Understanding structure and character in rural water governance networks
Bibliographic record
Abstract
Governance has emerged as one of the key concerns amongst water experts focused on sustainability. Achieving sustainable states of water governance requires alignment of governance structures with water management objectives that are context specific. Two rural watershed planning processes in the province of British Columbia- the Similkameen Valley (Similkameen) Watershed planning network and the Kettle River (Kettle) Watershed planning network - were investigated using social network analysis (SNA) and social discourse network analysis (s-DNA) to map the socio-ecological relationships and analyze the discourse upon which water governance networks are being built. The resulting network structures and key actor characteristics revealed limited evidence for a transition towards collaborative and adaptive water governance models, which have been argued to be better suited in addressing key goals such as adapting to climate change impacts. Recommendations are made for improving water governance processes in rural regions to achieve effective implementation within the context of the new British Columbia Water Sustainability Act, 2014. SNA and s-DNA provide a means, through interdisciplinary research, to examine social network drivers and potential barriers to sustainable water governance development. Identifying network structures and measuring network characteristics gives resource managers the insight to intervene into evolving governance processes, to ensure proper alignment with contextually determined water sustainability goals. Results from this research will enable those involved in water governance design and implementation to make informed water resource decisions leading to effective, adaptive, and sustainable water 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".