Indigenous insights on human–wildlife coexistence in southern India
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".