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Record W4226190194 · doi:10.1177/07352751221088919

Dueling with Dual-Process Models: Cognition, Creativity, and Context

2022· article· en· W4226190194 on OpenAlexaff
Gordon Brett

Bibliographic record

VenueSociological Theory · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCreativityCognitionCognitive scienceDual (grammatical number)Context (archaeology)Action (physics)PsychologyScholarshipImprovisationProcess (computing)Thinking processesDual process theory (moral psychology)EpistemologyCognitive psychologySocial psychologyComputer scienceMathematics education

Abstract

fetched live from OpenAlex

Sociologists increasingly draw on dual-process models of cognition to account for the ways context, cognition, and action interrelate. Drawing from 40 interviews with improvisers and observations from improvisational theater, I find that dual-process model scholarship is limited in three respects: It does not consider how cognition operates in situations where order and disruption are concurrent, it fails to realize there is interindividual variation in cognitive processing, and it underestimates the creativity emerging through automatic processes. Interactions in improv contain elements of both order and disruption, and they place demands on automatic and deliberate cognition simultaneously. Improvisers respond to these competing demands through either automatic or deliberate thinking dispositions, which are engendered through explicit instruction, practical experience, and artistic commitments. These dispositions, in turn, shape creative decision-making, predicting interindividual differences in how improvisers respond to contingencies on stage. I conclude by discussing the implications for culture, cognition, and action.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.998
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.021
Scholarly communication0.0120.018
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.062
GPT teacher head0.319
Teacher spread0.257 · 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.

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

Citations36
Published2022
Admission routes1
Has abstractyes

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