Does structure matter: (Where) do questions about structure fit on the higher education policy agenda? Paper presented at the John Deutsch Institute’s Higher Education in Canada Conference
Bibliographic record
Abstract
The primary focus in higher education policy discussions in Canada for the past twenty-five years has been on the topics of funding levels and mechanisms, accountability, accessibility, and quality. To be more precise, the focus has been on these topics in relation to existing postsecondary education institutions as each of them continues to perform within the framework of its existing institutional mission. The recent focus in higher education policy discussions contrasts with the focus in an earlier era, from the early 1960s to the early 1970s. In that decade, the emphasis was not so much on the individual institution, as on the structure of emerging and developing systems of higher education. By structure, I mean the distribution of postsecondary institutions by size, mission and type, and by geographic location. In that earlier period, across Canada a lot of effort went into the question of what was the optimal, or at least most appropriate, structure for higher education systems. The outcomes of these efforts included some remarkably articulate and cogent visions for provincial higher education systems, such as the Macdonald Report in British Columbia, the Parent Commission in Quebec, and- fittingly, given the host of this
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".