The role of non-state actors in the enforcement of environmental laws
Bibliographic record
Abstract
Weak enforcement of environmental laws is a global trend that worsens environmental threats, notwithstanding the prolific growth of environmental laws and organizations worldwide. A significant challenge to the enforcement of environmental laws is the state actor’s lack of political will, which has motivated the involvement of Non-State Actors (NSAs). This study set out to determine whether NSAs influence the enforcement of environmental laws and the conditions under which non-state action has led to better enforcement. The study had three objectives. First, to identify the strategies used by NSAs in effecting the enforcement of environmental laws. Second, to explore the impacts of non-state action on Canadian environmental law enforcement. Third, to discuss the possible application of these findings to a different jurisdiction. I adopted a qualitative analytical approach using data collected from documentary analysis and interviews to answer the research questions. The focal point of this research is a case study analysis of the Wood Buffalo National Park (Canada), where NSAs frustrated by domestic setbacks to enforcing environmental laws chose transnational circumvention as the next best option. The thesis establishes that NSAs have assisted in efforts leading to the enforcement of environmental laws in Canada, and the conditions for success vary on a case-by-case basis.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".