Urban Wildlife Organizations and the Institutional Entanglements of Conservation’s Urban Turn
Bibliographic record
Abstract
Abstract Urban wildlife organizations—which include groups focused on wildlife rehabilitation, rescue, removal, advocacy, education, and conflict resolution—have typically been viewed as out of step with the goals of wildlife conservation because of their focus on encounters with individual nonhuman animals, common species, and degraded habitats. The recent shift by large conservationNGOs toward a “humans and nature together” framework, because of its focus on urban natures, has brought the field into discursive relation with urban wildlife organizations. Drawing on a case study of four wildlife organizations in an urban center, this research explores their discourse about human-wildlife relationships in the city, and the challenges and opportunities presented by their emergent intersections.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".