A Study of Differentiation within the Ontario College Sector and the Impact of Geography
Bibliographic record
Abstract
In 1965 Ontario Education Minister, William G. Davis, introduced a new sector of Ontario post-secondary institutions, Colleges of Applied Arts and Technology. A primary purpose of this sector was to create access to education, particularly for those who were not accessing university. Additional primary characteristics of colleges were that they were to be comprehensive institutions offering a variety of primarily occupationally focused programs, and that they were to respond to local education and training needs. Over five decades later, although colleges have evolved in numerous ways, this study demonstrates that these key characteristics are still present. In 2012 the Ministry of Training, Colleges and Universities (MTCU) indicated it would be pursuing a policy direction of differentiation to, in part, create a more efficient post-secondary system in Ontario. This study explores potential impacts of differentiation policy on the traditional mandate of colleges as institutions designed to promote local student access and to respond to local community needs, and the potential impact on Ontario students and communities. Using theoretical frameworks of institutional diversity and differentiation, social policy theory and the capability approach, this study examines the levels and dimensions of existing diversity and differentiation in the Ontario post-secondary system. It draws on three data sources to undertake this study: document analysis of strategic mandate agreements; geographic attendance data for college and university students in Ontario; and interviews with institutional leaders and senior policy actors. The study’s findings confirm that college students in rural and remote communities tend to attend colleges that are located close to them. Students in the Toronto area demonstrate higher mobility between colleges located in Toronto but overall also display a tendency to attend regionally close institutions. University students also demonstrate in-region attendance patterns within the Toronto area but rural and remote university students overall are more mobile across the province. Through qualitative interviews and document analysis, this study also finds that rural and remote colleges still, five decades after their inception, demonstrate very high community connectedness and characteristics of strong regional responsiveness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".