A systematic review on the role of trust in the water governance literature
Bibliographic record
Abstract
Trust is generally considered to play a key enabling role in water governance. Despite this notion, there have been no systematic assessments examining the way in which the literature on water governance engages with ‘trust’. Our article fills this gap by providing an overview of the way in which this literature has engaged with trust as a conceptual lens, analytical device and empirical phenomenon. Through an explorative systematic literature review of N = 200, mainly peer-reviewed journal articles, our findings reveal that the knowledge base on the role of trust in water governance is fragmented, poorly conceptualized, and contextually dispersed. We also observe that the role of trust is often understudied, especially in the context of the global south and with regard to ethnic minorities and indigenous people as the subjects of trust. We recommend that future research should build on solid empirical evidence, diversify its foci, go beyond an instrumental approach to trust and rely on clear and transparent conceptualizations that acknowledge the context-specific and dynamic nature of trust relationships. The results of this review should serve to better systemize future research and to further the understanding on the role(s) of trust in varying contexts and related to different water governance issues.
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".