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Record W2346400422 · doi:10.1177/2158244016646150

Power, Ethnic Origin, and Sexual Objectification

2016· article· en· W2346400422 on OpenAlexaff
Ciro Civile, Amentha Rajagobal, Sukhvinder S. Obhi

Bibliographic record

VenueSAGE Open · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMcMaster University
Fundersnot available
KeywordsObjectificationPsychologyEthnic groupSocial psychologyPerceptionExtant taxonDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

In this study, we investigated the effects of primed power on sexual objectification of Caucasian and Asian men and women. As in previous studies, sexual objectification was assessed using an inversion paradigm with face–body compound stimuli. Previous work has shown that participants primed to power do not show the typical drop in recognition performance for inverted face–body compound stimuli, suggesting that they process these stimuli in terms of their individual features, in a manner akin to objects, and quite different from the way in which faces and bodies are normally processed (i.e., configurally). Caucasian male and female participants were primed to high or neutral-power before engaging in an old/new recognition task involving sexualized face–body compound images of Caucasian and Asian men and women. Participants primed to high-power showed a decreased inversion effect for Caucasian models of the opposite gender, but not for Asian models. Thus, power exerts different effects on this specific type of social perception, depending on the ethnic origin of the target. We discuss our results in the context of the extant literature on power and with reference to media stereotyping of Caucasians and Asians.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.389
Teacher spread0.304 · 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.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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