Methodological Challenges in Studying Trust in Natural Resources Management
Bibliographic record
Abstract
Trust has been identified as a central characteristic of successful natural resource management (NRM), particularly in the context of implementing participatory approaches to stakeholder engagement. Trust is, however, a multi-dimensional and multi-level concept that is known to evolve recursively through time, challenging efforts to empirically measure its impact on collaboration in different NRM settings. In this communication we identify some of the challenges associated with conceptualizing and operationalizing trust in NRM field research, and pay particular attention to the inter-relationships between the concepts of trust, perceived risk and control due to their multi-dimensional and interacting roles in inter-organizational collaboration. The challenge of studying trust begins with its conceptualization, which impacts the terminology being used, thereby affecting the subsequent operationalization of trust in survey and interview measures, and the interpretation of these measures by engaged stakeholders. Building from this understanding, we highlight some of the key methodological considerations, including how trust is being conceptualized and how the associated measures are being developed, deployed, and validated in order to facilitate cross-context and cross-level comparisons. Until these key methodological issues are overcome, the nuanced roles of trust in NRM will remain unclear.
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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 | 0.211 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".