A corpus-assisted discourse study of parental concerns regarding multilingual child-rearing
Bibliographic record
Abstract
Many parents have concerns about raising their children with multiple languages. However, there is a paucity of previous research regarding parental concerns about multilingual child-rearing, particularly in multilingual societies. We address this gap with a corpus-assisted discourse study of parental concerns regarding multilingual child-rearing in Quebec, Canada. We created two corpora comprising 641 parents’ responses to an open-ended survey question regarding their main concerns about raising multilingual infants/toddlers (English corpus: 12,502 words, French corpus: 9,360 words). We examined frequencies, collocations, concordance lines, and longer segments to investigate the nature and strength of different concerns. Our results revealed that two previously-attested concern types – cognition concerns and exposure-fluency concerns – were most prominent. The results also provided more nuanced insights into the nature of these concerns. Moreover, the results revealed two additional concern types: concerns regarding trilingual/heritage language transmission and concerns about the effect of multilingualism on children’s identity and social/emotional well-being. These had not previously been attested. Our research makes a theoretical contribution by advancing knowledge about parental concerns and how they can contribute to the study of family language policies. Additionally, our findings may serve as the basis for improving support for multilingual families.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".