Beyond Human-Wildlife Conflicts. Ameliorating Human/Nonhuman Animal Relationships through Workshops on Terminology
Bibliographic record
Abstract
Human-Wildlife Conflicts (HWCs) occur when nonhuman animals’ needs clash with those of humans. One recent effort regards shifting HWCs into Human-Human Social Conflicts, where conflicts are about humans disagreeing on how to deal with nonhuman animals. This method can help reduce guilt placed on nonhuman animals, but also robs them of their agency. Conversely, some in the field of biology seek to increase animal agency and their moral status, even making them key stakeholders. A helpful relationship may seek both aspects. Fourteen workshops (147 participants, 40 subgroups), with relevant stakeholders, were run on this topic. Participants were involved in biology and/or environmentalism and/or sustainability. They sought to develop terminology diminishing guilt in HWCs, while maintaining agency. Common themes were then brought out. Eight subgroups argued for more inclusive terms, like “sentient beings” and 21 argued for diminishing human/nature dichotomies. Both fit well with increasing agency, and giving nonhumans greater moral status, by narrowing human/nonhuman animal gaps. Participants also discussed nonhuman animals as “icons”, which 26/30 subgroups saw as, at least potentially, problematic, arguing it conceptually “freezes” species, ignoring their dynamism. In sum, the workshops aid in framing healthier relationships with the natural world.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".