Time to Preference: Early Preference Uptake under the EU-Canada Comprehensive Economic and Trade Agreement and the EU-Korea Free Trade Agreement
Bibliographic record
Abstract
This study examines the uptake of trade preferences under the EU-Canada Comprehensive Economic and Trade Agreement and the EU-Korea Free Trade Agreement during their respective first 21 months of application. The research analyzes the impact of time on the preference utilization rate of EU imports from Canada and Korea and EU exports to the two countries. The findings shed light on how EU member states perform vis-à-vis each trade partner and whether certain product groups appear more successful than others in terms of using trade preferences. The study further analyzes the potential effects of learning how to use preferences over time. Finally, the study argues that firm-pair transaction level data is necessary for discerning more conclusive answers regarding why trade preferences are (not) used. The results point to that lack of knowledge and awareness is the most plausible reason to a low use of trade preferences in the early days of an agreement. To increase preference utilization rates in the beginning as well as later during agreement implementation, continuous information campaigns appear to be essential, not least since importing and exporting firms change over time.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".