When is a Voucher not a Voucher? Debating Ontario’s Equity in Education Tax Credit
Bibliographic record
Abstract
The purpose of this paper is to share findings from a study of the debate over Ontario’s Equity in Education Tax Credit (2000-2005). The study was grounded in Hajer’s argumentative discourse theory. According to this theory, politics and policies are struggles for discursive dominance wherein actors attempt to persuade others to support their definitions of the social world. Policy actors refer to story lines in their change efforts, and actors engaging the same story lines form discourse coalitions that advocate or sustain particular interpretations of social situations. Texts produced by policy actors between 2000 and 2005 that contain references to the Equity in Education Tax Credit were analyzed using Argumentative discourse analysis (ADA), a means of identifying story lines. Preliminary findings show two discourse collations engaged in the debate over the meaning of the tax credit. One coalition’s story line asserted that the policy increased parental choice and equity by enabling all parents to send their children to their private schools, while the other coalition argued the policy benefitted the wealthy and took scare public resources out of public schools via “a voucher in all but name”. Implications for contemporary debates over public funding of private schools are discussed.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".