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Record W2593285422 · doi:10.1177/0956797616688885

Relative Physical Position as an Impression-Management Strategy: Sex Differences in Its Use and Implications

2017· article· en· W2593285422 on OpenAlexaff
Anastasia Makhanova, James K. McNulty, Jon K. Maner

Bibliographic record

VenuePsychological Science · 2017
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsImpression managementPsychologyImpressionPosition (finance)Impression formationSocial psychologyApplied psychologySocial perceptionPerceptionAdvertising

Abstract

fetched live from OpenAlex

People's physical position relative to others may shape how those others perceive them. The research described here suggests that people use relative physical position to manage impressions by strategically positioning themselves either higher or lower relative to ostensible observers. Five studies supported the prediction that women take and display photographs portraying themselves in a low relative physical position to highlight their youthful features and appear attractive, whereas men take and display photographs portraying themselves in a high relative physical position to highlight their size and appear dominant. The effectiveness of these strategies was confirmed in two studies that measured social perceptions of male and female targets who varied in their relative position. In sum, as do members of other social species, people use relative physical position to manage social impressions, and although these impression-management strategies may have deep ancestral roots, they appear to manifest themselves through a contemporary human modality-photographs.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.458
Teacher spread0.318 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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