Understanding human-elephant interactions across time is key to illuminate pathways toward coexistence
Bibliographic record
Abstract
Research on human-elephant interactions (HEI) seeks to better understand relationships between people and elephants with the goal of reducing unwanted interactions for the long-term survival of elephants in social-ecological systems. Many examinations of HEI often rely on a short temporal scale of several seasons to several years, often because of limited data availability across time. These examinations offer limited understanding of processes that influence HEI and mutual adaptations of people and elephants. In this synthesis, I present an ethnographic case study from the Okavango Delta, Botswana, where human and elephant populations have increased in the past 20 years. I use bricolage, a practice of using available materials at hand, to weave together diverse historical and current scholarship and primary data to understand dynamics of HEI and coadaptation across three different periods (pre-colonial, colonial, and post-independence). I show that people and elephants were coadapted in the pre-colonial period when people were highly mobile and hunted elephants with rudimentary technologies in ways that supported human development across southern Africa with minimal impact on elephants. European colonization brought sweeping changes, including through the introduction of guns and the development of the ivory trade that led to massive declines in elephant populations. Development policies that were magnified in the years following independence, including the establishment of land policies that settled communities, additionally disrupted the formally fluid nature of HEI. Simultaneously, wildlife conservation policies that coincided with dramatic increases in elephant populations shape how people perceive HEI and elephants as a predominant environmental force today. I argue that the incorporation of wider historical contexts, where necessary through the practice of bricolage, reveals coadaptation across time and offers understanding of possibilities of coexistence where people and elephants thrive alongside each other.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".