Managed Globalization 2.0: The European Commission’s Response to Trade Politicization
Bibliographic record
Abstract
Several studies have sought to explain the politicization of European Union’s (EU) trade policy during negotiations on the Transatlantic Trade and Investment Partnership (TTIP) and the Comprehensive Economic and Trade Agreement between the EU and Canada (CETA). This article contributes to the literature on the politicization of trade by assessing how politicization is addressed by those tasked with the content and implementation of trade policy, namely the European Commission (hereafter Commission). We identify the origin and definition of managed globalization (MG), and thereafter identify, through a qualitative content analysis of EU Trade Commissioners’ speeches from 2013 to late 2017, how the doctrine re-emerged as the leitmotif of EU trade policy. The Commission’s initial response to civil society organizations’ contestation over TTIP and CETA was to insist on the economic benefits of the agreements. As contestation intensified, we find indirect references to MG, as the Commission focused on clarifying that upholding European values was equally important to market access in EU trade policy. Then, from late 2016 until late 2017, the Commission’s messaging was directed primarily at populist fears of trade and globalization; emphasizing that protectionism was unnecessary, and that globalization could be controlled, culminating in the emergence of explicit references to MG. The article expands on existing research on MG by identifying trade politicization as a factor that prompted a modification and expansion of the MG doctrine and its use, while also discussing some accompanying policy changes.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".