of widening participation and academic excellence, which are often viewed as competing. This research employs theories and concepts at the intersection of sociology and politics of education, namely, maximally maintained inequality, effectively maintained inequality, isomorphism, and legitimacy to compare Ontario to international trends of social stratification and inequality within the higher education sector. Phase 1 provides a landscape analysis of Ontario’s higher education system and a classification of Ontario’s universities as recruiting or selecting. The universities were classified using three sources of data. The first was the entering grade-point averages across universities and programs from Common University Data Ontario (CUDO), the second was a discourse analysis of websites for five programs (Arts, Science, Business, Engineering, and Nursing) at each university, and third was Maclean’s (a Canadian current affairs magazine) rankings of university reputation. Phase 1 found that certain Ontario universities were distinctively selecting across most programs, while others were recruiting across most programs, and that the majority of Ontario universities had both selecting and recruiting programs. In phase 2, I conducted a discourse analysis of guiding documents (Strategic Mandate Agreements and Strategic Plans), and discourse analysis of publicly facing websites and viewbooks to explore how two of Ontario’s selecting universities and two recruiting universities commit to both widening participation and academic excellence. The findings reveal that Ontario’s selecting universities and programs prioritize being perceived as academically excellent (e.g., rigorous training, prestigious learning opportunities, leading innovation, and impact) on a global stage. Recruiting universities prioritize widening participation for local communities through strong recruiting and retention discourse and local community building through research. Recruiting and selecting universities use the discourse of academic excellence and widening participation to establish cognitive, moral, and pragmatic legitimacy. Selecting universities commit to widening participation to the extent that it does not challenge perceptions of global academic excellence. The findings of Phases 1 and 2 are relevant for policymakers, researchers, university leaders, and the public, as Ontario’s differentiated higher education system is not removed from the debate around equality of opportunity.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| 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".