Applying conservation social science to study the human dimensions of Neotropical bird conservation
Bibliographic record
Abstract
Abstract As the global human population increases, and many bird populations in the Neotropics and the rest of the world continue to decline, the study of the intersection of humans, birds, and conservation has become more relevant than ever. The field of conservation social science is an interdisciplinary field that applies the social sciences and humanities to examine research questions that have implications for biodiversity conservation, and encompasses disciplines as diverse as psychology, economics, and political ecology. An understanding of the human dimensions of biodiversity conservation issues can be an essential element in the success or failure of a conservation initiative, policy, or practice. The purpose of this article is to provide an understanding of the growing body of conservation social science relevant to Neotropical bird conservation research and to demonstrate its importance. We discuss how this research can contribute to addressing 5 major threats to bird conservation in the Neotropics, including future research needs, and we provide 3 case studies of bird conservation social science projects, demonstrating the insights that can be gained. We close with a discussion of how conservation biologists and ornithologists can most effectively work with conservation social scientists.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".