Examining the emerging environmental protection policy convergence in the Ontario municipal drinking water, wastewater and stormwater sectors
Bibliographic record
Abstract
This study examines the governance approaches applying to Ontario's municipal water management activities and observes an environmental policy convergence occurring in two different dimensions: across the drinking water, wastewater, and stormwater aspects of municipal water activities with respect to governance approaches, and federal, provincial, and municipal governments in terms of drawing on private management system standards to supplement conventional regulatory requirements. This study supports the proposition that municipal water governance approaches are developed within a context that includes both state-based requirements and non-state market-oriented standards such as ISO 9001 and ISO 14001, and this context facilitates convergence and calibration between and among state-based and private governance at the public policy level adopted by municipalities. In addition to increasing use of private environmental management systems (EMSs) by Ontario municipalities as methods of addressing operational challenges they face, Canadian courts are also referencing EMS in their decisions. This article suggests that EMS standards such as ISO 14001 can be useful supplements to state regulations, and this supplementing would not be characterized as supplanting or substituting conventional state-based regulation, but rather as a form of practical and conceptual ‘bridge’ between public and private forms of regulation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".