Challenges to integrating planning and policy-making with environmental assessment on a regional scale – a multi-institutional perspective
Bibliographic record
Abstract
Regional environmental assessment (EA) requires the participation of policy- and plan-making institutions to formulate, implement and monitor regional environmental management strategies. However, there is little understanding of what effective integration is in the context of regional EA and from the perspectives of planners and policy-makers involved. This paper seeks to explore how institutional actors perceive cross-domain integration vis-à-vis their own involvement in regional EAs. Thirty-eight participants from four regional EA initiatives in Canada shared their perspectives in an online survey. Three types of silo effects are identified: (1) institutional – intricately linked to factors such as coordination, goals and expectations, leadership and capacity; (2) disciplinary – characterized by limited communication and scepticism around data sharing; and (3) transactional – tendency of actors to emphasize individual narrow perspective rather than collective social and environmental outcomes. Additional findings reveal the importance of learning and multiple domain expertise as opportunities for enhancing cross-domain integration in regional EA practice. Finally, the study concludes that proactive consideration of potential silo effects is necessary for improved regional EA outcomes, and to facilitate more effective regional resource governance.
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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 | 0.089 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".