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Record W4289792727 · doi:10.7202/1090315ar

In the Language of Their Hearts: Emotions and Language Choice in Child-Parent Interaction, Insights from a Yupik village

2021· article· en· W4289792727 on OpenAlexvenueno aff
Daria Schwalbe

Bibliographic record

VenueÉtudes/Inuit/Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPoliticsLinguistic relativitySociology of languageHeritage languageEmotionalityShameSocial psychologySociologyLinguisticsComprehension approachLanguage educationCognitionPolitical scienceLaw

Abstract

fetched live from OpenAlex

In studies of language choice and minority language shift and maintenance, attention is frequently given to factors other than emotions: social context of contact, language politics, linguistic competence and attitudes, educational policies, and political agendas in a society. Yet human language is ideologically saturated, aesthetically experienced empirical phenomena, characterized by complex dynamics and linked to group and personal identities, morality, aesthetics, and epistemology. While negative moral emotions (e.g., shame) may lead people to abandon their first language, heritage languages may still be perceived as “more emotional,” and their loss and maintenance is a deeply emotional matter. Drawing on Pavlenko, Cavanaugh, and Ahmed, I discuss the role of emotion-related factors—affective repertoires and perceived language emotionality—in language choice of native Chukotkan parents, as a way of understanding human interactivity and the potential of the local environment for children’s acquisition of their heritage languages. Perceived language emotionality, I argue, is an important yet often overlooked aspect of heritage language sustainability and learning. The focus of this article is not on how bodies are transformed into objects of emotions (e.g., “the shamed one”), but on interplay between emotions and multilingual phenomena: how language and wordings are used to move people, to produce affects, attachments, equalities, and authenticities.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.072
GPT teacher head0.447
Teacher spread0.375 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2021
Admission routes1
Has abstractyes

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