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Record W4211099351 · doi:10.1017/cbo9780511500107.001

Introduction

2002· book-chapter· en· W4211099351 on OpenAlexaff
Marisa Bortolussi, Peter Dixon

Bibliographic record

VenueCambridge University Press eBooks · 2002
Typebook-chapter
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrativeConversationPsychologyContext (archaeology)CognitionRecallEpistemologyLinguisticsSociologySocial psychologyCognitive psychologyHistoryPhilosophyCommunication

Abstract

fetched live from OpenAlex

“fear is a failure of the imagination” T. Findlay, Not Wanted on the Voyage The Study of Narrative Narratives in one form or another permeate virtually all aspects of our society and social experience. Narrative forms are found not only in the context of literature but also in the recollection of life events, in historical documents and textbooks, in scientific explanations of data, in political speeches, and in day-to-day conversation (Nash, 1994: xi). In fact, narrative discourse seems to be intrinsic to our ability to use language to explain and interpret the world around us, and there is an abundance of evidence suggesting that the manner in which we process narrative affects our cognitive and linguistic behavior in general. Therefore, understanding the dynamics of narrative can be instrumental in gaining knowledge about how the mind works (Chafe, 1990); how individuals behave in social and personal relationships (Tannen, 1982, 1984); how they acquire and organize knowledge and analyze themselves, the world, and others around them (Potter & Wetherell, 1987; Lamarque, 1990); how they shape their experience of reality (White, 1981; Ricoeur, 1983); and how they are affected by cultural codes and norms. Because of narrative's ubiquitous nature and its perceived importance in all aspects of social life, it is not surprising that narrative “is no longer the private province of specialists in literature (as if it ever should have been)” (Nash, 1994:xi), and that it is now studied across a wide range of disciplines, such as literary studies, cultural studies, linguistics, discourse processing, cognitive psychology, social psychology, psycholinguistics, cognitive linguistics, artificial intelligence, and, as Nash points out, ethno-methodology and critical legal studies (Wieder, 1974).

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.528
Threshold uncertainty score0.673

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.5280.298

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 head0.217
Teacher spread0.194 · 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 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

Citations0
Published2002
Admission routes1
Has abstractyes

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