How state-reinforced knowledge infrastructure influences adaptive urban water governance
Bibliographic record
Abstract
Resilience and environmental governance scholars have long studied and debated the role of the state in driving or coordinating responses to the varied dimensions of adaptive governance. In this study, we empirically analyze how multilevel, state-reinforced institutional designs impact the adaptive governance of urban water systems by structuring information production and use. Specifically, we analyze the multilevel institutional designs of “knowledge infrastructure systems,” defined as the rules and capacities within a system that allow actors to “produce, curate and communicate” information for governance. Drawing on a novel compilation of hydroclimatic data, media content, interviews, planning documents, and institutional designs, we empirically examine a typology of multi-level institutional arrangements in four U.S. urban water systems. Drawing from scholarship that considers the reflexivity of legal avenues and system performance, we conclude that state-reinforced rules governing the production and use of knowledge can clarify capacity-needs and support the efforts of managers responding to climate stressors through adaptive governance processes. They do so by formalizing planning types, timelines, and sanctions for noncompliance, while allowing local users and providers flexibility to innovate within these processes.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".