Understanding Residents' Environmental Risk Perceptions in Three Toronto Neighbourhoods: Lived Experiences, Expectations and Policy Implications
Bibliographic record
Abstract
The environment is fraught with uncertainties that vary in scope and nature. Dangers posed by exposure to environmental pollutants in the air, soil, water and food are difficult to measure with certainty. Due to scientific and technical limitations, levels of risk stemming from environmental uncertainties cannot be defined solely in objective terms. Environmental-risk constructs are inherently subjective and influenced by multiple and interdependent factors such as psychological, social, cultural, economic, political and environmental conditions. Although environmental-risks are assessed largely on the basis of subjective considerations, lay-individuals' views on environmental-risks are seldom considered as relevant dimensions of risk management. By examining three Toronto neighbourhoods, this paper demonstrates that lay individuals' perceptions toward environmental-risks are rooted in contextual factors and often linked to the neighbourhood's structural conditions. This paper found certain variables such as socioeconomic status (SES), education, locus of control and commitment to place (among others) as influential factors in determining the level of environmental-risk perception. These underlying forces that mediate risk perception can vary widely across neighbourhoods, understanding them in their local contexts can enhance environmental-risk communication and strategic decision-making. Lay-individuals' knowledge and experiential wisdom about their environment should be acknowledged with more sincerity and given more consideration in decision-making.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".