Cancel Culture, the Paradox of Commemoration, and the Need for Public Interest Relations
Bibliographic record
Abstract
Background: Toronto Metropolitan University, formerly known as Ryerson University, faced a significant reputational crisis in 2021, when Egerton Ryerson, whose name it commemorates, was heavily criticized for causing significant and intergenerational harm to Indigenous communities in Canada. In response to mounting public pressure, the university went through a year-long renaming process that involved reviewing records and consultations with a wide range of stakeholders. On April 26, 2022, its new name, “Toronto Metropolitan University,” was announced. Analysis: This commentary views the case from the lenses of cancel culture and crisis communication. It elucidates (1) the problematic nature of characterizing the public outcry for renaming Ryerson University as a manifestation of cancel culture, and (2) the ineffectiveness of traditional image-repair tactics in addressing paradoxes of commemoration, where organizations struggle to navigate the constrained space created by polarizing narratives. Conclusions and implications: Overall, the commentary argues that debates about Egerton Ryerson's role in the establishment of the Residential School system highlight essential elements of racism and colonialism in contemporary Canadian society; thus, dismissing his critics as mobs motivated by cancel culture overlooks the distinctive role of universities in fostering, promoting, and engaging in dialogue and diverse viewpoints.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.059 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".