Cross‐cultural Differences In Beliefs And Practices That Affect The Language Spoken To Children: mothers With Indian And Western Heritage
Bibliographic record
Abstract
BACKGROUND: Speech-language pathologists often advise families about interaction patterns that will facilitate language learning. This advice is typically based on research with North American families of European heritage and may not be culturally suited for non-Western families. AIMS: The goal of the project was to identify differences in the beliefs and practices of Indian and Euro-Canadian mothers that would affect patterns of talk to children. METHODS & PROCEDURES: A total of 47 Indian mothers and 51 Euro-Canadian mothers of preschool age children completed a written survey concerning child-rearing practices and beliefs, especially those about talk to children. OUTCOMES & RESULTS: Discriminant analyses indicated clear cross-cultural differences and produced functions that could predict group membership with a 96% accuracy rate. Items contributing most to these functions concerned the importance of family, perceptions of language learning, children's use of language in family and society, and interactions surrounding text. CONCLUSIONS: Speech-language pathologists who wish to adapt their services for families of Indian heritage should remember the centrality of the family, the likelihood that there will be less emphasis on early independence and achievement, and the preference for direct instruction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".