Well-Being Impacts of Human-Elephant Conflict in Khumaga, Botswana: Exploring Visible and Hidden Dimensions
Bibliographic record
Abstract
High densities of wild African savannah elephants ( Loxodonta africana ) combined with widespread human land-use have increased human-elephant conflict in northern Botswana. Visible impacts (e.g. crop/property damage, injury/fatality) of elephants on human well-being are well documented in scholarly literature while hidden impacts (e.g. emotional stress, restricted mobility) are less so. This research uses qualitative methods to explore human experiences with elephants and perceived impacts of elephants on human well-being. Findings reveal participants are concerned about food insecurity and associated visible impacts of elephant crop raids. Findings also reveal participants are concerned about reduced safety and restricted mobility as hidden impacts threatening livelihoods and everyday life. Both visible and hidden impacts of elephants contribute to people's negative feelings towards elephants, as does the broader political context. This research emphasises the importance of investigating both visible and hidden impacts of elephants on human well-being to foster holistic understanding of human-elephant conflict scenarios and to inform future mitigation strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".