Transatlantic Trade Disputes on Health, Environmental and Animal Welfare Standards: Background to Regulatory Divergence and Possible Solutions
Bibliographic record
Abstract
The paper analyses the pattern and background of transatlantic trade disputes where the US and Canada have challenged EU health, environmental or animal welfare.It shows that, in principle, the EU maintains stricter (or arguably higher) standards in these areas, partly due to some historic events or societal characteristics which make Europeans more risk averse, and partly due to the nature of the EU supranational regulatory process.The paper examines whether and how the currently negotiated agreements, CETA and TTIP, could address this regulatory divergence.It argues that the regulatory differences which reflect the different values of two constituencies are worth maintaining so as to foster pluralism, diversity, experimentation and democracy.In contrast, regulatory differences caused by the mere fact that regulators work independently of one another should be eliminated so as to achieve the benefits of greater trade liberalisation.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".