Positive social relationships in hunting groups are related to compliance with the higher-level moose management
Bibliographic record
Abstract
Managing shared natural resources, such as moose (Alces alces) in Finland, is often challenging due to the involvement of multiple stakeholders with opposing views and the need for coordination across several spatial levels. A sustainable moose population is maintained through a carefully planned, multi-level system of adaptive management. However, ensuring that these plans are followed requires substantial support from the lowest level—the hunters. We investigated the decision-making and joint action of moose hunting groups, and how these are related to compliance with hunting recommendations. We conducted a country-wide questionnaire study with a sample of 4729 hunters in Finland. We applied the multidisciplinary social-ecological systems framework—rooted in systems thinking—alongside insights from evolutionary theory on cooperation. Our results showed that hunters who positively assessed social interactions and decision-making within their hunting group were more likely to be satisfied with and compliant toward natural resource management. To achieve long-term sustainability, we suggest that harvest regulations and recommendations should be accompanied by attention to the decision-making and group dynamics of those carrying out the harvest. We found that processes such as trust and frequent meetings that promoted social capital and communication within hunting groups, between groups, and between hunters and the national management level were crucial for sustainable local moose management. A balance between member commitment to the group and the regular acceptance of new members had a positive influence. Our results highlight that deeper understanding of local social dynamics can facilitate regional and national management of shared resources.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".