Government, Anti-Reflexivity, and the Construction of Public Ignorance about Climate Change: Australia and Canada Compared
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article compares the political strategies used by conservative governments in Australia (John Howard) and Canada (Stephen Harper) to manage public impressions of climate change and climate change policy. These cases are significant in part because both governments acted against the weight of domestic public opinion. While many studies of political resistance to climate change mitigation focus on the role of denial, skepticism, and counter-claims, our comparison finds a significant role for what we call “affirmation techniques,” namely the rhetorical acceptance of the consensus position on climate change followed by concerted attempts to control precisely what acceptance means. We draw on recent theoretical work on anti-reflexivity and the sociology of ignorance to explain the political effectiveness of these strategies.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it