The Role of Attitude, Parenting Styles, and School in First-Language Attrition and Code-switching
Bibliographic record
Abstract
Study 1: Attitude is the unconscious feelings that a person has about a language that are not directly observable but, by using the correct stimuli, the person’s responses can be inferred (Cherciov, 2013). Previous research shows that attitude and motivation towards language influences language attrition in adults. This study aims to investigate if parental and child attitudes towards language influences L1 attrition in bilingual children and if school environment impacts the language attitudes children possess. There are three research questions for this study. First, is there a difference in language attitude and first language proficiency before and after attending school in bilingual children? Second, does parental L1 language attitudes influence children's language proficiency? Third, what is the relationship between attitude and L1 attrition in bilingual children? Study 2: Using English-Hindi bilingual preschool students as participants, this study will try to analyze the factors affecting code-switching. These factors may include parenting style, parents' duration of stay in Canada, family type (nuclear or joint), duration of exposure to each language, extra efforts towards teaching the child the mother tongue. This will be examined using parental surveys and giving the children picture retelling tasks and asking them to retell a Peppa Pig cartoon in Hindi. The instances where the child code-switches to English will be noted and correlated to the factors noted above. First, what factors affect code-switching in bilingual preschoolers? Second, does parenting style have an effect on code-switching in bilingual preschoolers?
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".