Bibliographic record
Abstract
The return of the Conservatives to power in Ontario, Canada in 2018 saw major attacks on the province’s K-12 education system, centering on increases to class size and mandatory e-learning courses for students which, taken together with other budget cuts, amounted to the elimination of thousands of teaching and support staff positions, as well as threats of privatization. These policies provoked widespread resistance from education workers, who as union members and grassroots activists conducted extensive outreach to build public support, engaged in job actions, and participated in the largest strikes in Ontario for decades as part of the campaign for “No Cuts to Education.” The start of the COVID-19 pandemic in the spring of 2020 ended the movement. This article assesses the victories and defeats of this key struggle in defense of public education. It considers the strategies and tactics of provincial and local union leadership and activist members, in which the battle with the provincial government for the alignment of public support was widely recognized as being of decisive importance. The author uses autoethnographic research as a local union leader, interviews with active union members, policy documents, union statements and media coverage to construct an historical account. This experience has relevance for studies of teachers’ resistance to the neoliberalization of education, as well as social movement unionism and its challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".