Rámování klimatických politik: Diskurzivní analýza debat o oceňování emisí oxidu uhličitého v Kanadě a Austrálii
Bibliographic record
Abstract
Framing Climate Policies: Discourse Analysis of Carbon Pricing Debates in Canada and Australia Abstract The aim of this paper is to analyze and compare the discourses of Stephen Harper and Tony Abbot during federal election campaigns where climate policies played an unusually important role (2008 in Canada and 2013 in Australia). The study builds on a hypothesis, that according to the post-materialist theory and the Environmental Kuznets Curve, such economically advanced, democratic countries as Canada and Australia should be at the vanguard of climate action. However, in reality they are some of the worst performers when it comes to tackling carbon emissions. Both Harper and Abbott publicly promised to put in serious efforts to tackle climate change. However, when the question of setting a national price on carbon came up for discussion during the above-mentioned election campaigns, they both not only opposed it, but even tried to discredit it by framing the whole debate in overwhelmingly negative terms. In order to uncover what kind of frames and other discursive strategies the two politicians used to shape the debate, critical discourse analysis was applied to their public statements on the policy of carbon tax. Results of this analysis show that they used all of the frames that are typically associated...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".