Language attitudes, practices, and concerns of bi-/multilingual families raising infants and toddlers
Bibliographic record
Abstract
The early years are a critically important period in language acquisition. Infants’ and toddlers’ language exposure and interactions, which predominantly occur in the family context at this age, are foundational to successful development. However, in families who use two or more languages, children’s language environments and their acquisition achievements are highly variable and complex. Many parents have questions and concerns about how to best support their bi-/multilingual infants and toddlers, but unfortunately science has so far provided few empirically-supported answers. This project investigates Quebec-based parents’ attitudes and ideologies regarding childhood multilingualism, their concerns, their linguistic behaviours to support their children’s multilingual development, and their use of resources in order to do so. Our focus is on Quebec-based families across a full range of bi- and multilingual language configurations: English-French, English-Other, French-Other, and English-French- Other. In Stage 1 of the project, we conducted small-scale focus groups and interviews to identify key themes, using open-ended questions and discussions. Stage 2 now focuses on quantitative as well as qualitative data elicited from a much larger sample by means of a questionnaire (based on the focus group and interview findings).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".