Institutional arrangements for assessing and managing cumulative effects on watersheds: Lessons from the Grand River watershed, Ontario, Canada
Bibliographic record
Abstract
Assessing and managing cumulative effects on watersheds involves numerous agencies, regulatory frameworks and jurisdictions, and necessitates the co-creation of new, or innovations in existing, institutional arrangements. This paper examines the institutional arrangements needed to implement and sustain cumulative effects assessment and management (CEAM) for watersheds. The study is based on semi-structured interviews with 29 key informants in the Grand River watershed, Canada, including members from academia, government agencies, consulting firms, non-governmental organizations, and First Nations, with knowledge of, and direct experience in, watershed planning, assessment and monitoring. The research explored governance conditions in the watershed based on a framework of CEAM requisites developed from other watersheds where institutional arrangements have been investigated. Results indicate the need for improved institutional arrangements in the Grand River watershed to support the development of watershed CEAM, namely: a combined law–policy approach to implement CEAM, yet ensuring sensitivity to watershed context; a strong mandate for CEAM leadership and the capacity to coordinate CEAM initiatives; and tiering of CEAM planning, monitoring and assessment initiatives as a means to strengthen nested governance structures.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".