Cast Out Urbanites: The Historical Problematization of Cows in Kingston
Bibliographic record
Abstract
Animals are regularly defined and managed as problems in urban policies and practices. Despite being common, the problematization of animals is ill-understood and under-theorized in urban geography. In this dissertation, I argue that problematization is important because it has significant implications for animals: not only in how they are subjected to violent disciplinary practices but also in how they are made epistemically visible (or not) as urban subjects. That is, problematization objectifies animals and can contribute to their physical and epistemic in/visibility in cities. One effect of problematization is that it makes some animals visible to the historical record as problems. Consequently, scholars often write urban histories and analyses that reconstitute these animals as problematic objects, failing to recognize that problematization involves multispecies power relations that animals experience. Focusing on the problematization of cows in Kingston, Ontario between 1838-1938, I explore how problematization can conceptually be used to understand the urban histories of animals in a way that takes them seriously as subjects. I argue that a spatial understanding of problematization allows for a nuanced multispecies analysis. In doing so, I analytically focus on spaces of configuration, material spaces of governance, and institutional/social spaces. Methodologically, I use material gathered from the Queen’s University Archives and conduct a discourse analysis that focuses on how cows were legally constituted and municipally governed as problems. I supplement this analysis with speculative vignettes and maps that make cows better visible as historical subjects. Drawing together these diverse modes of analysis, I argue that cows in Kingston were problematized because they were defined and managed as transgressive in property relations, risky in health relations, and waste in commodity relations. This problematization not only resulted in cows’ bodies, environments, and social worlds being violently managed but also contributed to cows being cast out from Kingston’s urban imaginary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".