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Record W4245011236 · doi:10.31235/osf.io/9k2tj

Bridging cultural sociology and cognitive psychology in three contemporary research programmes

2018· preprint· en· W4245011236 on OpenAlexafffund
Michèle Lamont, Laura Adler, Bo Yun Park, Xin Xiang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCanadian Institute for Advanced Research
FundersCanadian Institute for Advanced Research
KeywordsCognitionPsychologyImplicit attitudeCultural psychologyImplicit-association testSocial psychologyCultural biasPovertySociologyCognitive psychologyPolitical science

Abstract

fetched live from OpenAlex

Three prominent research programmes in cognitive psychology would benefit from a stronger engagement with the cultural context of cognition: studies of poverty focused on scarcity and cognitive bandwidth, studies of dual-processmorality and stud- ies of biases using the implicit association test. We address some limitations of these programmes and suggest research strat- egies for moving beyond an exclusive focus on cognition. Research on poverty using the cognitive bandwidth approach would benefit from considering the cultural schemas that influence how people perceive and prioritize needs. Dual-process morality researchers could explain variation by analysing cultural repertoires that structure moral choices. Research using the implicit association test can better explain implicit attitudes by addressing the variability in cultural schemas that undergird biases. We identify how these research programmes can deepen the causal understanding of human attitudes and behaviours by address- ing the interaction between internal cognition and supra-individual cultural repertoires.

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)0.034
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0140.011
Science and technology studies0.0070.094
Scholarly communication0.0180.029
Open science0.0020.014
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0060.000

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 head0.474
GPT teacher head0.548
Teacher spread0.074 · 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 designTheoretical or conceptual
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

Citations7
Published2018
Admission routes2
Has abstractyes

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