Why Do Democracies Exit International Agreements? The Role of Information in International Cooperation on Climate Change
Bibliographic record
Abstract
In December 2011, the Canadian government decided to officially withdraw from the Kyoto Protocol on climate change. While this decision has been expected by some negotiators and policy-makers, it poses an interesting question for political-economic theories of international cooperation: Why do governments exit international agreements that they previously committed to on a voluntary basis? Without a supranational enforcement power at the international level, no country can be forced to participate in an international treaty, and agreements must be self-enforcing. However, international cooperation becomes vulnerable to withdrawal as soon as governments and, even more so, domestic electorates learn about the true benefits from cooperation. While cooperation may be a reasonable choice ex ante, this may change after an informational shock occurs. When payoff uncertainty disappears, ex post cooperation benefits may be much lower than they were initially believed to be. With highly skewed benefit distributions, countries at the lower end of the distribution are suddenly better off as free-riders. If voters know about this benefit distribution, they back their national governments in exiting international agreements. This theory rationalizes the Canadian government’s behavior with the Kyoto Protocol.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".