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Record W4327682232 · doi:10.1007/s10508-023-02571-0

Worth the Risk? Greater Acceptance of Instrumental Harm Befalling Men than Women

2023· article· en· W4327682232 on OpenAlexaff
Maja Graso, Tania Reynolds, Karl Aquino

Bibliographic record

VenueArchives of Sexual Behavior · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmPsychological interventionPsychologyIntervention (counseling)Social psychologyDevelopmental psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Scientific and organizational interventions often involve trade-offs whereby they benefit some but entail costs to others (i.e., instrumental harm; IH). We hypothesized that the gender of the persons incurring those costs would influence intervention endorsement, such that people would more readily support interventions inflicting IH onto men than onto women. We also hypothesized that women would exhibit greater asymmetries in their acceptance of IH to men versus women. Three experimental studies (two pre-registered) tested these hypotheses. Studies 1 and 2 granted support for these predictions using a variety of interventions and contexts. Study 3 tested a possible boundary condition of these asymmetries using contexts in which women have traditionally been expected to sacrifice more than men: caring for infants, children, the elderly, and the ill. Even in these traditionally female contexts, participants still more readily accepted IH to men than women. Findings indicate people (especially women) are less willing to accept instrumental harm befalling women (vs. men). We discuss the theoretical and practical implications and limitations of our findings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.093
GPT teacher head0.299
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2023
Admission routes1
Has abstractyes

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