MétaCan
Menu
Back to cohort
Record W2345965789 · doi:10.1017/s1743923x16000131

Do Women and Men Respond Differently to Negative News?

2016· article· en· W2345965789 on OpenAlexaff
Stuart Soroka, Elisabeth Gidengil, Patrick Fournier, Lilach Nir

Bibliographic record

VenuePolitics & Gender · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPsychologySkin conductanceArousalNeuropsychologySocial psychologyDevelopmental psychologyClinical psychologyMedicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

This article offers a new approach to studying sex differences in responses to negative news, using real-time physiological responses as opposed to self-reports. Measurements of skin conductance and heart rate are used to examine whether there are differences in the extent to which women and men are aroused by and attentive to negative news stories. Like experiments that have relied on postexposure self-reports, we detect no sex differences in arousal in response to negative news stories. However, in contrast to those experiments, we find indications that women are more attentive than men to negative news content. We consider possible reasons for this difference in findings. We also discuss neuropsychological studies that are consistent with our finding of greater attentiveness on the part of women to negative stimuli. Finally, we consider the relationship between our work and evidence in the literature that women consume less news than men.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.112
GPT teacher head0.425
Teacher spread0.312 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePolitics & GenderSame topicBehavioral Health and InterventionsFrench-language works237,207