Bibliographic record
Abstract
Coexistence of people and wildlife outside protected areas is of critical conservation importance. However, human-wildlife interactions on shared landscapes can produce negative outcomes for wildlife populations and people. This article focuses on the effects of wildlife on local people's lived experiences of physical safety in the Tarangire ecosystem of northern Tanzania. The Tarangire ecosystem supports a diverse array of wildlife species of global conservation significance, encompassing several national parks, community-based conservation areas, forest reserves, and trophy hunting blocks. From the perspectives of local agropastoral Maasai communities, coexisting with wildlife is a routine part of everyday life, though some species are dangerous and pose threats to physical safety. These human security concerns compound the economic impacts of wildlife on local livelihoods, manifest in the forms of crop raiding, livestock depredation, and property damage. Based on mixed qualitative methods including ethnographic fieldwork (2019–2020; 2022; 2023), participant observation, household surveys (n = 1076), and in-depth interviews (n = 240), this paper identifies the species of particular concern to communities. Elephants, spotted hyenas, buffalo, and lions pose significant threats to human security. Venomous snakes and leopards are also safety concerns, but to a lesser degree. The anthropological dimensions of these threats to physical safety are underrepresented in the literature on human-wildlife conflict. This paper spotlights three recent incidents of people being killed by wildlife (elephant, hyena, and lion) in the area, and the psychosocial consequences that have since rippled across local communities. People expressed feelings of fear, resentment, anger, grief, and insecurity born of their experiences coexisting with large nondomestic mammals. Wildlife attacks on people engender material and emotional impacts with traumatic aftereffects. These human dimensions of wildlife are significant for equity reasons in and of themselves, and also for environmental sustainability as they affect people's tolerance for living with wildlife. Greater attention to the lived experiences of local people is needed to improve conservation practice in northern 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".