TDSB - TO P3 or Not? Exploring alternatives in public-private partnerships for including public schools in mixed-use vertical developments in Toronto
Bibliographic record
Abstract
In Toronto, the Toronto District School Board is facing overenrollment and school infrastructure shortage in areas of rapid growth. In these neighbourhoods, TDSB faces challenges of limited land availability and inadequate financial capability, and it is looking at mixed-use developments and public-private partnerships to address these issues respectively. This research project assesses the viability of these strategies through a series of case studies. While constructing new schools through p3 is not new to Canada, the construction of public schools in mixed-use vertical developments through p3 is relatively new and this research project explores this emerging concept. P3s for building public school infrastructure in mixed-use developments have been applied with varying degrees of success. Such projects present opportunities for the TDSB to address enrollment issues in overcrowded areas, achieve its infrastructure goals, provide quality public facilities, support after-school-hour usage of these facilities, lighten financial burdens, reduce backlogs on maintenance and repairs, and continue ownership of public school lands in the coming years. Not all schools can be built using p3s, and not all schools can be built in mixed-use developments. School boards should approach partnerships with thoughtfulness, carefully weigh the benefits and challenges, and adopt a model that is suited to the local context.
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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 | 0.004 | 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 | 0.010 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".