Small systems, big challenges: review of small drinking water system governance
Bibliographic record
Abstract
Small drinking water systems (SDWS) are widely identified as presenting particular challenges for drinking water management and governance in industrialised nations because of their small customer base, geographic isolation, and limited human and financial capacity. Consequently, an increasing number and range of scholars have examined SDWS over the last 30 years. Much of this work has been technocentric in nature, focused on SDWS technologies and operations, with limited attention to how these systems are managed, governed, and situated within broader social and political–economic contexts. This review seeks to provide a comprehensive overview of the governance dimensions of SDWS by drawing together existing literature relating to SDWS governance and exploring its key themes, research foci, and emerging directions. This overview is intended to provide guidance to scholars and practitioners interested in specific aspects of SDWS governance and a baseline against which researchers can position future work. The review identified 117 academic articles published in English-language journals between 1990 and 2016 that referred to some aspect of drinking water governance in small, rural, and Indigenous communities in industrialised nations. The articles’ content and bibliographic information were analysed to identify the locations, methods, journals, and themes included in research on SDWS governance. Further analysis of SDWS’ governance dimensions is organised around four questions identified as central to SDWS research: what governance challenges are experienced by SDWS, and what are their causes, solutions, and effects? Overall, the review revealed that the SDWS governance literature is piecemeal and fragmented, with few attempts to theorise SDWS governance or to engage in interdisciplinary, cross-jurisdictional conversations. The majority of articles examine North American SDWS, retain a technocratic orientation to drinking water governance, and are published in technical or industry journals. Such research tends to focus on the governance challenges SDWS face and proposed solutions to systems’ performance, capacity, and regulatory challenges. A small but growing number of studies examine the causal factors underpinning these governance challenges and their socio-spatially differentiated impacts on communities. Looking forward, the review argues for a more holistic, integrative approach to research on SDWS governance, building on a water governance framework.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 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".