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Record W2129416096 · doi:10.1037/a0025970

In perfect harmony: Synchronizing the self to activated social categories.

2011· article· en· W2129416096 on OpenAlexaff
Kerry Kawakami, Curtis E. Phills, Anthony G. Greenwald, Daniel Simard, Jeannette Pontiero, Amy Brnjas, Beenish Khan, Jennifer S. Mills, John F. Dovidio

Bibliographic record

VenueJournal of Personality and Social Psychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsYork University
FundersNational Institute of Mental Health
KeywordsPsychologySocial psychologyFeelingSocial categoryAction (physics)PerceptionSelf-conceptSelfPriming (agriculture)InterdependenceSociology

Abstract

fetched live from OpenAlex

The self-concept is one of the main organizing constructs in the behavioral sciences because it influences how people interpret their environment, the choices they make, whether and how they initiate action, and the pursuit of specific goals. Because belonging to social groups and feeling interconnected is critical to human survival, the authors propose that people spontaneously change their working self-concept so that they are more similar to salient social categories. Specifically, 4 studies investigated whether activating a variety of social categories (i.e., jocks, hippies, the overweight, Blacks, and Asians) increased associations between the self and the target category. Whereas Studies 1 and 2 focused on associations between stereotypic traits and the self, Studies 3 and 4 examined self-perceptions and self-categorizations, respectively. The results provide consistent evidence that following social category priming, people synchronized the self to the activated category. Furthermore, the findings indicate that factors that influence category activation, such as social goals, and factors that induce a focus on the interconnectedness of the self, such as an interdependent vs. independent self-construal, can impact this process. The implications of changes to the working self-concept for intergroup relations are discussed.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.708

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.102
GPT teacher head0.387
Teacher spread0.285 · 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 designQualitative
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

Citations55
Published2011
Admission routes1
Has abstractyes

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