Retaining change in attitudes and emotions toward coyotes using experiential education
Bibliographic record
Abstract
Context Education programs concerning wildlife conservation and safety typically include the biology of the wildlife species and public safety information. Information retention using traditional means such as signs, pamphlets and static presentations have been shown to be minimally effective at changing attitudes and behaviour when it comes to human–wildlife interactions. Aims An experiential education program with interactive modules was designed to support information retention in participants. On the basis of previous research, a targeted experiential education program focusing on perceptions of risk and preventative behaviours was produced to increase people’s comfort level when in coyote habitat. Methods Pre-, post- and retention-test questionnaires were used to study differences in attitudes and risk perception instantly following (post-test) as well as 1 year after participating in the program (retention test). Key results Overall, the program had significant positive effects on participants’ attitudes, and significant decreases in their overall perception of risk in terms of potential interaction with coyotes. These positive effects were observed instantly and 1 year after participants were surveyed. Conclusions Targeted and interactive educative experiences can have impacts on participants’ perceptions over the long term. This technique might be useful when dealing with human–wildlife interactions. Implications Designing targeted educative experiences for people may also support lasting positive change in human–wildlife interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".