Bibliographic record
Abstract
This master's thesis deals with climate policy integration in two European union's trade agreements, EU-Canada Comprehensive Economic and Trade Agreement (CETA) and EU-Japan Economic Partnership Agreement (EPA). Ambitions of EU's climate policy have grown in recent years. Therefore the EU needs to cooperate with other world countries to tackle the climate change now even more than ever before. One of the solutions for such a binding cooperation to fight climate change could be implemented through the EU trade policy. This master's thesis is therefore interested in climate policy integration concerning the policy coherence during the process of making trade agreements and also in climate policy aspects of the final form of the agreements. In the theoretical part, this thesis describes the academic debates of policy coherence, climate policy actors in the institutional framework of the EU and also the history of EU's climate policy. Research operationalises the academic concept of climate policy integration (CPI) and carries it out through analysisand comparison of official EU's institutional documents. In the final part, this master's thesis draws its conclusions mainly from comparison of EPA and CETA.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".