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Record W7143622138 · doi:10.34415/00000641

A study of sex difference in health risk perception

2005· article· ja· W7143622138 on OpenAlexaboutno aff
晶子 松本, 亮 小田, 裕 五百部, Akiko Matsumoto, Ryo Oda, Hiroshi Ihobe

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2005
Typearticle
Languageja
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryTest (biology)Risk perceptionPerceptionSelection (genetic algorithm)Financial riskRisk assessment

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.360
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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