Total Pit Bull Shit: Anomie and Breed Specific Legislation in Windsor, Ontario
Bibliographic record
Abstract
This study employs Durkheimian sociology, anomie in particular, to examine breed-specific legislation in Windsor, Ontario. This thesis is unique in that it analyses breed-specific legislation (BSL) in a way that has not been done previously, by applying a rigorous, sociological theory perspective. Other than discussions on totemism and limited discussions of animals, previous applications of Durkheim’s theories on anomie, morality and law have not focused on human-animal relationships, especially the relationship between humans and companion dogs. Human animal studies (HAS) and critical animal studies (CAS) literature has not employed the Durkheimian concept of anomie to understand human-animal relationships and BSL specifically. I conceptualize anomie as a social condition resulting from moral derangement and the overabundance of conflicting moral rules, how they are understood and applied that results in a lack of stable moral references. This conceptualization of anomie guides my analysis of the provincial Dog Owners’ Liability Act R.S.O. 1990, CHAPTER D.16 (DOLA) and the municipal By-law 245-2004 (BL-245). I use the DOLA and the BL-245 to analyse how obligations and sanctions are imposed upon humans and animals, while looking for evidence of anomic social relations. The findings of this thesis indicate that there are discrepancies in the collective consciousnesses, law, science, and the general public. It also articulates how risk and responsibility impact human-animal relationships. Finally, this study exemplifies how breed-specific legislation destabilizes the epistemic reference of what makes a dog a dog.
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".