THE NORMALIZATION OF SCHOOL FUNDRAISING IN ONTARIO: AN ARGUMENTATIVE DISCOURSE ANALYSIS
Bibliographic record
Abstract
Fundraising is common in Canada’s public schools despite objections from some parents, educators, and other citizens who argue that the practice perpetuates inequities between schools and communities. In this article I first situate school fundraising in North America within its broader socio-historical contexts. I then describe Hajer’s (1997) argumentative discourse theory that grounds my investigation of the struggle over fundraising policy that has taken place in Ontario over the past 20 years. Drawing on findings from an argumentative discursive analysis of 159 texts produced since 1996, I present the arguments (i.e., the story lines) of two discourse coalitions that have engaged in the struggle over the meaning of school fundraising: the fund-the-basics coalition and the fundraising-is-necessary-and-desirable coalition. I demonstrate the dominance of the fundraising-is-necessary-and- desirable coalition’s argument by showing diverse ways the practice has become institutionalized and highlight aspects of the policy context that have contributed to the normalization of school fundraising. Finally, I conclude with a discussion of the utility of Hajer’s argumentative discourse theory and analysis in education policy research and highlight contributions of the current study to understanding of advocacy, media, and education policy change.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.042 | 0.034 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 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".