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Record W2942838808 · doi:10.1037/xge0000605

Complex intersections of race and class: Among social liberals, learning about White privilege reduces sympathy, increases blame, and decreases external attributions for White people struggling with poverty.

2019· article· en· W2942838808 on OpenAlexaff
Erin Cooley, Jazmin L. Brown‐Iannuzzi, Ryan F. Lei, William Cipolli

Bibliographic record

VenueJournal of Experimental Psychology General · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSympathyPrivilege (computing)BlameWhite privilegeWhite (mutation)RacismSocial psychologyPovertyPsychologySociologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

= 1,189), we hypothesized that White privilege lessons may both highlight structural privilege based on race, and simultaneously decrease sympathy for other challenges some White people endure (e.g., poverty)-especially among social liberals who may be particularly receptive to structural explanations of inequality. Indeed, both studies revealed that while social liberals were overall more sympathetic to poor people than social conservatives, reading about White privilege decreased their sympathy for a poor White (vs. Black) person. Moreover, these shifts in sympathy were associated with greater punishment/blame and fewer external attributions for a poor White person's plight. We conclude that, among social liberals, White privilege lessons may increase beliefs that poor White people have failed to take advantage of their racial privilege-leading to negative social evaluations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.023
GPT teacher head0.347
Teacher spread0.324 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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