Renegotiating in good faith: How international treaty revisions can deepen cooperation
Bibliographic record
Abstract
Abstract International agreements are often understood to help governments make credible commitments to future policy by limiting their ability to renege on their promises. Renegotiations of agreements are accordingly viewed as a threat to cooperation, since renegotiations call past commitments into question. But we know little about the frequency or nature of treaty renegotiations. When are international agreements renegotiated, and what effect does renegotiation have on international cooperation? Do most renegotiations indeed aim to backtrack on past commitments? Using the topical context of the trade regime, I collect new data on international treaty revisions, covering 310 preferential trade agreements signed since the year 2000. Around a quarter of these agreements have been amended in some form, and the supermajority of amendments result not in scaled back agreements, but in deeper commitments. Survival analysis shows that ‘like-minded’ countries with a shared language and similar voting patterns at the UN General Assembly are most likely to revise their commitments. In contrast, I do not find evidence to support the view of PTA revisions as ‘backsliding’ on past commitments. The effects of revisions on trade cooperation support the more cooperative view of revisions. An error-correction model shows revisions are associated with a long-run increase in export volumes. Renegotiations are not breakdowns in international relations, but opportunities for governments to renew their commitment to cooperation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".