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Record W1886786995 · doi:10.1111/pops.12056

Applying the Flanker Task to Political Psychology: A Research Note

2013· article· en· W1886786995 on OpenAlexaff
Scott P McLean, John P. Garza, Sandra A. Wiebe, Michael D. Dodd, Kevin B. Smith, John R. Hibbing, Kimberly Andrews Espy

Bibliographic record

VenuePolitical Psychology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Alberta
FundersUniversity of Nebraska-LincolnNational Science Foundation
KeywordsDistractionPsychologyTask (project management)PoliticsIdeologyCognitive psychologyValue (mathematics)Psychological researchCognitionPolitical psychologySocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

One of the two stated objectives of the new “Research Note” section of Political Psychology is to present short reports that highlight novel methodological approaches. Toward that end, we call readers' attention to the “flanker task,” a research protocol widely employed in the study of the cognitive processes involved with detection, recognition, and distraction. The flanker task has increasingly been modified to study social traits, and we believe it has untapped value in the area of political psychology. Here we describe the flanker task—discussing its potential for political psychology—and illustrate this potential by presenting results from a study correlating political ideology to flanker effects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.111
metaresearch head score (Gemma)0.106
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.111
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.106
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0060.017
Open science0.0050.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.002

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 head0.175
GPT teacher head0.555
Teacher spread0.381 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2013
Admission routes1
Has abstractyes

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