The paradox of human rights conditionality in EU trade policy: when strategic interests drive policy outcomes
Bibliographic record
Abstract
Increasingly, trade agendas are expanding to include non-commercial objectives such as the promotion of fundamental political and human rights. Although the European Parliament (EP) positions itself as an advocate of such objectives in the conclusion of European Union (EU) trade agreements, it rarely insists on them in negotiations. Yet, in the negotiations with Canada, the EP successfully took a tough stance on a human rights conditionality clause. Why did the EP invest political resources in insisting on conditionality in the agreement with Canada – a country which is among the top five regarding fundamental rights? We argue that, due to limited organizational capacity, composite actors, such as the EP, have to select ‘strategic issues’ among political events that make them appear as unique supporters of public interest. In this context, composite actors factor in saliency in their utility calculation of investing political resources in a policy issue.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".