Policy advocacy, inequity, and school fees and fundraising in Ontario, Canada
Bibliographic record
Abstract
Fundraising and collecting fees are ubiquitous in Ontario, Canada’s public schools. Critics assert that these practices perpetuate and exacerbate inequities between schools and communities. In this article we present findings from a critical policy analysis of an advocacy group’s efforts to change Ontario’s fees and fundraising policies over the past two decades. Rhetorical analyses of 110 texts finds that the group constructed the problem of each policy similarly, targeted the same audiences, and utilized many of the same strategies to appeal to logos, ethos, and pathos in their struggle over the policies’ meanings. However, only one out of four of the group’s policy meanings became dominant. The discursive and critical policy perspectives grounding the study directed us to examine how neoliberalism and the policies’ shared broader social, political, and economic contexts can help explain this outcome. Specifically, the group’s efforts to change Ontario’s school fees and fundraising policies confronted dominant discourses that construct parents as consumers of education and responsible for their children’s success in a competitive world, promote the meritocratic notion that successful people deserve their success and the benefits it brings, view the government as responsible only for providing the basic requirements of education, and support privatization and marketization of public schools.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".