Beyond Academic Freedom: Canadian Neoliberal Universities in the Global Context
Bibliographic record
Abstract
In April 2010, the University of Toronto announced receipt of a thirty-five million dollar donation from the Peter and Melanie Munk Charitable Foundation to fund a new institute, to be named the Munk School of Global Affairs. This donation is emblematic of the intensifying neoliberalization of university governance in Canada, and critical responses to it have been twofold. Faculty members have suggested that academic freedom—an important principle that protects researchers from censorship, termination and other institutional pressures— is at risk. Students, on the other hand, have drawn attention to the source of Munk’s capital, pointing to his role as CEO of Barrick Gold, and to recent accounts of human rights abuses in Barrick’s mining practices. In practice, however, we have found that both academic freedom and human rights are arguably less available to people in some specific global and social locations. This article will critically analyze the discourses of academic freedom in Canada in relation to human rights discourses in the global South. Using anti-authoritarian intersectionality theory, we argue that in both instances the systems of oppression and exclusion are part of the same logic of global neoliberalism, inflected by intersectionally hierarchical practices of capitalism, patriarchy and colonialism. To conclude, we look briefly at alternative knowledge-production sites that strive for horizontalism in pedagogies, research and governance, and that attempt to eliminate hierarchies by experimenting with real practices of equality—practices that are fundamental to the accessibility of academic freedom.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.062 | 0.048 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".