Carbon capture: the rise of the influence of Australia and Canada on climate negotiations
Bibliographic record
Abstract
In this paper, we explore the emergence of an alliance between Australia and Canada, an alliance that helped to derail climate change negotiations at two international meetings in 2013. We hypothesise that carbon-based industries create policy ties to national governments in order to forestall regulation, using negotiators to create a global policy corral (Barley, 2010). We use three events in 2009 that increased risks to carbon-based industries as a natural experiment: the change in the US presidency, the onset of the Great Recession and the sudden rise in Chinese investment in photovoltaics. Using panel data, we create a model for the impact of social, political and environmental factors and for the changing influence of industries. We find that the correlation between national carbon assets and climate policy increases in these two countries after 2009, suggesting that corporate interests were able to incorporate these governments into new international policy corrals.
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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 | 0.001 | 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 | 0.000 |
| 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".