Higher EducationPolicy in Saskatchewanand the Legacy of Myth
Bibliographic record
Abstract
The rationalization and coordination of the university sector has been a major public policy concern for the Government of Saskatchewan. Following two major inquiries in the 1990s, policy-making in this area has been placed within a logical, coherent framework. This essay steps back from the particulars of specific models and planning processes to consider broader conceptual issues. Public policy is shaped by the historical context in which it has arisen, including well-established "myths" that are never subjected to critical scrutiny. For this very reason, such "myths" function as extremely effective instruments of public policy. Using the Roland Barthes definition of myth, the essay argues that the University of Saskatchewan, for most of its history, has been in the grip of a powerful myth that has helped shape its identity and govern its decision-making. According to the myth, there is only one university in Saskatchewan, and it operates without interference from the Provincial Government. The myth was most prevalent from 1907, when the University Act was passed, to 1974, when the University of Regina came into existence, but its effects have not been altogether extinguished. It exerted great influence over the history of higher education in Saskatchewan, but, as with all with myths, as soon as it is identified and dissected, it dissolves, enabling policy-makers to understand issues and problems in a new light.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".