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Record W351717382 · doi:10.1017/cbo9780511616792

Language, Culture, and Society

2006· book· en· W351717382 on OpenAlexaff
Christine Jourdan, Charles Taylor, John Leavitt, Régna Darnell, Penelope Brown, Paul Kay, Monica Heller, Elinor Ochs, Elizabeth A. Povinelli, Paul Friedrich, Kevin Tuite

Bibliographic record

VenueCambridge University Press eBooks · 2006
Typebook
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSociology of languageLinguisticsComprehension approachReading (process)PerceptionPersonality psychologySociolinguisticsSociocultural linguisticsLinguistic anthropologyPsychologySociologyLanguage educationPersonalitySocial psychology

Abstract

fetched live from OpenAlex

Language, our primary tool of thought and perception, is at the heart of who we are as individuals. Languages are constantly changing, sometimes into entirely new varieties of speech, leading to subtle differences in how we present ourselves to others. This revealing account brings together eleven leading specialists from the fields of linguistics, anthropology, philosophy and psychology, to explore the fascinating relationship between language, culture, and social interaction. A range of major questions are discussed: How does language influence our perception of the world? How do new languages emerge? How do children learn to use language appropriately? What factors determine language choice in bi- and multilingual communities? How far does language contribute to the formation of our personalities? And finally, in what ways does language make us human? Language, Culture and Society will be essential reading for all those interested in language and its crucial role in our social lives.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.015
Scholarly communication0.0120.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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 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

Citations371
Published2006
Admission routes1
Has abstractyes

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