Defending Teachers’ Rights and Promoting Public Education: Evolving and Emerging Union Strategies within a Globalized Neoliberal Context
Bibliographic record
Abstract
T eacher unions in Canada have been impacted by global recession, increasing competition from private schools, fiscal restraint that shrinks expenditure on education, and a neoliberal policy environment hostile to unions. What are the unique challenges teacher unions in Canada face, and what strategies are they utilizing to enhance their vitality? Canadian education is under provincial, not federal, jurisdiction (with the exception of aboriginal education), and the nature and extent of neoliberal education policy varies greatly from one province to another. To begin to address the question, I focus on a single case study—the case of the British Columbia Teachers’ Federation (BCTF), the sole bargaining agent for public school teachers in the province of British Columbia. The BCTF has mounted particularly strong, consistent, and more or less successful resistance to neoliberal education policy. The BCTF case illustrates a variety of strategies teacher unions are employing to battle neoliberal education policy, to protect their collective rights as teachers and union members, and to defend public education. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".