The next chapter: How Ryerson University became Toronto Metropolitan University
Bibliographic record
Abstract
The murder of George Floyd in 2020 and the discoveries of unmarked graves at the sites of former residential schools in Canada in 2021 have put the spotlight on the treatment of Black and Indigenous peoples across North America. This focus is increasing calls for the removal of names, statues and other forms of commemoration tied to figures from Canada's colonial history. By virtue of its name, Ryerson University — named after Egerton Ryerson (1803–82), formerly the Superintendent of Education and a primary developer of Ontario's public education system, who is also viewed to have paved the way for Canada's residential school system — became a focal point in this broader debate about how to recognise Canada's history and take the important steps forward on the path to reconciliation. In April 2022, Ryerson University became Toronto Metropolitan University, the first post-secondary institution in North America to change its name to address colonialism and better reflect its values of equity, diversity and inclusion. The decision to rename the 74-year-old institution was unprecedented. There was no blueprint, no budget and — for reasons of confidentiality and to manage potential leaks — the marketing and communications teams had to develop and execute strategies and tactics without knowing the official name. As Canadians learn more about Canada's history and the treatment of Indigenous peoples, the pressure on other public institutions to act in a manner with the principles of true reconciliation will continue to rise. This paper shares how the university's strategic communications and marketing teams supported the renaming by developing and executing a comprehensive plan to engage, inform, educate and prepare the community for the launch of a new name and pave the way for adoption. It presents the key components of the plan, strategies and tactics used, evaluation metrics, challenges and how they were met, and critical lessons learned.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".