Towards an Environmental Governance Agenda in Regional Environmental Assessment: A Case Study of the Crown Managers Partnership
Bibliographic record
Abstract
In the last decade, the emphasis of regional environmental assessment (EA) has shifted away from simply project approval towards facilitating environmental governance by accommodating heterogeneous stakeholders and emphasising relationship building across diverse institutions. However, there are very few advanced regional EA cases that may be studied to understand how practice has evolved and the implications for regional environmental governance. This paper characterises and assesses the interactions among the members of the Crown of the Continent Managers Partnership (CMP), whereby individuals with planning, policy-making, and EA roles attempted to implement an adaptive approach to regional cumulative effects assessment. Twelve in-depth, semi-structured interviews with key stakeholders provide data used in the investigation. The analysis demonstrates opportunities for an approach to regional EA that facilitates environmental governance through collective visioning, innovative leadership, learning from failure, and collaborative science and management. Lessons from the CMP are relevant internationally to jurisdictions seeking to implement regional EA via multi-disciplinary, multi-jurisdictional partnerships.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".