A study of sex difference in health risk perception
Bibliographic record
Abstract
リスク管理は環境行政において重要な問題である。リスク管理のためには、人々がどのように健康上のリスクを認知しているのかを調査することが必要である。性淘汰理論と進化心理学から、男性の方が女性より、短期的利益の追求において将来を割り引くことに抵抗がないだろうと予測される。ウィルソンら(1996)と小田(2003)は、カナダと日本の大学生に対し、想定したジレンマ状況について選択をするように質問し、男性が健康コストより経済的な成功を選ぶ傾向があったことを示した。本研究は、資源と健康のトレードオフに対してどのような心理メカニズムが働いているのかを考察するために沖縄で調査をおこない、被験者の居住環境が及ぼす影響について調べた。また、先行研究において想定された状況を逆転した質問をおこない、資源と健康のトレードオフへの反応の変化を検討した。 Risk management is an important issue in environmental politics. It is needed to investigate how people recognize health risk for the risk management. Sexual selection theory and evolutionary psychology predict that males may be more willing than females to discount the future in the pursuit of short-term gains. Wilson et al. (1996) and Oda (2003) asked university students in Canada and Japan to make a choice in hypothetical dilemmas and indicated that males tended to choose their financial success at the cost of their health. In the hypothetical situation, the subject males were willing to be transferred from a small town to a new branch in big city for an increase in salary though the city was famous for its smog and severity of illness was high. In this paper, we carried out the same test at Okinawa to know effects of residential environment when human trade health for resource. Moreover, we put the reverse test of previous studies at Aichi and discussed changes of response for trade-off between resource and health.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".