Water Policy And Governance Networks: A Pathway To Enhance Resilience Toward Climate Change
Bibliographic record
Abstract
Abstract Natural resources governance is key to enhancing resilience toward climate change and strengthening socioecological systems in light of future uncertainties. Overlapping jurisdictions and lack of clarity in the lines of authority reduce the efficiency of environmental policies and governance, jeopardizing the conservation and sustainable use of resources. With the forecast of longer droughts, extreme precipitation patterns, faster runoff, and slower water table recharge over the coming years, water governance becomes an impellent issue. To understand the risks posed by water scarcity and water regulations, a case study was conducted of Oklahoma state‐level water policies and governance. A content analysis of water policies and a network analysis of water governance was used to determine how Oklahoma experiences features of fragmented and adaptive governance within its natural resource governance structure. Data analysis reveals that Oklahoma water governance experiences multiple forms of fragmentation while also showing features of an adaptive network. Such adaptive features make Oklahoma's water governance network more resilient than forecasted. Identifying gaps and understanding how a governance system experiences fragmentation can help policy makers develop strategies to enhance the adaptive features of water governance, thus preparing for risk and disasters related to water scarcity and climate variability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".