Rural women vulnerability to human-wildlife conflicts: Lessons from villages near Mikumi National Park, Southeast Tanzania
Bibliographic record
Abstract
Human-wildlife conflicts (HWC) remain a serious conservation problem in Tanzania, particularly for rural communities near national parks. Despite this prevalence, research on rural women’s experiences with human-wildlife conflicts is limited. To address this research gap, this study examined the impacts of HWC on rural women from two villages neighboring Mikumi National Park (MNP) in Southeast Tanzania. A total of 20 adult female victims of human-wildlife conflicts (HWC) were purposely selected and interviewed to understand the impacts of human-wildlife conflicts in their lives. Findings indicate that loss of grassland and water within MNP borders exacerbated by climate change are pushing wild animals from MNP to seek food in nearby villages, causing frequent human-wildlife tensions. Crop damages, livestock killings, household food insecurity, and fears for physical safety were found to be significant impacts of HWC increasing rural women’s vulnerability to poverty. Despite these conservation threats, most interviewed HWC victims receive very little support from conservation authorities threatening the survival of wild animals from MNP. For peaceful co-existence, the study recommends empowering rural women with conservation training on HWC prevention and investment in the large-scale restoration of degraded lands and water sources to reduce competition over natural resources between humans and wildlife. Key words: Human-wildlife conflict, rural women, Mikumi National Park, Tanzania.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".