Bibliographic record
Abstract
Resilience is increasingly applied the context of water systems, and water governance more broadly, in response to climate change impacts, hydrologic variability and uncertainty associated with various dimensions of global environmental change. However, the meanings, applications and implications of resilience as it relates to water governance are still poorly understood. Drawing on a systematic scoping review of the peer‐reviewed academic literature, this paper addresses the questions: how is resilience framed in relation to water systems and water governance, how are diverse resilience framings (re)shaping ideas and trends in water management, and what are the associated implications? The analysis found that the resilience‐informed water governance literature remains fragmented and predominantly centered on conventional approaches and framings of water planning, with a predominant focus on engineering resilience in water supply infrastructure. A recently emerging engagement with resilience in the water governance literature, however, draws on more diverse framings and theories and calls for a shift towards more integrative and ecologically‐centered thinking in water governance. Despite this, significant empirical and conceptual gaps remain, particularly around the integration of the various subsectors of water governance and, more importantly, around the institutional and governance dimensions of building water resilience. This article is categorized under: Engineering Water > Planning Water Human Water > Water Governance
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 | 0.047 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.006 | 0.071 |
| Scholarly communication | 0.020 | 0.052 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".