Beware of Law: the Socio-Legal Construction of “Dangerous Dogs” and the Cultural Economy of Interspecies Injury (Ontario, Canada)
Bibliographic record
Abstract
Since the 1980s, dog bites have received expansive media coverage across Can-America, sustaining public attention and generating impassioned demand for government intervention. In recent years, anthropologists have noted that responsive regulations consistently reinforce existing patterns of inequality and dispossession. Yet, little is known about “dangerous” dogs or their owners themselves, nor how the pairs interact with, participate in, and challenge the laws that govern the relationship they share, on the one hand, and their shared relationship to publics, on the other. Beware of Law centres these perspectives through a longitudinal study (2015-2021) structured by the experiences of two Toronto-area dog owners navigating the protracted social, legal, and financial costs of owning a biting dog. I map these connections across a century of paradigm shifts in pet-keeping in general and dog training in particular, and scrutinize their configuration across several interconnected sites, namely: the opinion editorials and public hearings where provincial dog legislation is debated; the city streets where municipal dangerous dog orders are issued and the quasi-judicial tribunals where they are appealed; the homes where dog-related injury lawsuits arrive in the mail and the personal injury law firms that produce these suits in high volume; and a human-dog training/support group where participants envision and bring into practice an alternative social contract that derives its force from canine interests. Throughout these chapters, I disrupt the anthropocentric premise that injury reflects a fixed-path breach of strictly human duty. Not-quite property and not-quite persons, I argue that pet dogs occupy a proximal status and proximate identity that disrupts dichotomous readings of private and public space, as well as civil and criminal jurisdiction, ultimately troubling the person/property distinction which sits at the heart of the common law tradition. This thesis makes three substantial contributions to studies of animals, injury, and law. First, it is comprised by the first critical qualitative analyses of how dog owners interact with legal systems and bureaucracy. Second, it puts forth the first ethnography of personal injury firms. Third, it opens a new conversation about intra-species legalities, giving fresh attention to the imbrication and elision of non-human (canine) agency in contemporary jurisprudence. Beware of Law ultimately presents a dynamic and potentially transformative approach to interspecies law and justice, and encourages more radical forms of legal consciousness in a more-than-human world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.038 | 0.027 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".