Relating social networks, ecological health, and reservoir basin governance
Bibliographic record
Abstract
Abstract The Columbia River Basin is a complex social‐ecological system, spanning political, legal, socio‐economic, geographic, and biophysical boundaries. Outreach to others in social networks develops fundamental communications needed for sustainable collaborations in adaptive management. However, operationalizing and comparing measures of social processes and outcome success in biophysical indicators remains challenging for resource managers. Using survey‐based research, we examined the interactions for water resource governance of five Columbia River reservoir basins in the northwestern US and Canada: Lakes Chelan, Roosevelt, Pend Oreille, Koocanusa, and člq'etkw (Flathead). Respondents included: water resource professionals working for Tribes/First Nations, federal, state, or provincial departments in water quality and/or fisheries, and people who engage in the networks on behalf of area businesses, government offices, public services, non‐profit organizations, and other entities. Perceived social process metrics in these governance networks included the levels of collaboration and inclusiveness, common goals, common strategies, identifying issues, implementing action, and the adequacy of available scientific data. We evaluated social measures relative to participant‐reported changes in physical lake health indicators. Qualitative data‐enhanced understanding of basin‐specific differences. Correlations of social by ecological measures varied widely between basins. Even moderate to strong functionality parameters did not scale well from individual to cross‐basin levels, as many correlations vanished with data aggregated. However, data analysis at the basin scale revealed important variability across the region in scope and governance functionality. Process indicators such as identifying issues and implementing action yielded stronger relationships for 10‐year horizons than for 2 years, reflecting the lag‐time in resource action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".