An analytical framework to understand the problematization of urban (historical) animals
Bibliographic record
Abstract
Despite being common, the problematization of animals is ill-understood and undertheorized in urban geography. Being problematized 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. This article offers a theoretical framework for analyzing the problematization of urban animals. It requires understanding problematization as a sociospatial and historical process in which animals come to be discursively constituted as problems in urban regulation, and materially managed as such through disciplinary practices that frequently rely on material and spatial interventions. I argue that a spatial awareness is essential to telling multispecies histories and geographies that attempt to grapple with problematization as a process as well as its impacts on the experiences of animals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| 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".