Tracking the Real Thing: Representation, Relationship, and Masculinity in the Animal Stories of Euro-Canadian Men
Bibliographic record
Abstract
Animal stories by Euro-Canadian men have had a disproportionate influence on the development of the mode, and engage with representations of animals from within the dominant androcentric, anthropocentric discourse of Western cultures. Dominant representations influence public and personal opinions, and these in turn influence both personal actions and governmental policy, which affect wild and domestic animals on individual as well as systemic levels. Animal stories by Euro-Canadian men work within preconceived conceptualizations of animals, as informed by the work of prominent Europeans such as René Descartes and Charles Darwin, and either reinforce those conceptualizations or work against the predominant Western understanding of animals, and can have lasting effects on both cultural and environmental landscapes. Analyses of the primary texts problematize definition and categorization, and identify common themes and narrative strategies in texts’ representations of animals and human-animal relationships. The eleven stories examined here use expressions of Western masculinity and femininity as a means of creating identification with their protagonist species. These stories support hegemonic masculinity and prototypically masculine values such as virility and dominance, values that reinforce gendered stereotypes and male dominance in Western culture and that Other femininity and non-binary identities. These stories support hegemonic masculinity that perpetuates prototypically masculine values such as virility and dominance that reinforce gendered stereotypes and male dominance in Western cultures and Others femininity and non-binary identities. In large part, these stories perpetuate the status quo and naturalize cultural understandings of masculinity and femininity as biologically based, and in so doing promote emotional bonding through suggestions of shared cultural experience. Stories have the power to change perceptions of entire species, and even to alter human behaviour toward nonhuman animals on a cultural level. Canadian animal stories as a mode have received little critical attention, and future research into this area may provide insights into authors’ contributions to Canadian literature, women and minority writers’ responses to the hegemonic discourse, and the influence of animal representation on readers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".