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Record W4417239784 · doi:10.1007/s13280-025-02317-3

Beyond kill or no-kill: Institutional analysis of lethal control decision-making in large carnivore management

2025· article· en· W4417239784 on OpenAlexaff
Nimisha Srivastava, John D. C. Linnell, Ramesh Krishnamurthy, Hannes J Koenig, Christine Fürst

Bibliographic record

VenueAMBIO · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia
FundersGerman Academic Exchange ServiceDeutscher Akademischer AustauschdienstNorges ForskningsrådEuropean CommissionHORIZON EUROPE Framework ProgrammeWorld Wildlife Fund
KeywordsInstitutional analysisCarnivoreAccountabilityLegislatureControl (management)DemocracyGovernment (linguistics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.245
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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