Nuclear Families and Nuclear Risks: The Effects of Gender, Geography, and Progeny on Attitudes toward a Nuclear Waste Facility*
Bibliographic record
Abstract
Abstract Studies of reactions to nuclear facilities have found consistent male/female differences, but the underlying reasons have never been well‐clarified. The most common expectations involve traditional roles—with men focusing more on economic concerns and with women (especially mothers) being more concerned about family safety/health. Still, with changing gender roles, women are becoming economic providers as well as caregivers; past studies have not actually examined the interaction of employment and gender effects. This study examines a rural county where issues of risk and economic interest were both salient—a county where a nuclear waste site had been proposed but where an existing nuclear power plant was a major employer. Overall, concern levels expressed by employed mothers did not differ significantly from those in the rest of the sample, but further analyses revealed a sharp contrast: In the half of the county that was home to the existing nuclear power plant, where economic concerns could be expected to be more salient, over 90 percent of the employed mothers expressed low levels of concern; in the other half of the county, closer to the potential risks of the proposed nuclear waste site, almost 90 percent of the employed mothers expressed high levels of concern. No such differences are found for other sociodemographic groups. This county may or may not be unique; what the findings show is that the interplay of geography, gender roles and risks should receive more attention in other contexts, as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".