Large carnivores and zoos as catalysts for engaging the public in the protection of biodiversity
Bibliographic record
Abstract
Addressing the biodiversity crisis requires renewed collaborative approaches. Large carnivores are ambassador species, and as such they can aid the protection of a wide range of species, including evolutionarily distinct and threatened ones, while being popular for conservation marketing. However, conflicts between carnivores and people present a considerable challenge to biodiversity conservation. Our cross disciplinary essay brings together original research to discuss key issues in the conservation of large carnivores as keystone species for biodiversity rich, healthy ecosystems. Our findings suggest the need to promote coexistence through challenging ‘wilderness’ myths; to consider coexistence/conflict as a continuum; to include varied interest groups in decision making; to address fear through positive mediated experiences, and to explore further partnerships with zoos. As wide-reaching institutions visited by over 700 million people/year worldwide, zoos combine knowledge, emotion and social context creating ideal conditions for the development of care towards nature, pro-environmental behaviors and long-term connections between visitors and carnivores. Based on current research, we provide evidence that large carnivores and zoos are both powerful catalysts for public engagement with biodiversity conservation, recognizing barriers and suggesting future ways to collaborate to address biodiversity loss.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".