Conservation grounded in traditional ecological knowledge, culture and hunting
Bibliographic record
Abstract
Abstract For millennia, traditional hunting practices have provided a means of subsistence for numerous indigenous peoples worldwide. Not only do managed traditional hunting practices perpetuate Traditional Ecological Knowledge (TEK), but they also promote the sustainable utilization of natural resources. However, the contribution of ethnic minority traditional hunting cultures to the conservation of bird diversity remains understudied in China. The Yao people employ a unique ‘bird‐basin’ hunting practice during the migratory season in the Dayao mountains. In order to assess the impact of forest utilization practices on bird diversity, this study compares the bird diversity in the hunting grounds of the Yao people with that in old‐growth forests and in firewood forests. We found a significantly higher level of bird species richness in the hunting grounds than in the old‐growth forests and the firewood forests. Functional diversity and phylogenetic diversity were similar in the old‐growth forests and hunting grounds and were lowest in the firewood forests. Most of the birds captured by bird‐basins were migratory species that were small in size and not threatened . ‘Bird‐basin’ hunting could be an effective way of resolving conflicts between bird diversity conservation and regional economic development. Unlike firewood forests, forests within hunting grounds are proactively and effectively managed by the Yao people and require no external funding. We recommend considering traditional ecological knowledge (TEK) and managed hunting strategies when managing forests outside protected areas, particularly in areas inhabited by indigenous peoples. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".