Knowledge engagement in collaborative water governance: A New Brunswick example
Bibliographic record
Abstract
Authoritative, top-down forms of environmental governance are presently giving way to more collaborative approaches in which decision making is an ongoing negotiation between government and non-government actors. There is growing consensus that critical environmental concerns—such as contamination of drinking water—relate as much to political, economic and social issues, as to technical and scientific issues. As the trend toward collaborative environmental governance continues, and as science-based knowledge increasingly shares a role in decision-making processes with more “local”, non-scientific knowledge, questions arise concerning how diverse knowledge contributions are understood and engaged in these governance processes. This research explored the relationships between knowledge and collaborative environmental governance processes. The purpose of the research was to identify (1) types of knowledge that individual actors bring into collaborative governance pertaining to water resource protection, (2) uses of that knowledge, and (3) features of collaborative processes that affect the engagement of actor knowledge. Collaborative water governance in New Brunswick provided the context for the research. Most actors did not see a definitive distinction between “expert”, scientific and “local”, non-scientific knowledge; they considered both to be important contributions. Nonetheless, science-based knowledge, especially natural science, was found to be a predominant knowledge type among actors involved in collaborative water governance. Science-based, expert knowledge was more readily used than local knowledge types in the various stages of collaborative governance. Leadership and the definition of actor roles were considered paramount for engaging a wide range of knowledge types in collaborative governance processes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".