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Locating Culture in the Brain and in the World

2016· book-chapter· en· W2408921508 on OpenAlexaff
Rebecca Seligman, Suparna Choudhury, Laurence J. Kirmayer

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsMcGill University
Fundersnot available
KeywordsEmbodied cognitionCultural neuroscienceOperationalizationPsychologySituatedSet (abstract data type)Context (archaeology)Priming (agriculture)Social neuroscienceSociologyCognitive scienceSocial psychologyNeuroscienceEpistemologySocial cognitionCognitionBiologyComputer science

Abstract

fetched live from OpenAlex

Abstract Cultural neuroscience explores the interplay between the social transmission of knowledge and the functional organization of the nervous system. However, much current cultural neuroscience simplifies culture into categories and constructs. The operationalization of culture as categories and traits that can be measured using questionnaires or priming techniques often lacks cultural validity. Moreover, treatment of culture as a set of fixed and even hard-wired traits has the potential to reify and essentialize differences between groups that are better understood as culturally constructed, fluid, and context-dependent. We argue that culture is better conceived of in terms of interactional processes rather than categories and demonstrate how a more nuanced understanding of culture in cultural neuroscience can contribute to an understanding of mind, self, and emotion as embodied, socially embedded, and situated or enacted in specific contexts, opening up new directions for research with greater potential relevance to issues of health and social disparity.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.977
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.289
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2016
Admission routes1
Has abstractyes

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