Community-led vertebrate pest management in urban areas: barriers and motivations
Bibliographic record
Abstract
Residential green spaces in cities can make a significant contribution to urban conservation. To engage urban residents in conservation, we need to understand what influences participation. We interviewed leaders of community conservation groups and surveyed members of the public in Auckland, New Zealand using an anonymous questionnaire. We investigated whether environmental attitudes differ between those who do and do not participate in conservation actions (volunteering in a community conservation group and/or controlling pest mammals), and the motivations and barriers to participating in conservation actions. We found that conservation leaders often founded their conservation groups with a biodiversity motivation, whereas many of their group members subsequently joined and continued to participate for social reasons. Conservation group members were more likely to be in favor of pest control and had more positive environmental attitudes than non-participants. They found group work more motivating and productive than working alone. For people already participating in conservation (controlling pests, leading a group, or volunteering), the most common barrier to increasing participation was opportunity, most notably a lack of time. We found that people tended to control pest mammals for self-interested reasons, such as preventing damage to their homes (67%; n = 358), whilst biodiversity motivations (protecting native species) were secondary (53%; n = 283). For people not participating in pest control, the primary barrier was a lack of interest in participating (26%; n = 109). Although people were supportive of conservation, biodiversity motivations alone are unlikely to be a sufficient motivator for participation. Given the range of different motivations and barriers, targeted messaging (e.g., promoting social connections) could increase participation in urban conservation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".