Exploring Collaborative Environmental Governance: Perspectives on Bridging and Actor Agency
Bibliographic record
Abstract
Abstract This article explores the prospect and practice of alternative and more collaborative approaches to environmental governance, focusing on recent North American experience and with particular attention to: (i) characteristic barriers to effective and collaborative environmental governance; (ii) approaches to collaboration and bridging between actors and sectors; and (iii) the potential role of actor agency. The focus of inquiry is primarily on recent North American experience. The key literature on environmental governance is discussed, and interviews conducted with environmental practitioners, academics, government officials and community leaders to explore and analyse their experiences with environmental governance are analysed. Collectively, these experiences suggest that there is a need for more skilful bridging of actors and initiatives, and a greater role for governments in facilitating collaborative approaches. The interviews also point to a number of challenges that must be overcome before the full potential of collaborative environmental governance can be realized. Although some researchers advocate the need for unified and overarching approaches to environmental governance, there is considerable evidence indicating that efforts are better directed towards building networks and collaboration among a wide diversity of actors, philosophies and approaches.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| 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".