Ideological Coyotes: A more-than-human geography of landowners’ discourse in the Foothills Parkland Region of Alberta, Canada
Bibliographic record
Abstract
This thesis presents a critical animal geography analysis of human-coyote relationships in the Foothills Parkland Region of Alberta, Canada. Concerned by large-scale reports of coyote killing in rural parts of North America, this thesis reveals discursive themes in interviews of landowners living alongside coyotes in the study area. Previous studies in North America have predominantly focused on why killing predators is not sound ecological practice. While some studies have begun to address human dimensions of perceived wildlife conflict, research has not attended directly to the discourse and ideologies behind the perceived conflict with coyotes. In this thesis, I identify that coyote management practices appear sociocultural and ideological rather than ecological in reasoning. In the more-than-human landscape of the Foothills Parkland Region, where livestock industry abounds, coyotes are discursively framed as pestilant and threatening bodies to many agricultural landowners. Yet, as the region has developed, becoming more heterogenous, views on coyotes are increasingly divided and polarized. This research explores how coyotes become social, cultural, political, and ideological creatures. While, overall, I find practices regarding coyotes are dictated by speciesism, my discourse analysis also identifies that ideologies of rurality, masculinity, and capitalism influence the human-coyote relationship. The Foothills Coyote Initiative provided 47 audio interviews which I transcribed, coded, and analyzed, identifying emergent discursive themes in landowners’ reported relationships with coyotes. Bringing together disciplines of rural geographies and critical animal geographies, this thesis reveals the ideologies that sustain the practice of killing coyotes, offering insights on anti-predator attitudes across North America.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".