Trust ecology and the resilience of natural resource management institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 it