Influences on trust during collaborative forest governance: a case study from Haida Gwaii
Bibliographic record
Abstract
Collaborative natural resource governance is increasingly relied upon to resolve conflicts, generate social and ecological benefits, and increase implementation of decisions. Trust is widely recognized as critical to successful collaborative natural resource governance; however, the multidimensional nature of trust has been underexplored in this context, and few studies specifically address collaborations involving Indigenous Peoples. Literature on collaborative governance involving Indigenous Peoples emphasizes issues of power-sharing, participation, and intercultural purpose and insights into how trust created with these considerations in mind have the potential to improve processes and outcomes. This paper used a case study of collaborative forest governance on Haida Gwaii, an archipelago located off the coast of British Columbia, Canada, to identify linkages between power-sharing and individual, interpersonal, and institutional influences on trust. Collaborative forest resource governance on Haida Gwaii formally began following signing of the Strategic Land Use Agreement (2007) and the Kunst’aa guu-Kunst’aayah Reconciliation Protocol (2009) and had led the Haida to achieve several of their goals for resource management. The research linked theoretical and empirical literature on collaborative governance and trust with empirical evidence gathered from 19 semi-structured interviews with current and former members of the Haida Gwaii Management Council and the Solutions Table and identified five individual influences, five interpersonal influences, and four institutional influences on trust.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".