The Making of the Campus Namescape: A Comparison of University Naming Policies in Canada and the United States
Bibliographic record
Abstract
The naming of places on university campuses plays an important role in shaping the cultural landscapes and geographies of higher education institutions. In recent years, there have been contentious debates over place renaming at colleges and universities in North America and around the world, which has drawn increasing attention to the politics of toponymic practices in higher education contexts. The decision-making process involved in place naming on a university’s campus is generally informed by the institution’s naming policy and implemented by a university naming committee, yet there is very little scholarship on university naming policy frameworks, procedures, and practices. In this article, we provide a systematic and comparative analysis of university naming policies in Canada and the United States. Drawing on data from more than 2,000 colleges and universities across North America, we assess the level of representation that faculty and students have on university naming committees, institutional commitments to public engagement in the naming process, the value of diversity, and restrictions on corporate naming rights agreements. We conclude that colleges and universities should develop more inclusive and equitable naming policy frameworks to ensure that campus namescapes live up to the ideals of higher education institutions in the twenty-first century.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".