Explaining perceptions of a technological environmental hazard using comparative analysis
Bibliographic record
Abstract
This study addresses one of the main research problems in the area of environmental hazard risk—to explain why perception of threat from the same hazard varies between groups. We argue that the cultural theory of risk, explicitly place‐contingent ways of life and worldviews that support those ways of life, goes a long way towards explaining risk perception differences in the communities of Kinuso, Fort Assiniboine and Barrhead Alberta. Fifty‐five in‐depth interviews were conducted within these communities; three of the four communities are closest to the Alberta Special (hazardous) Waste Treatment Facility. A regional donut pattern of interviewee concern is partially explained as differential attachment to ways of life like farming, tourism and hunting for the concerned and amenity‐proximate rural living for the unconcerned. These relationships are further supported by worldviews like distrust and sensitivity to equity for the concerned and the price of progress for the unconcerned. Though this study is not about siting process per se, detailed conversations about the siting process indicate that the perceptions of risk (as concern) in the operational phase of this hazard were solidified early on and are likely difficult to change.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".