A Breed Apart? Narrating Innocence and Viciousness in Breed-Specific Legislation
Bibliographic record
Abstract
Narratives of vicious pit bulls and children at risk are foundational to arguments in favour of breed-specific legislation (BSL), a pre-emptive form of governance, implemented in select provinces, states, and municipalities around the world, that works (or does not work, as the case may be), not by policing vicious dog behaviour, but by preventing the existence of supposedly intrinsically vicious members of chosen breeds. Though the breed targeted differs depending on context, most often included is, not surprisingly, that canine-non-grata, the pit bull. Taking Ontario’s “pit bull ban” (Bill 132) as a case study, this essay tracks the figure of the pit bull, first in the parliamentary debates leading up to the passing of the law, and then in a subsequently published juvenile novel, Ingrid Lee’s Dog Lost (2008), a text that deploys a familiar narrative of boy-and-dog as an explicit response to Ontario’s BSL. If BSL is predicated on pit bulls being, as Ontario’s Attorney General Michael Bryant put it, “a breed apart”—that is, they must be recognizable, first as “a breed,” and second as a breed that is distinct from all others, or at least from all others that are not covered by BSL—Lee’s narrative too suggests that these dogs might be exceptional, for, like so many canine protagonists, Lee’s “Cash” is hyperbolically noble, the polar opposite of the “commonsense” narrative of the pit bull—the pit bull that “everyone knows.” And, in narrating the experience of her exceptional pit bull under the looming threat of BSL, Lee offers a productive reminder that—attacks by particular, individual dogs notwithstanding—the essentially vicious pit bull, the one that is, as Bryant put it, “a breed apart,” is itself a social construction, and one with very material consequences for dogs and children alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".