Faculty Associations Confront Accountability Governance in Ontario Universities
Bibliographic record
Abstract
Much literature focusing on the academy is concerned with the spread of neo-liberalism into the university sector. We argue that universities in Canada are operating in an era of “accountability governance,” with ideologies, discourses, and practices centred on quality, accountability, and efficiency. We explore the interplay between accountability governance as a regime of power and the work of faculty associations, especially as they strive to preserve faculty members’ professional autonomy and control over their academic work. Using in-depth qualitative interviews with executive members of several Ontario university faculty associations, we explore themes of neo-liberalization and corporatization of the university, shrinking faculty budgets, program reviews, and strategic mandates. While opportunities for action and resistance for faculty unions arise, particularly at the level of senate, more militancy and radicalism are not favoured by many members, as political action is often seen as “unprofessional.”
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.056 | 0.026 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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".