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Record W1779815013 · doi:10.18806/tesl.v29i0.1110

Through the Eyes and From the Mouths of Young Heritage-Language Learners: How Children Feel and Think About Their Two Languages

2012· article· en· W1779815013 on OpenAlexvenueno aff
Maureen Jean, Esther Geva

Bibliographic record

VenueTESL Canada Journal · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)Active listeningPsychologyHeritage languageContext (archaeology)Language assessmentPopulationLanguage acquisitionLiteracyFirst languageAttributionSecond-language attritionLinguisticsDevelopmental psychologyComprehension approachLanguage educationMathematics educationSocial psychologyPedagogyCommunicationSociology

Abstract

fetched live from OpenAlex

This study explores the affective responses and beliefs school-aged heritage-language learners (HLLs) hold regarding learning their two languages. Sixty-three HLLs in grades 3 and 4 were presented with pictorial scenarios involving activities across five language and literacy domains in their HL and second language (English). Children were asked to indicate the affect they associated with the scenario and were queried about their chosen affect. They associated positive affect with listening and speaking the HL at home and with English across all domains regardless of context. Qualitative analysis of children’s attributions revealed skill in the domain or language as the most common rationale for their chosen affective responses. Other common themes in children’s rationales in descending order of frequency included children’s degree of interest in the domain or language, the perceived availability of assistance from others, their membership in language groups, and the influence of language environments on language-learning. Implications for further research with this population and recommendations for relevant parties are discussed.

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 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 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 score0.175
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations16
Published2012
Admission routes1
Has abstractyes

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