‘The wide and the narrow gate’: Benchmarking in the SCM Agreement after the<i>Canada–Renewable Energy/FIT</i>Ruling
Bibliographic record
Abstract
Abstract This article discusses the future of benchmarking after theCanada–Renewable Energy/FITcase. This decision left us with bad law. Assuming that any momentous shift, especially in the current regulatory framework that does not provide for any express justification for good subsidies, is difficult, we speculate on what may lie ahead for future litigants and the dispute settlement. Either ‘a wide’ or ‘a narrow road’ can now be followed. After outlining the risks that a normalization and expansion of this ruling may pose (the ‘wide road’), we have responded to the call for clarification and narrowing of this case (the ‘narrow road’) and speculated on how this could be done. The EUAltmarkdecision of the European Court of Justice, which was certainly on the minds of the EU litigators and whose ethos the Appellate Body embraced by referring to the use of ‘price-discovery mechanisms’, has inspired the analysis. The exercise has, however, exposed many challenges and difficulties, many of them having already occurred in EU law. The amount of helpful clarification the WTO judicature could offer is thus limited, and would probably be restricted to taking the link between market definition and benchmarking seriously. This unsatisfactory conclusion leads to suggest, once again, law reform as the only solution to the current status quo.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".