Environmental Damages after the Federal Environmental Enforcement Act: Bringing Ecosystem Services to Canadian Environmental Law?
Bibliographic record
Abstract
The Canadian Environmental Enforcement Act [EEA] directs judges to consider actual environmental damage, or risk thereof, when setting fines for environmental offences. The EEA defi nes damage as including the loss of use and non-use values. While these terms are not unprecedented in Canadian environmental law, their use in environmental damage assessment is. Bearing in mind recent developments in environmental valuation in the United States and internationally, and considering the emergence of the “ecosystem services” paradigm in particular, this article explores the opportunities and challenges for ecosystem services based environmental damages assessment in the Canadian environmental sentencing context. The ecosystem services concept, much written about in American legal literature, provides a framework for identifying and organizing the numerous direct and indirect contributions that ecosystems make to human well-being, the value of which can then be expressed in economic terms. Although novel and ambitious in some respects, this approach would be consistent with both Parliament’s intention in passing the EEA and with the pre-existing common law framework for environmental sentencing in Canada.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".