CANADA – FEED-IN TARIFF: ARE FITS DESIRABLE, OR EVEN LEGAL? A CASE COMMENT
Bibliographic record
Abstract
This article summarizes the World Trade Organization Appelate Body's report in Canada – Feed-in Tariff. It focuses the report's three main issues: (1) the application of the Agreement on Trade-Related Investment Measures' Illustrative List to the minimum required domestic content levels in light of article III:8(a) GATT; (2) the article III:8(a) GATT derogation to a violation of national treatment; and (3) the determination of whether a “benefit” has been conferred under article 1.1(b) of the SCM Agreement. It will mainly be argued that the article III:8(a) analysis is overly simplistic and that the subsidy benefit determination technique employed by the Appelate Body is both overly complex and poorly suited for government-regulated markets. Each of these issues comports novel components: it is the first time the TRIMs Agreement's Illustrative List is applied in relation to an article III:4 GATT violation; it is the first time the article III:8(a) derogation is applied in jurisprudence and the article 1.1(b) of the SCM Agreement's benefit determination gives new perspectives regarding government-created markets. The discussion will ultimately move to an analysis of a possible defence under the GATT XX general Exceptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.010 |
| 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".