Bibliographic record
Abstract
Over the past two decades, universities in Canada have increasingly styled themselves as being invested in addressing anti-Black racism, coloniality, and histories of exclusion (McGill University, 2020; Scarborough Charter, 2022; Universities Canada, 2022).We see this institutional styling manifested in a range of administrative postures and performative gestures.To appear interested in addressing long-standing grievances of racism and colonization, universities have ramped-up and mass-promoted implementation of equity-focused initiatives such as equity, diversity, and inclusion (EDI) offices, executive-level EDI positions, equity-focused stakeholder consultations, race-based data collection initiatives, EDI working groups, subcommittees, and councils, as well as anti-racism charters, statements of solidarity, training, workshops, and so on.Yet these efforts have done little to meaningfully address entrenched forms of anti-Black racism at every level of the university (Hampton, 2016; Thobani, 2021;Walcott, 2021).Rather than review the innumerable ways in which institutions resist change, I provide a few examples of how institutions have nullified Black dissent, so shedding light on how universities respond to Black insurgency.I then put forward four principles derived from my higher education research grounded in the Black Radical Tradition and Black Queer Feminist theory, to offer criteria for unmaking the university. THE FORGETTING MACHINE: TWO EXAMPLESUniversities are committed to a performative course of action that does not meaningfully address entrenched forms of racial exclusion.Simply put, these strategies are meant to nullify dissent and maintain the status quo.Universities have become increasingly sophisticated in stifling challenges to power through incorporation, co-optation, and through what Roderick Ferguson (2012) refers to as "minority difference".Like the state and capital, the academy now takes up insurgence from oppressed communities, adapting hegemonic practices to discipline the challenge this represents to dominant actors.Together, these strategies impede and frustrate radical calls for change in the university.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.117 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".