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Record W2954348742 · doi:10.1017/langcog.2019.15

Are stories just as transporting when not in your native tongue?

2019· article· en· W2954348742 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

VenueLanguage and Cognition · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsYork University
Fundersnot available
KeywordsNarrativeFluencyFirst languageComprehensionLinguisticsContrast (vision)English languagePsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

abstract We spend much of our time consuming stories across different types of media, often becoming deeply engaged or transported into these stories. However, there has been almost no research into whether processing a story in one’s non-native language influences our level of transportation. We analyzed three existing datasets in order to compare engagement with English-language stories for those who reported English as their first language and those who reported English as their second language. Stories were presented as text (Study 1), audio (Study 2), and short films (Study 3). Across all studies, equivalent levels of narrative transportation between language groups were found, even after accounting for age and years of English fluency. These results are in contrast to some previous proposals that emotional reactions are attenuated during non-native language processing, despite equivalent levels of comprehension. Our evidence indicates that individuals processing a narrative in their second language feel just as transported into the story as those processing the same narrative in their native language.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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