Unveiling Social Dynamics in People's Perception of Raptors to Guide Effective Conservation Strategies
Bibliographic record
Abstract
Ethnoscientific approaches offer valuable tools for exploring human–nature relationships, making them useful for developing effective conservation strategies. Raptors, which are birds of prey, face significant conservation threats worldwide, with human persecution being a leading factor in their population decline. Urgent conservation strategies are needed, particularly in regions of high raptor diversity like the tropical Andes. In this study, we employed semistructured questionnaires and logistic models to investigate how demographic factors, economic activity, and traditional knowledge shape people's perceptions of raptors in rural communities of the Ecuadorian Andes. These communities have historically experienced poverty and inequality, and our approach takes into account their local realities to provide conservation recommendations. Our findings reveal that traditional ecological knowledge provides a broad understanding of human–raptor relations, and that raptors are viewed as both providers of ecosystem services and disservices. Additionally, social demographics, such as gender and educational level, can influence people's perception of raptors. Based on these results, we can promote conservation actions from a local to global level. Ethnoecological approaches offer diverse conservation opportunities that can vary based on different local contexts. In addition to conventional measures such as environmental education programs, poultry management, and landscape preservation, it is essential to consider the political ecology of specific sites, particularly in regions of the Global South where poverty and inequality are closely intertwined with social and environmental injustices. As such, policy making to alleviate poverty and inequality in rural communities in Ecuador and other Andean countries; and science decolonization to make conservation more inclusive are crucial for human well-being and successful and lasting conservation actions for raptors and biodiversity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".