Individual animal geographies for the more-than-human city: Storying synanthropy and cynanthropy with urban coyotes
Bibliographic record
Abstract
Recent efforts within geography to deconstruct anthropocentric readings of the urban and explore the city as 'multispecies' or 'more-than-human' face substantial methodological challenges. This paper contributes an empirical case study of human-coyote urban cohabitations in the Greater Toronto Area, Canada, using a 'hybrid' methodological approach to individual animal geographies. It builds on dialogues surrounding animals' geographies that centre individual animal lifeworlds and experiences, exploring coyotes as subjects and actors who participate in the co-creation of shared urban worlds. A methodological approach based on collaboration and storying recounts the tales of two coyotes - Urban10 and Blondie - and their kin whose stories are gleaned by weaving together diverse social and ecological research tools, including: participant observation with Coyote Watch Canada, document review, semi-structured interviews, GPS collar data, field investigations, ethological observations, and trail cameras. The discussion details implications in terms of cynanthropy - 'becoming-canid' as methodology, delving into coyote lifeworlds using hybrid tools - as well as synanthropy - coyote synurbization and more-than-human urban belongings. Dwelling with Urban10 and Blondie in cynanthropic exploration makes visible opportunities for multispecies researchers to generate knowledge collaboratively with other-than-humans. Findings surrounding synanthropy highlight the practices involved in adapting to and participating, ecologically and socially, in life in the multispecies city. Overall, this paper advances efforts aimed at developing innovative and experimental hybrid methodologies for animal geographies, and theoretical discussions around re-storying the more-than-human 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| 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".