Trust ecology and the resilience of natural resource management institutions
Bibliographic record
Abstract
The resilience of natural resource management (NRM) institutions are largely contingent on the capacities of the people and organizations within those institutions to learn, innovate, and adapt, both individually and collectively. These capacities may be powerfully constrained or catalyzed by the nature of the relationships between the various entities involved. Trust, in particular, has been identified repeatedly as a key component of institutional relationships that supports adaptive governance and successful NRM outcomes. We apply an ecological lens to a pre-existing framework to examine how different types of trust may interact to drive institutional resilience in NRM contexts. We present the broad contours of what we term "trust ecology," describing a conceptual framework in which higher degrees of diversity of trust, as conceptualized through richness and evenness of four types of trust (dispositional, rational, affinitive, and systems based), enhance both the efficacy and resilience of NRM institutions. We describe the usefulness and some limitations of this framework based on several case studies from our own research and discuss the framework's implications for both future research and designing more resilient governance arrangements.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".