Aid Suspensions as Coercive Tools? The European Union’s Experience in the AfricanCaribbean-Pacific (ACP) Context
Bibliographic record
Abstract
Since the signing of the Cotonou Agreement in 2000, the European Union (EU) has suspended development aid towards a number of African Caribbean and Pacific (ACP) countries in response to breaches of Human Rights and democratic principles by activating the so-called Human Rights clause (article 96). The present article analyses the use by the EU of aid suspensions as political tools and their efficacy in achieving the desired policy goals, in an attempt to identify and explain the determinants leading to the success of these measures. The investigation finds that the use of development aid suspensions is frequently effective. Classical sanctions theory appears to account largely for their success, given that most targets display a significant degree of dependence on the EU as a donor or a trading partner. However, and without refuting the explanatory power of that approach, a closer look at this practice unveils a number of factors that contribute to facilitate success. One of them is the selective use of the tool: suspensions are applied predominantly in cases of interruptions of the democratic process, while they are rarely used in situations of violent conflict. The specificities of the consultations mechanism, and especially the attitude of ACP neighbouring countries- often openly supportive-, largely determine the final outcome. Full textavailable at: https://doi.org/10.22215/rera.v3i2.155
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".