Use and Abuse of Adaptive Management In Environmental Assessment Law And Practice: A Canadian Example and General Lessons; (2010)
Bibliographic record
Abstract
Adaptive management theory recognises that we cannot make foolproof predictions of environmental impacts of human interventions into complex ecosystems. It mandates that environmental managers retain the ability to respond to change and inaccurate predictions. The Canadian Environmental Assessment Act (CEAA) authorises government to implement adaptive management into project follow-up. A key Canadian court decision has interpreted this to mean that adaptive management enables projects to proceed when mitigation measures are uncertain, that could be used in tempering the significance of impacts, and that it offsets the impact of the precautionary principle. Taking a legal perspective, the paper discusses how adaptive management may benefit environmental assessment, how the CEAA uses it, how a court has misinterpreted its role in the CEAA, and how it relates to the precautionary principle. In closing the paper sets out general lessons from the Canadian experience for the use of adaptive management in environmental assessment generally.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.038 | 0.066 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".