United we stood, divided we transform? Exploring coalition transformation divergence in the EU trade policy field
Bibliographic record
Abstract
During the EU-US (TTIP) and EU–Canada (CETA) free trade negotiations, large coalitions of civil society organisations were active not only across borders but also within European member states. In several countries, coalitions saw the opportunity to transform their issue-specific group into a general coalition on EU trade policy in order to achieve more sustained engagement. However, in hindsight, only some of the transformed coalitions remained active and visible with the same organisations, while others experienced a decline in visibility, activities, and membership. This study aims to explore the factors contributing to this divergence in coalition transformation, drawing on the literature from social movement and interest group studies. Based on interviews with trade activists in Belgium and the Netherlands, the analysis points to differences in perception of political and discursive opportunities, resource mobilisation, the degree of ideological and cultural overlap between the coalition’s actors, and organisational structure as important factors.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".