Canadian Higher Education’s Role in Shaping Global Cities as Sites of Racial Imperialist Capitalist Struggle
Bibliographic record
Abstract
This article maps out the role of Canadian Higher Education (CHE) in producing global cities as sites of struggle for racial imperialist capital. There are two main parts to the argument. One is that production of global cities has become essential to organizing global racial capital. The other is that within Canada, higher education, consisting of systems of universities and community colleges, is essential to producing global cities for monopoly finance capital (that is, global racial capital in its current form). In developing the analysis, the author pays particular attention to Toronto for several reasons: The author lives in Toronto; is faculty member of the University of Toronto where they have taught and studied postsecondary systems; has been active in Toronto grassroots struggles challenging militarized policing, particularly through participating in the No Pride in Policing Coalition working group; and, finally, Toronto is a designated “Global City” and is a key Canadian site of urban “innovation” for imperialist capital. That is, Toronto belongs to a network of “global cities,” each of which contains infrastructure necessary to coordinate the flow of global racial capital. Global cities are therefore “assets” that must be securitized, as evidenced by the intensification of militarized policing and surveillance. The article explains how Canadian higher education, through its systems of universities and colleges, has been shaped to produce “imperialized cities” for global racial capitalism. The author then outlines the abolition work that has been a source of inspiration for “Another University Now!”
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.021 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".