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Record W1997813200 · doi:10.1037/a0022159

Mind the gap: Increasing associations between the self and blacks with approach behaviors.

2011· article· en· W1997813200 on OpenAlexaff
Curtis E. Phills, Kerry Kawakami, Emmanuel Tabi, Daniel Nadolny, Michael Inzlicht

Bibliographic record

VenueJournal of Personality and Social Psychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsPsychologyPrejudice (legal term)Social psychologyOutgroupIngroups and outgroupsAssociation (psychology)Implicit-association testContext (archaeology)Developmental psychology

Abstract

fetched live from OpenAlex

Although perceived differences between outgroup social categories and the self are often cited as a major contributor to prejudice and intergroup tension, surprisingly few studies have examined ways to improve associations between the self and racial outgroups. The present research investigated one strategy to increase these associations-approach training. Specifically, 3 studies investigated the impact of training participants to conceptually approach Blacks on 3 separate measures: 2 response latency measures indexing the strength of association between the self and Blacks and a psychophysiological measure indexing brain activity in response to Blacks in the context of the self. A fourth study examined the link between earlier research on the impact of approach training on implicit prejudice against Blacks and the current results related to self-Black associations. Together, these findings provided consistent evidence that training in approaching Blacks increases associations between the self and Blacks that in turn reduce implicit prejudice against Blacks.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.127
GPT teacher head0.381
Teacher spread0.254 · 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.

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

Citations87
Published2011
Admission routes1
Has abstractyes

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