Educational Restructuring and the Policy Process: The Toronto District School Board 1997-2003
Bibliographic record
Abstract
This paper will examine the seminal events leading up to passage of the Fewer School Boards Act (Bill104), and its impact on the newly created Toronto District School Board (TDSB). While the focus of thispaper will be on Bill 104, it is important to note that significant structural changes needed to occur inToronto and its neighbouring municipalities before Bill 104 could take effect. To facilitate thesechanges, the Ontario government introduced and passed Bill 148 An Act to Establish a New City ofToronto. Bill 148 amalgamated the City of Toronto with the surrounding cities of East York, Etobicoke,North York, Scarborough, and York to create the "New" City of Toronto with a combined population of2.5 million citizens. Passage of Bill 148 cleared a path for the Ontario government to then pursuepassage of Bill 104. Passage of the Fewer School Boards Act amalgamated Toronto’s Public SchoolBoard with its five neighbouring cities but it also terminated the Metropolitan Toronto Public SchoolBoard. Where 74 trustees had represented citizens in Metropolitan Toronto, the newly elected 22-member TDSB became responsible for over 300,000 students, 21,000 employees, and almost 600schools. Each trustee represents a ward containing nearly 100,000 residents (Bedard and Lawton2000). While this study will focus on Toronto, both Bills 148 and 104 were part of a larger agenda bythe Ontario Progressive Conservative government to restructure a number of Ontario sectors, witheducation being the most predominant. As Leithwood, Fullan, and Watson note, the latter half of the1990s in Ontario can be viewed as "the most tumultuous in the province’s history" (2003, 1).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".