Conceptualizing trust and distrust as alternative stable states: lessons from the Flint Water Crisis
Bibliographic record
Abstract
Despite the universally recognized importance of fostering trust and avoiding distrust in governance relationships, there remains considerable debate on core questions like the relation between (dis)trust and the evaluations of the characteristics that make a governance agent appear (un)worthy of trust. In particular, it remains unclear whether levels of (dis)trust simply follow levels of (dis)trustworthiness-such that building trust is primarily a question of increasing evidence of trustworthiness and avoiding evidence of distrustworthiness, or if their dynamics are more complicated. The current paper adds novel theory for thinking about the management of trust and distrust in the governance context through the application of principles borrowed from resilience theory. Specifically, we argue that trust and distrust exist as distinct, self-reinforcing (i.e., stable) states separated by a threshold. We then theorize as to the nature of the self-reinforcing processes and use qualitative data collected from and inductively coded in collaboration with Flint residents as part of a participatory process to look for evidence of our argument in a well-documented governance failure. We conclude by explaining how this novel perspective allows for clearer insight into the experience of this and other communities and speculate as to how it may help to better position governance actors to respond to future crises.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".