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Record W4293215802 · doi:10.1111/cobi.13981

Indigenous insights on human–wildlife coexistence in southern India

2022· article· en· W4293215802 on OpenAlexaff
Helina Jolly, Terre Satterfield, Milind Kandlikar, Suma TR

Bibliographic record

VenueConservation Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia
FundersNational Geographic Society
KeywordsWildlifeIndigenousWildlife managementGeographyWildlife conservationBushmeatHuman–wildlife conflictEnvironmental ethicsTraditional knowledgeEcologyBiology

Abstract

fetched live from OpenAlex

As human-wildlife conflicts escalate worldwide, concepts such as tolerance and acceptance of wildlife are becoming increasingly important. Yet, contemporary conservation studies indicate a limited understanding of positive human-wildlife interactions, leading to potentially inaccurate representations of human-animal encounters. Failure to address these limitations contributes to the design and implementation of poor wildlife and landscape management plans and the dismissal of Indigenous ecological knowledge. We examined Indigenous perspectives on human-wildlife coexistence in India by drawing ethnographic evidence from Kattunayakans, a forest-dwelling Adivasi community living in the Wayanad Wildlife Sanctuary in Kerala. Through qualitative field study that involved interviews and transect walks inside the forests, we found that Kattunayakans displayed tolerance and acceptance of wild animals characterized as forms of deep coexistence that involves three central ideas: wild animals as rational conversing beings; wild animals as gods, teachers, and equals; and wild animals as relatives with shared origins practicing dharmam. We argue that understanding these adequately will support efforts to bring Kattunayakan perspectives into the management of India's forests and contribute to the resolution of the human-wildlife conflict more broadly.

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 score0.024
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.030
GPT teacher head0.259
Teacher spread0.229 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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