Home language use in Norwegian early childhood education and care – Polish mothers’ perspectives
Bibliographic record
Abstract
The aim of this study is to increase knowledge on immigrant parents’ perspectives on use and support of home languages (HLs) in Norwegian Early Childhood Education and Care (ECEC) to promote language development and social inclusion. Better knowledge about parents’ views and expectations may prepare ECEC teachers for difficult situations in partnerships with parents. In this study, we interviewed five Polish immigrant mothers to explore their experiences and expectations regarding the support of their HL, Polish, in Norwegian ECEC centres. The interviews were analysed using a reflexive thematic analysis, and we characterised the mothers’ perspectives by the use of the analytical concepts of assimilative, supportive and inclusive language practices [Chumak-Horbatsch (Citation2012). Linguistically Appropriate Practice: A Guide for Working with Young Immigrant Children. University of Toronto Press]. In general, the mothers appreciated the use of Polish during the transitional phase in ECEC to ease their children’s first weeks there. While some mothers advocated for continued HL use within ECEC following the transition period, others prioritised full Norwegian immersion to promote the acquisition of Norwegian and social integration. Interestingly, they all treasured a general multilingual ECEC environment, but they were divided as to whether this multilingual environment should include Polish.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".