When do "weak" states win? A history of African, Caribbean and Pacific countries manoeuvring in trade negotiations with Europe
Bibliographic record
Abstract
Can small "weak" countries shape the outcomes of asymmetric trade negotiations and, if so, how? I scrutinise ten episodes of trade negotiations involving powerful European states and small developing countries from Africa, the Caribbean and Pacific (ACP) since the 1960s. I draw on legal agreements, public documents, interviews with and the written memoirs of key negotiators, media reports and the secondary literature. I show that ACP countries influenced outcomes in important ways. For each negotiation I establish the variation between European preferences and the final negotiated outcome and show that in four of the ten negotiations there was a substantial gap between what European countries wanted and the final outcome. Close examination and comparison of these ten negotiations suggests that when three conditions hold, small developing countries can exert substantial influence even in a profoundly asymmetric encounter: First, the small state must be able to "walk away" from the negotiation at no cost. Second, where the small state is considered to be highly strategic by the large state, it can use this as a source of leverage. Third, the small state must have the political leadership and technical skills to deploy an astute negotiating strategy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".