The Causes and Effects of Leaks in International Negotiations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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 it