The process of judging the environment: civil procedure, environmental ethics and their effects on environmental law
Bibliographic record
Abstract
This research seeks to better understand judicial enforcement of environmental law in Canada. This involves inquiry into the rules of civil procedure, which both enable and limit the judicial role. It also involves exploration of environmental law principles and ethics, because procedural rules contain much discretion. This makes it necessary to understand what drives how judges interpret and apply these rules. The project aims to expand the field of "environmental ethics in law" by exploring the intersection of environmental law and civil procedure, and the implications of using environmental principles and ethics as a framework for procedural decision-making. I argue that several building blocks for putting environmental ethics into practice already exist. International treaties, domestic legislation, and recent jurisprudence suggest that Sustainability and principles associated with it, such as Precaution, are beginning to form part of the judicial framework. The problem is that courts often do not bring this environmental framework to bear when dealing with decisions that are "merely procedural". The thesis uses four pre-trial procedures—Public Interest Standing, Interveners, Pre-trial Injunctions, and Class Action certification—to demonstrate that procedure is never "merely procedural" and that the lack of an environmental framework can have significant substantive effect, such as preventing cases from getting to trial, either at all or before permanent environmental damage is done. The case studies suggest that courts have become more open to an environmental framework in relation to standing and intervention, and explores why there remains reluctance to take an environmental approach in certifying class actions. This understanding of the judicial role in environmental enforcement is important to improving the operation of courts generally. Environmental principles and ethics can lead courts and society to question what is deemed worthy of judicial protection, and how these interests are identified, valued and balanced. They can also encourage courts to enunciate the ethics and values that are always present in procedural adjudication, but often hidden behind a mask of procedural neutrality.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.081 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".