Contemporary Water Governance: Navigating Crisis Response and Institutional Constraints through Pragmatism
Bibliographic record
Abstract
Water has often been the source of crises and their frequency will intensify due to climate change impacts. The Niagara River Watershed provides an ideal case to study water crises as it is an international transboundary system (Canada-United States) and has both historical and current challenges associated with water quantity and quality, which resonates broadly in water basins throughout the world. The aim of this study was to understand how stakeholders perceive ecosystems and the relationship with preferences for governance approaches in the context of water governance. An online survey instrument was employed to assess perceptions of the system in terms of resilience (engineering, ecological, social-ecological, or epistemic), preferences for governance approaches (state, citizen, market, and hybrid forms), and the most pressing issues in the watershed. Responses showed that, despite demographic differences and adherence to different resilience perspectives, support was strongest for governance approaches that focused on state or state-citizen hybrid forms. The validity of the resilience typology as a grouping variable is discussed. The roles of institutional constraints, pragmatism in governance approach preferences, and the influence of multiple crises are explored in relation to the context of the study site, as well as to water governance scholarship more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".