Taking into account of the environment in free-trade agreement impact assessments [Prise en compte de l'environnement dans l'évaluation des impacts des traités de libre-échange]
Bibliographic record
Abstract
Several large treaties are being negotiated or ratified today: between the European Union and the United States or Canada, the Trade in service agreement, three projects between EU and Africa, or the Transpacific agreement. We evaluate the methods of the many socio-economic impact studies of these treaties. Most of them take into account only the direct costs for the companies and not the external costs, social or environmental, which are much higher. The environmental impacts are taken into account only through sustainability impact assessments, whose input data are results of socio-economic impact studies. These impact assessments, in their most serious part, translate the economic impact assessments into impacts on pollutant emissions, consumption of raw materials or waste generation through inventory methods. But an inventory is only the first phase of an impact assessment. These studies try also to assess the impacts on other environment and sustainability aspects, as biodiversity, culture, inequalities, etc. but with a biased strictly economic rationality, without drawing on the variety of disciplines necessary for such exercises.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".