Bibliographic record
Abstract
Preface. List of Abbreviations. 1. Introduction and research problem. 1.1. Internationalisation and domestic regulation. 1.2 Overview of this study. 1.3. The California effect. 1.4. The research problem. 1.5. The research design and the use of theory. 1.6. Outline of the book. 2. A theoretical framework. 2.1. Introduction. 2.2. International bargaining. 2.3. Domestic politics and international bargaining. 2.4. The interaction between international and domestic politics. 2.5. Summary and hypotheses. 3. The European leghold trap regulation. 3.1. Introduction and background. 3.2. The European ban. 3.3. The potential impact of the European Regulation on the US and Canada. 3.4. Compatibility with GATT/WTO law. 3.5. Negotiating an agreement. 3.6. The implementation of the agreements and further domestic developments. 3.7. Conclusions. 4. The European ban on the use of growth-hormones in meat production. 4.1. Introduction and background. 4.2. The European ban on the use of growth-hormones. 4.3. The initial situation in the US and Canada and early developments. 4.4. International developments and the WTO cases. 4.5. The process after the WTO cases. 4.6. Conclusions. 5. Genetically modified foods and food products. 5.1. Introduction and background. 5.2. The initial situation in the US and Canada. 5.3. The European regulation of GM foods. 5.4. International developments. 5.5. Reactions in the US and Canada. 5.6. Conclusions. 6. The European data protection directive. 6.1. Introduction and background. 6.2. The EC data protection Directive. 6.3. The European Directive and international trade law. 6.4. The European Directive and the US. 6.5. The European Directive and Canada. 6.6. Conclusions. 7. Summary and conclusions. 7.1. The eight cases and their outcomes. 7.2. Explaining the outcomes. 7.3. Generalising the conclusions to other cases. List of interviews. References. Index.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".