When Precaution Creates Misunderstandings: The Unintended Effects of Precautionary Information on Perceived Risks, the EMF Case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past decade, growing public concern about novel technologies with uncertain potential long-term impacts on the environment and human health has moved risk policies toward a more precautionary approach. Focusing on mobile telephony, the effects of precautionary information on risk perception were analyzed. A pooled multinational experimental study based on a 5 × 2 × 2 factorial design was conducted in nine countries. The first factor refers to whether or not information on different types of precautionary measures was present, the second factor to the framing of the precautionary information, and the third factor to the order in which cell phones and base stations were rated by the study participants. The data analysis on the country level indicates different effects. The main hypothesis that informing about precautionary measures results in increased risk perceptions found only partial support in the data. The effects are weaker, both in terms of the effect size and the frequency of significant effects, across the various precautionary information formats used in the experiment. Nevertheless, our findings do not support the assumption that informing people about implemented precautionary measures will decrease public concerns.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it