Context, Cultural Bias, and Health Risk Perception: The “Everyday” Nature of Pesticide Policy Preferences in London, Calgary, and Halifax
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Risk perception and the cultural theory of risk have often been contrasted in relation to risk-related policy making; however, the local context in which risks are experienced, an important component of everyday decision making, remains understudied. What is unclear is the extent to which localized community beliefs and behaviors depend on larger belief systems about risk (i.e., worldviews). This article reports on a study designed to understand the relative importance of health risk perceptions (threat of harm); risk-related worldviews (cultural biases); and the experiences of local context (situated risk) for predicting risk-related policy preferences regarding cosmetic pesticides. Responses to a random telephone questionnaire are used to compare residents' risk perceptions, cultural biases, and pesticide bylaw preferences in Calgary (Alberta), Halifax (Nova Scotia), and London (Ontario), Canada. Logistic regression shows that the most important determinants of pesticide bylaw preference are risk perception, lack of benefit, and pesticide "abstinence." Though perception of health risk is the best single predictor of differences in bylaw preferences, social factors such as gender and situated risk factors like conflict over chemical pesticides, are also important. Though cultural biases are not important predictors of pesticide bylaw preference, as in other studies, they are significant predictors of health risk perception. Pesticide bylaw preference is therefore more than just a health risk perception or worldview issue; it is also about how health risk becomes situated-contextually-in the experiences of residents' everyday lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it