Bibliographic record
Abstract
This study examines publicly voiced resistance by a Canada-wide community of scientists and citizen supporters against what they perceived as the Canadian government's efforts to undermine publicly supported science, with its concern for empirical evidence, in order to facilitate a narrowly pro-industry orientation in its policy-making. Using Hajer's argumentative discourse analysis (ADA) to interpret a corpus of some 700 Web-published texts, the author identified a macro-argument collectively produced and publicly communicated by the Canadian scientific community. The study also showed how this macro-argument served as a vehicle for two ideological representations: a virtuous self-representation of the scientific community itself and a negative representation of the motives and actions of the Canadian government. The findings of the research contribute to our understanding of how collective argumentative positions emerge within the discourse of a major scientific controversy. At the same time, the study offers policymakers insights in how they might communicate more effectively with communities of scientific experts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".