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Record W2739045028 · doi:10.1075/ni.27.1.05bis

Story sequencing and stereotyping

2017· article· en· W2739045028 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNarrative Inquiry · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsYork University
Fundersnot available
KeywordsCompetition (biology)ChinaAccountabilityPsychologySocial psychologySequence (biology)SociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Over the last decade, sometimes violent conflicts have erupted between generations in China over who should have a seat on a crowded bus. Through a small story approach to an extended sequence of Chinese bus stories, this study examines how elder-blaming comes to be instantiated in talk-in-interaction. The analysis elaborates Deppermann's finding that cooperative in-group bonding is not the sole reason that out-group stereotypes are instantiated: competition among interactants as they “top” one another’s stories also plays an important part. We nuance this, first, by pointing to actions that are simultaneously cooperative and competitive. Second, we foreground how the interactional troubles of our storytellers fundamentally revolve around issues of epistemic accountability and, in turn, are assuaged by cooperative epistemic acts, in which stereotyping and story "topping" entwine.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.176
GPT teacher head0.357
Teacher spread0.181 · 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