A Case Study of the Governance of a U of T Capital Project: 90 Queen’s Park
Bibliographic record
Abstract
This case study examines the governance, accountability, and transparency in the approvals process of the 90 Queen’s Park capital project at the University of Toronto (U of T). This capital project, which will have a stark golden exterior, is heralded by the university as the new “gateway to campus” and will be the future home of the Centre for Civilizations, Cultures, and Cities. Although the university affirms that the project’s consultation process went beyond legal requirements and several changes to the plans were made based on feedback, there has been much public opposition to the project. In fall 2020, this push-back resulted in a pause in approvals at the City of Toronto, where councillors have required a review of the “cultural heritage landscape” before going forward. We found that information on the consultation process, feedback, and approvals process was easier to identify in city documents than through university governance systems. For instance, the financial approvals for the capital project were made “in camera” at all levels of university governing council. This case study is part of broader interest in accountability and transparency in capital project development at the university, and we conclude by inviting contributions and reflections from other units and universities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".