Predictors of Extreme Negative Feelings Toward Coyote in Newfoundland
Bibliographic record
Abstract
Human–coyote interactions have occurred since the arrival of the species to the island of Newfoundland in 1985. A mail survey (N = 786) of Newfoundland residents was conducted in 2008. The survey explored negative feelings toward coyotes. A four stage hierarchical multiple regression model examined how the dependent variable, “feelings,” was influenced by four independent blocks of variables: “existence beliefs,” “impact beliefs,” “fear,” and “experience and demographic characteristics.” Together the predictors explained 50% of the variability, with existence beliefs accounting for most of the variation (ΔR2 = . 45), followed by impact beliefs (ΔR2 = .024) and fear (ΔR2 = .018). The experience-demographic block of variables accounted for minimal influence (ΔR2 = .003) and was not statistically significant. The remaining variability might be explained by emotions. When exploring human–wildlife interactions it is important to understand the role of affect in the formation of attitudes as feelings influence the tolerance and ultimately the willingness to coexist with wildlife.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".