(Re)storying the More-than-human City: Urban Coyotes in Canada
Bibliographic record
Abstract
The urban is most often constructed in our imaginaries, policies, and practices as a human space, understood in dualistic opposition to ‘nature’, ‘wilderness’, or the ‘rural’. This thesis aims to (re)story the city as a place of more-than-human belonging in three registers: theoretically, methodologically, and in practice. It does so using a case study of urban coyotes in Ontario as a point of entry, mobilizing ‘hybrid’ socio-ecological methods including: participant observation with Coyote Watch Canada, document review, semi-structured interviews, secondary analyses of GPS collar data, field investigations, and trail cameras. First, it (re)stories the city in urban theory by advancing a more-than-human everyday urbanism as a useful analytic for articulating a less anthropocentric reading of the city. It first performs a rhythmanalysis, demonstrating the importance of linear and cyclical rhythms in the spacetimes of human-coyote encounters. It then delves into coyote sensory worlds, illustrating the acoustic and olfactory ecologies which shape urban atmospheres, navigations, and inhabitations. Everyday urbanism shifts the focus from spectacular moments of conflict with wildlife, to a consideration of more-than-human place-making, resituating urban animals as neighbours rather than invaders. Second, this thesis (re)stories the city through a methodological focus on individual animals’ geographies. It relays narratives of two urban coyotes and their kin, Urban10 and Blondie, whose unique experiences are gleaned through hybrid socio-ecological research methods. Their stories become ones of synanthropy and cynanthropy – of animals learning to thrive in ‘human-dominated’ environments, and of the imaginative exercises of ‘becoming coyote’ required of multispecies researchers endeavouring to generate knowledge collaboratively with other-than-humans. Third, it (re)stories coyotes within urban wildlife management scholarship and practice by advancing a set of best practices for aversion conditioning (humane hazing) to mitigate human-coyote conflicts in urban areas. It challenges traditional language, characterizations of behaviours, and policy options, enabling an alternate set of compassionate responses that do not presume coyotes as incongruous with urban life, or the inevitability of animal death for human convenience. Overall, this thesis contributes to emerging dialogues at the intersection of more-than-human, animal, and urban geographies, as well as wildlife management, (re)storying the city towards livable multispecies futures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".