‘A drop of water in the pool’: information and engagement of linguistic communities around a municipal pesticide bylaw to protect the public's health
Bibliographic record
Abstract
The Multicultural Yard Health and Environment Project (MYHEP) used Toronto's Pesticide Bylaw roll-out process to examine how culturally specific perceptions and practices might influence the relevance of municipal public health information and community engagement strategies and the effectiveness of health protection initiatives. In Canada, and particularly in Toronto, such information is needed for governments to effectively engage with increasingly diverse populations. Focus groups and individual interviews were conducted with Spanish- and Cantonese-speaking participants to document opinions about pesticide use and regulation and views on municipal information and engagement strategies. MYHEP participants reported a need for more accessible environmental health messaging. There was confusion over the safety and legality of pesticide products available for sale in Toronto stores. Most participants indicated they were unwilling to make formal complaints about neighbours who were not complying with the bylaw (an important mechanism for enforcement). Results indicate that environmental health communication and engagement strategies need to be more carefully tailored to address local sociocultural and linguistic contexts in order to provide more equitable environmental health protection and promotion for all residents. These findings led Toronto Public Health to adapt its efforts so as to better engage communities regarding environmental health.
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.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".