Neoliberalism and Ontario Teachers’ Unions: A “Not-So” Common Sense Revolution
Bibliographic record
Abstract
This paper will critically analyze the degree to which the Ontario government, led by then Premier Mike Harris, embarked on a neoliberal agenda that led to a crisis in Ontario’s educational system. The period from 1995-2000 was one of the most contentious in Ontario’s educational history, and two pieces of legislation, The College of Teachers Act (Bill 31) and the Education Quality Improvement Act (Bill 160), pitted teacher unions, in particular, the Ontario Secondary School Teachers’ Federation (OSSTF) and the Ontario English Catholic Teachers’ Association (OECTA), against the Harris government. Bill 160 led to a ten-day protest by teachers across Ontario, which signaled a dramatic shift in teacher and state relations that marked a crisis period in Ontario’s educational sector.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.062 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".