Emotional Tone of Ontario Newspaper Articles on the Health Effects of Industrial Wind Turbines Before and After Policy Change
Bibliographic record
Abstract
Newspapers are often a primary source of health information for the public about emerging technologies. Information in newspapers can amplify or attenuate readers' perceptions of health risk depending on how it is presented. Five geographically distinct wind energy installations in Ontario, Canada were identified, and newspapers published in their surrounding communities were systematically searched for articles on health effects from industrial wind turbines from May 2007 to April 2011. The authors retrieved 421 articles from 13 community, 2 provincial, and 2 national newspapers. To measure the emotional tone of the articles, the authors used a list of negative and positive words, informed from previous studies as well as from a random sample of newspaper articles included in this study. The majority of newspaper articles (64.6%, n = 272) emphasized negative rather than positive/neutral tone, with community newspapers publishing a higher proportion of negative articles than provincial or national newspapers, χ(2)(2) = 15.1, p < .001. Articles were more likely to be negative when published 2 years after compared with 2 years before provincial legislation to reduce dependence on fossil fuels (the Green Energy Act), χ(2)(3) = 9.7, p < .05. Repeated public exposure to negative newspaper content may heighten readers' health risk perceptions about wind energy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".