How <i>Canada Renewable Energy</i> Supports the Use of the Alternative Commercial Reasonableness Standard in Future Feed-In Tariff Disputes
Bibliographic record
Abstract
In the Canada - Certain Measures Affecting the Renewable Energy Generation Sector and Canada - Measures Relating to the Feed- In Tariff Program disputes,1 Japan and the EU claimed that Canada was subsidizing certain renewable energy generators in Ontario through the use of a feed-in tariff programme. In those disputes the panel and the Appellate Body rejected the proposed market benchmarks during their 'benefit' analyses under Article 1.1(b) of the WTO Agreement on Subsidies and Countervailing Measures. Then, insufficient evidence prevented the dispute settlement organs from completing the analyses using their own ad hoc market benchmarks. Future disputes are at risk of similar unsatisfactory outcomes, especially when distorted markets are present. This article suggests the use of an alternative 'benefit' analysis using the 'commercial reasonableness' standard from EC - DRAMs and Japan - DRAMs when proposed market benchmarks are rejected.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".