Stories of coexistence: A narrative inquiry of leopard attacks on people
Bibliographic record
Abstract
Abstract Pursuing human–wildlife coexistence is particularly challenging for carnivore species as conflict can negatively impact human livelihood and well‐being. Understanding the social context of how communities perceive conflicts with carnivores, often witnessed in their folklore and stories, can shed light on pathways for fostering coexistence. In the current study, we explored the stories participants shared about leopard ( Panthera pardus ) attacks on people in India, Himachal Pradesh (HP), where 356 people have been attacked by leopards in 11 years (2004–2015). We analysed 89 stories of attacks by leopards in HP using narrative analysis, a novel approach to analysing accounts of carnivore conflicts, to reveal the elements described by the storytellers, including point of view (who the storyteller is), the actors, the setting (location, time, season), the conflict (leopard, human actions during encounter) and the resolution (approach authority, behaviour change, medical aftermath). This analysis revealed that in HP, the coexistence has shifted from ‘tolerant synantropy’ (people tolerate presence of synantropic species) archetype of coexistence historically to ‘reciprocal damages’ (frequent encounters prompt strong responses that perpetuate negative impacts on both parties) archetype of non‐coexistence in the past decade. This shift underscores the need for immediate intervention to prevent the slide into identity‐based conflicts. Synthesis and application : The interventions suggested are research‐informed awareness campaigns and the development of protection tools for people encountering leopards. Based on experiential descriptions of the reactions to attacks, we found that the use of common tools succeeded in fending off leopards and reducing injury severity. Further, we suggest applications for this underutilised framework of narrative inquiry in human–wildlife conflicts, to formulate conservation interventions that are culturally, ethically and practically site‐appropriate. This method centres the often‐marginalised voices of wildlife victims, providing a pathway for the administration to proactively address human safety and recognise possible resilience strategies of people coexisting with wildlife. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".