International Trade in Biofuels: Legal and Regulatory Issues
Bibliographic record
Abstract
Governments around the world are betting heavily on biofuels as one part of a solution to a wide range of public policy challenges, from environmental sustainability in the face of climate change, to energy security given rising geopolitical instability, to economic growth especially in rural regions and developing countries. Policy interventions typically take the form of legal and regulatory measures, for example, to drive demand for renewable fuels through mandates, or to subsidize costs through financial and other supports for production and processing of feedstock and output fuels. Such complex legal/regulatory mechanisms combine to create a multi-level or network system of governance. This article analyzes the implications of this complex framework for the production and international trading of biofuels.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.011 |
| 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".