Pollution control, administrative discretion and science: a journey through the maze of environmental law
Bibliographic record
Abstract
This thesis explores the interface between environmental regulatory discretion and scientific complexities. Two key observations about the nature of scientific information are made which lead to the argument in this thesis that science must be made more explicit in environmental decision making processes. First, scientific analysis is crucial to understanding the impact of pollution on the natural environment. Thus, it is fundamental to the design and implementation of environmental laws. Secondly, scientific information has certain methodological limitations and inherent uncertainties which often make it subject to interpretation and value judgments. These judgments involve important policy choices about environmental risks. In Canada, the use of science to shape environmental laws is a matter for bureaucratic discretion that is rarely subject to external scrutiny. This thesis argues for an express statutory obligation on environmental administrators to disclose and to explain the scientific analysis used to support the exercise of their discretion in making regulatory decisions. Discretionary powers are a necessary and permanent part of the Canadian legal landscape of environmental protection. Within this regulatory context, science is segregated as the rational "factual" basis for decisions when, in fact, science cannot be disentangled from the economic, political and social dynamics that influence regulatory discretion. The implications of this are illustrated by the experience of the United States which is considered for purposes of comparison in this thesis. Under U.S. environmental laws, regulators are required to disclose and to explain the scientific analysis used to support their regulatory decisions. While it is clear that a procedural "fix" is not a panacea, it does offer some distinct advantages that enhance the democratic legitimacy of environmental decision making. In short, a legal duty of this kind will improve the process of environmental decision making in Canada: (1) by requiring a more thoughtful analysis of the administrative task and the relevant information and thus increasing the accountability of regulators; and (2) by helping to harness and make accessible a valuable pool of knowledge. As a result, the integrity of the decision making process in Canada will be improved by making analysis more transparent and subject to challenge.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.093 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| 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".