ASSESS_CETA: Assessing the claimed benefits of the EU-Canada Trade Agreement (CETA)
Bibliographic record
Abstract
In late 2016, a decision will be made by the Council of the European Union whether to launch the ratification process of the free trade agreement between the EU and Canada (CETA). The European Commission (EC) is promoting the agreement with the prospects of more trade, stronger economic relations and job creation. However, studies on the economic impact of CETA report only marginal effects on GDP of 0.03% to 0.08% for the whole of the EU. In other words, CETA is expected to generate a one-time income effect of around 20 EUR per EU citizen after a 10 years implementation period. Despite these small effects by CETA, it is worthwhile to question models and assumptions that stand behind these estimations and show neglected risks and adjustment costs. This task is highly relevant, given that the EC is stressing the innovative character of the agreement as it includes intensive regulatory cooperation and strengthens investor protection via the controversially discussed investor arbitration mechanism. CETA is therefore considered the blueprint of the future EU trade policy that focuses on new topics such as regulation, liberalization of public procurement and the promotion and protection of investment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".