The demographics of knowledge, attitudes and emotions toward coyotes
Bibliographic record
Abstract
Abstract Context A coyote-caused human fatality in Cape Breton Highlands National Park of Canada (CBHNPC) occurred in 2009. Because CBHNPC is federally protected, rangers have a limited number of management options for dealing with human–coyote conflict. The park initiated the present study to understand the publics’ acceptance of coyotes. Aims This article examined relationships between each of four independent variables (respondent type (resident vs visitor), sex, age, education) and each of four dependent variables (knowledge, attitude, two emotions) related to coyotes in CBHNPC. Researchers have repeatedly suggested that demographics are related to cognitions and emotions toward wildlife. Managers can use demographic findings to target education campaigns to specific stakeholders. Methods Survey data were obtained from (a) residents living around CBHNPC (n = 556, response rate = 70%), and (b) visitors hiking two popular trails in CBHNPC (n = 443, response rate = 60%). Key results All four independent variables were related to knowledge. Visitors were more knowledgeable about coyotes than were residents. Females were more knowledgeable than were males. Younger respondents were more knowledgeable than were older individuals. All education categories differed from each other. Findings for the attitude construct were similar. Residents held negative attitudes toward coyotes, whereas visitors were slightly positive. Males and females both held negative attitudes. The youngest age category held a positive attitude, whereas the oldest group was the most negative. Respondents with a high-school degree had a negative attitude; those with a college degree held a positive attitude. For the first emotion concept, residents were more emotional than were visitors. Males were more emotional than were females, and high-school graduates were more emotional than were college graduates. For the second emotion, there were statistical differences between residents and visitors, as well as between males and females. However, age and education were not related to this scenario. Conclusions Although there were statistical differences for 13 of 16 tests, over 80% of the effect sizes were minimal and there were interaction effects among the four demographic variables. Implications Findings highlighted complexities managers should consider when designing communication strategies aimed at influencing stakeholders’ knowledge of and attitudes and emotions toward wildlife.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".