Pollution by Analogy: The <i>Trial Smelter</i> Arbitration [Abridged]
Bibliographic record
Abstract
Where there's muck, there's brass. – Yorkshire Folksaying. Every discussion of the general international law relating to pollution starts, and most end, with a mention of the Trail Smelter arbitration between the United States and Canada. For example, in the American Law Institute's Restatement (Second) of the Foreign Relations Law of the United States , the only precedent cited on the topic of a state's liability to another in connection with pollution is the Trail Smelter arbitration. Such heavy reliance on a single precedent breeds overstatement as analysts attempt to reinterpret the case to fit various hypothetical circumstances and new cases. Frequently, the precedent can be applied only by raising it to a level of abstraction far beyond the range of its logic. In the Restatement itself, the proposition that the Trail Smelter arbitration is cited to support is: The relation of cause to effect underlies the parallel principle that a state may be held responsible under international law for damage which it causes in the territory of another state. Thus Canada was held responsible to the United States under international law for the production of fumes in Canada which polluted the air in the United States. In fact, as will be seen, the arbitration did not hold that polluting the air in the United States was the basis of Canadian liability. But more of that later.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".