The Assessment of Environmental Damages Following the Supreme Court's Decision in Canfor
Bibliographic record
Abstract
Following the Supreme Court's ruling in British Columbia v. Canadian Forest Products Ltd., it is plain that any claim for environmental loss will have to be based on a coherent theory of damages and methodologies suitable for their assessment. The development of such a theory raises several important issues, including the nature of environmental harm and the purpose of compensation for such harm. An examination of the common law, legislation and academic literature reveals that protection of the environment, as a fundamental Canadian value, is predicated on the recognition that a healthy environment is requisite to the continuing well-being and prosperity of all Canadians, present and future. As such, any theory for assessing environmental damage must, first and foremost, recognize this function of the environment and seek to redress the injury done to it.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.026 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".