Shifts in Environmental Governance in Canada: How are Citizen Environment Groups to Respond?
Bibliographic record
Abstract
During a period when the relationship between government agencies and citizen environmental monitoring activities is shifting, this paper examines the nature of the relationship between government and citizen stewardship, by describing some citizen monitoring initiatives in Ontario, Canada. The authors begin by characterizing the changing nature of environmental governance by focusing specifically on the complexity surrounding the relationship between government administrative reform, demands for improved and increased environmental monitoring, and the role and function of citizens in monitoring activities. Then, building on the experience of one citizen-based environmental organization, Citizens' Environment Watch, as well as on two other local examples, they document possible new forms of collaboration that retain government responsibility while building community authority, knowledge, and power to improve local environmental quality. Suggested remedies include a recognition and public articulation of what government should do and what it does not do. In the case of the latter, government should commit to provide support for citizen monitoring efforts, and to heed the work of volunteer monitors. Finally, government needs to follow up on concerns about degraded environmental quality raised by local citizens through strong investigative and enforcement responses.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".