Killing the Beast: Animal Death in Canadian Literature, Hunting, Photography, Taxidermy, and Slaughterhouses, 1865-1920
Bibliographic record
Abstract
This dissertation explores the ways in which practices of killing animals in late nineteenth and early twentieth century Canada shaped humans’ perceptions of self and place. Analyzing the multivalent meanings of animal death in wild animal stories, sport hunting, photography, taxidermy, and meat eating, I argue that killing animals was integral to the expansion of settler colonialism in the dominion, materially facilitating the extension of agriculture and industry, and rhetorically legitimizing claims to conquest over indigenous peoples and wild landscapes.\nBut humans’ self-definitions through animal death were not straightforward tales of mastery. Increasingly aware of the disappearance of wildlife from the dominion’s forests, less dependent upon wildlife for subsistence, women and men attributed greater cultural, political, and economic value to the nation’s animals, empathizing with animals and condemning animal extinction. Expressing a sense of guilt over human culpability in the vanishing of wild species, then, humans sought ways of defeating the ravages of modernity by preserving traces of animals in material, representational forms, using encounters with animals as means of defining a sense of self and nation. Fictional stories of animals proliferated, sport hunting soared in popularity, and taxidermied animals adorned many walls. Contemporaries killed animals as a means of legitimizing colonial occupation of newly settled land and asserting mastery over nature, then, but they also regretted their role in precipitating the disappearance of animals from nature. In reconciling this paradox, human and animal engaged in an ongoing process of co-constitution, defining and redefining shifting boundaries of kinship and otherness in a myriad of ways.\nSuch paradoxical meanings of animal death emerged when humans were no longer reliant upon wild animals for survival. As such, I conclude this study by analyzing an important counterpart to wild animal death—the slaughtering of domestic animals as meat. Eating commercially produced meat increasingly defined one’s status as a modern subject within a technologically advanced and civilized nation, the transition from eating wild animals to domestic animals symbolizing a sense of success in overcoming the challenges of settlement in a colonial landscape.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.037 | 0.021 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".