Multi-Species Cities for the Anthropocene: Narrativizing Human-Wildlife Relations in an Urban Organizational Niche
Bibliographic record
Abstract
Amidst academic debates about how wildlife conservation should adapt in a postnatural world, big conservation NGOs have shown an increasing interest in cities as the kind of humanized landscapes that may define conservation for the Anthropocene. This research explores the ways that their emerging focus on urban natures represents a potential friction point with organizations who navigate the urban/wild relationship at close range through direct interventions with everyday human-wildlife encounters. I look at the work of four organizations involved in narrativizing ethical relations with wildlife in a large Canadian city: three urban wildlife organizations (UWOs) defined by their on-the-ground responses to encounters with wildlife and their involvement in urban coexistence education and, comparatively, a branch of an international conservation organization located in the same city. Through a series of staff and volunteer interviews and a qualitative analysis of organizational grey literature, I consider the evolution of an urban wildlife field, the organizations different engagements with affective wildlife encounters, and the way ideas of nature and postnature are mobilized in their practice and discourse about human-wildlife relations. I find that 1. Urban wildlife organizations are under-recognized as part of the institutional infrastructure of cities and their practice is characterized by struggles over funding and identity; 2. The big conservation organizations engagement with the city as a site of connection to nature evades the costs and complications of affective encounters that shape UWO practice ; and 3. The communications of the big conservation organization reflect in some ways the new human-centred conservation, posing explicit challenge to the fields historical attachment to a human/nature divide. The UWOs in this study, in contrast, remained invested in this division as a guideline for a harm-reduced coexistence. I conclude by exploring how UWOs fidelity to the human/nature divide speaks to relational theories about urban multispecies cosmopolitics, and how an appreciation of their interventionist niche might inform the aspirational project of the more-than-human city.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.065 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".