Breaking Through Disciplinary Barriers: Human–Wildlife Interactions and Multispecies Ethnography
Bibliographic record
Abstract
One of the main challenges when integrating biological and social perspectives in primatology is overcoming interdisciplinary barriers. Unfamiliarity with subject-specific theory and language, distinct disciplinary-bound approaches to research, and academic boundaries aimed at “preserving the integrity” of subject disciplines can hinder developments in interdisciplinary research. With growing interest in how humans and other primates share landscapes, and recognition of the importance of combining biological and social information to do this effectively, the disparate use of terminology is becoming more evident. To tackle this problem, we dissect the meaning of what the biological sciences term studies in “human–wildlife conflict” or more recently “human–wildlife interactions” and compare it to what anthropology terms “multispecies ethnography.” In the biological sciences, human–wildlife interactions are the actions resulting from people and wild animals sharing landscapes and resources, with outcomes ranging from being beneficial or harmful to one or both species. In the social sciences, human–nonhuman relationships have been explored on a philosophical, analytical, and empirical level. Building on previous work, we advocate viewing landscapes through an interdisciplinary “multispecies lens” in which humans are observed as one of multiple organisms that interact with other species to shape and create environments. To illustrate these interconnections we use the case study of coexistence between people of the Nalu ethnic group and Critically Endangered western chimpanzees (Pan troglodytes verus) at Cantanhez National Park in Guinea-Bissau, to demonstrate how biological and social research approaches can be complementary and can inform conservation initiatives at the human–primate interface. Finally, we discuss how combining perspectives from ethnoprimatology with those from multispecies ethnography can advance the study of ethnoprimatology to aid productive discourse and enhance future interdisciplinary research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| 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".