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Record W2267478843 · doi:10.1017/s0142716415000387

Perspectives on bilingual children's narratives elicited with the Multilingual Assessment Instrument for Narratives

2015· article· en· W2267478843 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

VenueApplied Psycholinguistics · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsDalhousie UniversityConcordia University
Fundersnot available
KeywordsNarrativeTurkishGermanSpecific language impairmentPsychologyLinguisticsLanguage impairmentHebrewSlovakDevelopmental psychologyCzech

Abstract

fetched live from OpenAlex

This Special Issue is all about the stories of children: preschool- and school-age children; bilingual and monolingual children; children developing typically or identified as having a specific language impairment (SLI); and children speaking and experiencing one or more of the following languages: English, Finnish, German, Greek, Hebrew, Italian, Russian, Slovak, Swedish, and Turkish in minority or majority language contexts. The stories are fictional ones, about baby birds and baby goats, a cat and a dog: a cast of characters the reader will come to know well as they read the Introduction (Gagarina, Klop, Tsimpli, & Walters, 2016) and individual articles. They were collected using a new narrative assessment tool that is common to all the articles within the issue: the Language Impairment Testing in Multilingual Settings—Multilingual Assessment Instrument for Narratives (LITMUS-MAIN; Gagarina et al., 2012, 2015), described at some length by its developers in the Introduction to the Special Issue.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.033
GPT teacher head0.351
Teacher spread0.319 · 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