Nature, Causes, and Impact of Human–Wildlife Interactions on Women and Children Across Cultures
Bibliographic record
Abstract
Despite the growing human–wildlife interactions (HWIs) globally, little attention has been paid to their effects on women and children, who often bear the brunt of loss of property and livelihoods. A systematic scoping review of four databases was undertaken to map and synthesize English-language evidence on the nature, causes, and impact of human–wildlife interactions on women and children across cultures. The 42 studies retained reveal that the proximity of human habitation to forest areas; expansion, deforestation, and encroachment of animal space; humans’ dependence on forest resources for livelihood; displacement of carnivores; and animals coming into the human space in search for food are the predominant causes of HWIs. Various types of HWIs and widely varying frequencies and durations of HWIs were reported. Individual and collective aspects of physical, psychological, economic, social, and environmental impacts on women and children were identified. The themes extracted were gendered roles, multi-factor vulnerabilities of women, religious beliefs, low participation of women in decision-making, social superstition against tiger widows, and perceptions of coexistence. Attention to perceptions of HWIs in different cultures and societies was limited, with notable gaps in the coverage of women and children and important geographic areas. These findings stress the need to bridge the geographical and cultural gap through multi-disciplinary actions on the determinants and effects of HWIs on women and children.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".