In the Language of Their Hearts: Emotions and Language Choice in Child-Parent Interaction, Insights from a Yupik village
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".