The Look of the Lawn: Pesticide Policy Preference and Health-Risk Perception in Context
Bibliographic record
Abstract
In this paper we report on the results of a residential questionnaire survey ( N = 1088) exploring the relative importance of health-risk perception as compared with social–contextual determinants of urban pesticide bylaw support in two Canadian cities: Calgary and Halifax. Multivariate analysis was used in order to determine the model estimates for five outcome variables: pesticide policy preference, pesticide-risk perception, a weed-free aesthetic, pesticide use, and chemical dissent. Findings indicate that the strongest determinants (based on relative odds) of pesticide preference are pesticide-free yard-care practices and divergent lawn aesthetics (eg pesticide-free versus weed-free yards). Though risk perception does help distinguish between differences in policy preferences, pesticide use, and chemical dissent, it does not for aesthetic preferences. Pesticide bylaw preference is more than just a health-risk perception issue; it is also situated in the experiences of residents' everyday lives (eg yard care) where decisions about pesticide use are made.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".