Stakeholder Participation in Investigating the Health Impacts from Coal‐Fired Power Generating Stations in Alberta, Canada
Bibliographic record
Abstract
Developing an effective stakeholder participation process and communication dialogue continues to be a challenge in dealing with risk issues, particularly those in which the risk is uncertain and people are fearful about the potential impacts. The complex public stakeholder relations and risk communication issues associated with investigating the potential human health effects associated with exposure to the emissions of coal‐fired power generating stations are discussed. Residents in the area around Lake Wabamun (west of Edmonton, Alberta, Canada) have raised concerns about potential health impacts from four nearby coal‐fired power generating stations. The Wabamun and Area Community Exposure and Health Effects Assessment Programme (WACEHEAP) was developed to look specifically at what people are being exposed to in this area as well as some of the health effects from these exposures. Public stakeholders to this process included the general public, community interest groups and the Paul First Nation. Two surveys were conducted to better understand community concerns, communication and information needs, and desire for involvement. Consultations were also held with the Paul First Nation. The results provided important insights into the risk perspectives of these groups, including communication needs and desired means of participating in the risk assessment process.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".