Bibliographic record
Abstract
We view Maarten Hajer’s argumentative discourse analysis (ADA) as a potent methodology to investigate policy change and continuity over time. In this chapter, we present—as an illustrative example of using ADA—findings from our comparative case study of the historical debate over public funding of private schools in the Canadian province of Ontario. We begin by situating our research within critical and historical orientations to policy study. We then provide an outline of Hajer’s argumentative discourse theory and his ADA approach. We also describe in detail how we applied this methodology in our research. To demonstrate ADA’s utility, we sketch the shifts in discourse coalitions’ arguments around three key inflection points in Ontario’s historical debate. Finally, we reflect on how useful argumentative discourse analysis can be for researchers, while taking into account some limits to our study, and we conclude that an ADA approach will, above all, illuminate connections between ever-shifting sociohistorical contexts and meanings in policy debates.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".