The Role of Trust in Sustainable Management of Land, Fish, and Wildlife Populations in the Arctic
Bibliographic record
Abstract
Sustainable resource management depends on support from the public and local stakeholders. Fish, wildlife, and land management in remote areas face the challenge of working across vast areas, often with limited resources, to monitor land use or the status of the fish-and-wildlife populations. Resource managers depend on local residents, often Indigenous, to gain information about environmental changes and harvest trends. Developing mutual trust is thus important for the transfer of knowledge and sustainable use of land resources. We interviewed residents of eight communities in Arctic Alaska and Canada and analyzed their trust in resource governance organizations using mixed-methods. Trust was much greater among Alaska (72%) and Nunavut (62%) residents than Churchill (23%). Trust was highest for organizations that dealt with fish and wildlife issues, had no legal enforcement rights, and were associated with Indigenous peoples. Local organizations were trusted more than non-local in Alaska and Nunavut, but the opposite was true in Churchill. Association tests and modeling indicated that characteristics of organizations were significantly related to trust, whereas education was among the few individual-level characteristics that mattered for trust. Familiarity, communication, and education are crucial to improve, maintain, or foster trust for more effective management of natural resources in such remote communities.
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".