Portrayal of interactions between humans and coyotes (Canis latrans): content analysis of Canadian print media (1998-2010)
Bibliographic record
Abstract
Print media is one form of public discourse that provides a means to examine human-coyote interactions. We conducted a content analysis of 453 articles addressing coyote events reported in the Canadian print media between 1998 and 2010. We found 119 articles about human-coyote interactions, of which 32 involved a report of coyote biting (26) or attempting to bite (6) a person. 108 articles were about coyote-dogs and 32 about coyotes-cat interactions. Remaining articles were on topics unrelated to interactions (e.g. culls). Basing our analysis in grounded theory, we identified important descriptive and emotional themes surrounding these events. The most common words describing coyotes were: brazen, wiley, mangy, nuisance, wild and vicious.Interactions were described as attacks in 185 articles, while only 32 “attacks” were identified. Coyotes were portrayed as not natural in cities, as an invasive species, and more recently using language depicting criminal behaviour. Descriptions of coyotes killing or attacking people were inflammatory (e.g. savaged, ripped juts open), whereas descriptions of people killing coyotes were not (e.g. euthanized). Five emotional responses emerged describing humans involved in coyote interactions. Of these, statements of fear were most prevalent and yielded the richest understanding of perceptions about the risk of coyote-human interactions, including:fear for children’s safety (73), fear for disease (44), fear for pet safety (43), and fear for self or others safety(35). Traumatic response was reported in 28 articles, while sadness and grief were described in 17. Two other themes were: 1) animal welfare concerns, 2) frustration due to lack of agency response. Popular media plays a critical role in shaping public understanding and can influence people’s emotional experiences, perceptions and management consequences. We highlight that coyotes are prejudiced (and stereotyped) based on the isolated and sensationalized incidents. Coyotes in particular elicit a wide range of emotional responses in people, and there is often a wide gap between perception and reality of risk when understanding whether it is possible for humans and coyotes to co-exist. Hence, there is a strong need for media literacy about the unintended or intended maligning of coyotes to the general public, as the consequence can be social amplification of risk and the unwarranted persecution of coyotes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".