From “trust” to “trustworthiness”: Retheorizing dynamics of trust, distrust, and water security in North America
Bibliographic record
Abstract
Assumptions of trust in water systems are widespread in higher-income countries, often linked to expectations of "modern water." The current literature on water and trust also tends to reinforce a technoscientific approach, emphasizing the importance of aligning water user perceptions with expert assessments. Although such approaches can be useful to document instances of distrust, they often fail to explain why patterns differ over time, and across contexts and populations. Addressing these shortcomings, we offer a relational approach focused on the trustworthiness of hydro-social systems to contextualize water-trust dynamics in relation to broader practices and contexts. In doing so, we investigate three high-profile water crises in North America where examples of distrust are prevalent: Flint, Michigan; Kashechewan First Nation; and the Navajo Nation. Through our theoretical and empirical examination, we offer insights on these dynamics and find that distrust may at times be a warranted and understandable response to experiences of water insecurity and injustice. We examine the interconnected experiences of marginality and inequity, ontological and epistemological injustice, unequal governance and politics, and histories of water insecurity and harm as potential contributors to untrustworthiness in hydro-social systems. We close with recommendations for future directions to better understand water-trust dynamics and address water insecurity.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".