When politicization meets ideology: the European Parliament and free trade agreements
Bibliographic record
Abstract
Abstract Since the Lisbon Treaty, the European Parliament (EP) has considerably increased its competencies in European Union (EU) trade policy. At the same time, a ‘new generation’ of free trade agreements (FTAs), including the Transatlantic Trade and Investment Partnership (TTIP) with the United States, Comprehensive Economic and Trade Agreement (CETA) with Canada, and the agreement with Japan, have been negotiated by the European Commission. Although existing literature has tackled the process of the EP's institutional self-empowerment in this policy area, there is no systematic research investigating the lines of conflict within the EP over FTAs. Through a newly collected dataset of all EP plenary debates between 2009 and 2019 on six relevant FTAs, we extract EP Members’ (MEPs) preferences by means of a manual textual analysis. We then test the explanatory power of the two traditional lines of cleavages within the EP over MEPs stated preferences: position on the left-right axis and support for EU integration. We find that both these dimensions fundamentally shape the conflict in the EP over FTAs. The impact of these two ideological cleavages is magnified in the context of politicized FTAs, namely the TTIP and CETA. Through these findings, the paper significantly contributes to the research on competition in the EP and, more broadly, to the understanding of EU trade policy and its emerging politicization dynamics.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".