Research on Higher Education in Canada: History, Emerging Themes, and Future Directions
Bibliographic record
Abstract
As in many other countries, higher education in Canada assumes a key role in discussions of the social and economic development of the nation. It is clearly a “high participation system of higher education” and increasing student participation has been a major and fairly consistent policy theme since at least the 1960s, providing a foundation for one of the world’s most educated populations, but it is also a system that struggles to address inequities in participation, especially for Indigenous and other under-represented populations (Jones, 2018). Universities also play an extremely important role within Canada’s research and innovation system; the modest level of business investment in research and development and the central role of universities in conducting research positions the sector as a core component of the nation’s research infrastructure. At the same time, higher education in Canada is highly decentralized, in fact it is frequently argued that Canadian higher education is best understood as the sum of thirteen quite distinct provincial and territorial systems, and the role of the federal government has largely focused on supporting research, a national student loans program, and a range of policy issues that intersect directly or indirectly with institutions of higher education. Given this highly complex, decentralized, multi-system arrangement, what is the state of research on higher education in Canada? This paper provides a largely descriptive analysis of the evolution of higher education as a field of study in Canada, focusing primarily on academic scholarship within this field of inquiry. We then focus attention on a number of more emergent themes or research topics that have received considerable attention and conclude by discussing some of the future trends associated with Canadian higher education research.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.052 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".