Beyond kill or no-kill: Institutional analysis of lethal control decision-making in large carnivore management
Bibliographic record
Abstract
Countries use lethal control as a tool to respond to human-large carnivore conflicts to varying degrees. The aim of this study was to explore the complexities surrounding the often-controversial decision to lethally manage carnivores. We examined the cases of the tiger (Panthera tigris) in India and the wolf (Canis lupus) in Germany. This study used an Institutional Analysis and Development framework to analyze contrasting sociopolitical processes. Through a review of legislative documents (n = 44) and interviews with experts (n = 47), the study examined the intricacies and challenges of the decision-making process and its implementation. While both countries were restrictive in their use of lethal control, decisions were primarily shaped by culturally embedded tolerance thresholds, accountability structures of decision-makers and influential societal factors. The findings demonstrate that effective carnivore management requires careful institutional design balancing scientific evidence with democratic participation.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".