Higher education and national and global public good(s) in Ontario, Canada
Bibliographic record
Abstract
Abstract This paper explores contributions to public good(s) in higher education in Ontario, Canada. In a break from the trend of this special issue, this paper does not offer a comprehensive national study. Rather, it is based on a multiple case study conducted in a single predominantly English-speaking province, Ontario, drawing on 19 semi-structured interviews conducted in 2019 with policymakers and university staff, including those in academic and executive roles. The analysis offers novel insights into contemporary understandings of public good(s) in general, and Ontarian higher education’s contributions to the public good, and its relationship with the state and global community. Participants highlighted three dimensions of the public good: economic, knowledge-based, and social. While participants from the provincial and national spheres focused more on narratives of province- or nation-building through economic development and social mobility, university staff additionally highlighted knowledge-based contributions, emphasizing an educated citizenry, critical debate, and curiosity-driven research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".