Differentiated Visions: How Ontario Universities See and Represent Their Futures
Bibliographic record
Abstract
This paper is concerned with long term strategic planning in higher education and focuses on Ontario’s strategic mandate agreement (SMA) sector planning framework. In 2012, the province initiated its new SMA planning process by requiring all higher education institutions to propose their own strategies for their academic visions, missions, and objectives. The proposals submitted by Ontario’s universities furnish the empirical content of this paper: a historically unique, comprehensive and comparable set of documents capturing institutions’ self-understanding and plans for their respective futures. Using concepts from organizational theory, content analysis of universities’ SMA proposals reveals divergent strategies, both in terms of institutional administrative responsiveness to the SMA process as well as the academic (i.e., education and scholarship) content of the submissions. In addition, two further sub-themes are analysed: proposals for experiential learning and so-called town-gown connections. Both themes also reveal very different visions amongst institutions. In general, the proposals appear to be independent of institution type and community size/location. Setting the stage for future research, the paper concludes with policy discussion of: (i) the possibilities for institutional diversity in the context of policy discourses on institutional differentiation; (ii) implications for system planning given the structure and process of Ontario’s ongoing SMA framework.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".