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Record W4391528300 · doi:10.1086/729834

Attractiveness and Attainment: Status, Beauty, and Jobs in China and the United States

2024· article· en· W4391528300 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsUniversity of Toronto
FundersCanada Research Chairs
KeywordsAttractivenessBeautyChinaDemographic economicsPolitical sciencePsychologyEconomics

Abstract

fetched live from OpenAlex

Research on how physical attractiveness affects labor market outcomes has yielded contradictory results. Conceptualizing attractiveness as a diffuse status characteristic, the authors emphasize the role of status consistency in matching job applicants to positions of varying prestige. They argue that the effects of attractiveness depend on consistency with the job seeker’s other status characteristics and fit with the status of the focal job. A résumé audit study in China and a survey experiment in the United States both show that more attractive applicants with elite educational credentials were favored for higher-status jobs and less attractive applicants from nonelite universities were favored for lower-status positions. Applicants with either attractive looks or elite educational credentials, but not both, were not favored for either type of job. The authors’ model reconciles the mixed results of previous research and illuminates the interplay between physical and nonphysical status characteristics in labor markets.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.366
Teacher spread0.349 · 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