Editorial: Social and affective domain in home language development and maintenance research
Bibliographic record
Abstract
social-emotional wellbeing. It highlights parents' key role in fostering balanced bilingualism and positive development. Purpuri et al. examine the "feeling different" experience of bicultural bilinguals during language switching, tied to cultural values and behavior. It can lead to exclusion but often enriches personal growth and societal contributions, offering insights into cultural identity amid immigration challenges. Protassova and Yelenevskaya analyze how the war in Ukraine has changed language policies in Russian-speaking immigrant families. They show that many families with Ukrainian roots now prioritize Ukrainian to strengthen cultural ties, while Russian is viewed negatively. Some families, however, still prioritize Russian for educational and professional benefits. Pagé and Noels study how childhood language policies in multilingual families affect language retention in emerging Canadian adults. They find that most participants, aged 17 to 29, aim to retain their home language and are open to adding other languages, providing insights into effective heritage language retention across generations. Ergün and Demirdağ explore how positive language education boosts foreign language enjoyment (FLE) via subjective well-being (SWB). Interventions improved classroom atmosphere and self-awareness. Results show SWB significantly predicts FLE, highlighting the role of positivity in language learning. Szczepaniak-Kozak and Wąsikiewicz-Firlej investigate teacher agency in Polish schools after the 2022 Ukrainian refugee influx, highlighting teachers' swift adaptation to new linguistic and cultural diversity through collaboration and training, despite limited resources. Nenonen scrutinizes positive attitudes towards multilingualism and the influence of social factors on language practices in a multilingual Russian-Italian family in Finland. The family uses an "one person-one language" strategy, with each parent speaking a different language to the child. Schwartz and Ragnarsdóttir present a model for home-preschool continuity in linguistically and culturally diverse settings. They integrate responsive teaching, family language policies, and parental involvement, based on Bronfenbrenner's ecological model, Epstein's parental involvement model, and teacher-parent agency. The suggested model aims to support children's linguistic security through collaboration between parents and teachers, offering a framework for research and practical solutions in multilingual preschool settings. Gacs, et al. examine listening comprehension in German-Russian bilinguals aged 13-19, focusing on Russian as the home language. They explore how language proficiency, family input, and media exposure affect listening skills at various levels (phoneme, word, sentence, and text), finding differences in comprehension across these levels and highlighting the role of linguistic background and language input in shaping listening abilities. This Research Topic provides valuable insights into the relationship between family language policies, bilingualism, and multilingual practices. The studies highlight the importance of supportive environments in both home and educational settings, showing how parents, educators, and communities play key roles in maintaining multilingualism. Together, the contributions emphasize the need for a comprehensive approach to language development that considers social, emotional, and cultural factors to ensure the sustainability of linguistic diversity across generations. The practical implications are broad. It can inform language policies that support bilingual education and home language preservation. The findings also offer guidance for training parents and teachers to better support bilingual development. Targeted services for multilingual families can help address language maintenance and integration challenges. In education, the research can shape curricula that promote bilingualism and heritage language retention. Finally, it highlights the role of community networks in supporting language maintenance and fostering intercultural understanding. Future research could explore the long-term impacts of bilingual upbringing on cognitive, identity, and socio-economic development across different cultural contexts.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.027 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".