The Causes and Effects of Leaks in International Negotiations
Bibliographic record
Abstract
Abstract International negotiations are founded on secrecy. Yet, unauthorized leaks of negotiating documents have grown common. What are the incentives behind leaks, and what are their effects on bargaining between states? Specifically, are leaks offensive or defensive: are they intended to spur parties to make more ambitious commitments, or are they more often intended to claw back commitments made? We examine these questions in the context of trade negotiations, the recurring form of which affords us rare empirical traction on an otherwise elusive issue. We assemble the first dataset of its kind, covering 120 discrete leaks from 2006 to 2015. We find that leaks are indeed rising in number. Leaks are clustered around novel legal provisions and appear to be disproportionately defensive: they serve those actors intent on limiting commitments made. The European Union (EU) appears responsible for the majority of leaks occurring worldwide. Using party manifesto data to track changing ideological positions within the EU, we find that the occurrence of leaks correlates with opposition to economic liberalization within the average EU political party. Moreover, leaks appear effective in shifting public debate. We examine trade officials’ internal communications and media coverage in the wake of a specific leak of negotiations between Canada and the EU. A given negotiating text attracts more negative coverage when it is leaked than when the same text is officially released. In sum, political actors leak information strategically to mobilize domestic audiences toward their preferred negotiating outcome.
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".