Development of a planning assessment to repurpose and redevelop under-utilized and deteriorating Toronto District School Board properties into community hubs
Bibliographic record
Abstract
One of the main issues faced by the Toronto District School Board (TDSB) is the poor structural condition of its aging school buildings. As of school year 2016/2017, the total renewal/repair backlog for all of TDSB schools has reached $3.4 Billion, with approximately 103 TDSB schools operating at a 65% or lower utilization rate (TDSB, 2014f). There is an immense pressure on school boards, particularly the TDSB, to sell off schools that have been declared as surplus (Mangione & Suen, 2015). However, the selling of school properties that have a high social and economic value is not a sustainable approach for the long run. Hence, there is a need for an effective and resilient planning strategy that will efficiently address the issues faced by the TDSB. As a result, this research will be recommending a land assessment tool that will efficiently repurpose and redevelop school properties, in critical condition, into community hubs and replace old and deteriorating TDBS schools with newer school facilities. Key Words: TDSB; Surplus Schools; Community Hubs; Shrinking Cities; Public Assets;
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".