Coexisting With Different Human-Wildlife Coexistence Perspectives
Bibliographic record
Abstract
Over the last decade, there has been a remarkable increase in scientific literature addressing human–wildlife interactions (HWI) and associated concepts, such as coexistence, tolerance, and acceptance. Despite increased attention, these terms are rarely defined or consistently applied across publications. Indeed, the meaning of these concepts, especially coexistence, is frequently assumed and left for the reader to interpret, making it hard to compare studies, test metrics, and build upon previous HWI research. To work toward a better understanding of these terms, we conducted two World Café sessions at international conferences in Namibia, Africa and Ontario, Canada. Here, we present the array of perspectives revealed in the workshops and build upon these results to describe the meaning of coexistence as currently applied by conservation scientists and practitioners. Although we focus on coexistence, it is imperative to understand the term in relation to tolerance and acceptance, as in many cases these latter terms are used to express, measure, or define coexistence. Drawing on these findings, we discuss whether a common definition of these terms is possible and how the conservation field might move toward clarifying and operationalizing the concept of human-wildlife coexistence.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".