The influence of family, school, and community on bilingual preference: Results from aLouisiana/Québec case study
Bibliographic record
Abstract
This case study examines the shifting bilingual preference of three French/English bilingual children over a three-year period. It also clarifies the distinction between the many often misleading terms used to refer to bilingual preference (i.e., a bilingual's language choice). The children's fluctuating bilingual preference is accounted for in terms of three contextual domains: home, school, and community. The home domain was predominantly French-speaking, while the community domain shifted between predominantly English-speaking Louisiana and French-speaking Québec. The 10-year-old identical twin girls were in a French immersion program in Louisiana during the entire three-year period; their 12-year-old brother was not. A new, domain-sensitive longitudinal measure – the bilingual preference ratio (BPR) – was created and applied for each child using 36 months of weekly tape recordings of mealtime conversations. BPR fluctuations indicate that the greatest effect on the children's language preference was community immersion in the target language. However, the twins' markedly greater preference for speaking French at home in Louisiana is attributed to the influence of French immersion at school.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".