Catching English: Constructing language choice between Tagalog–English bilingual siblings
Why this work is in the frame
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Bibliographic record
Abstract
In multicultural Canada, preserving heritage languages (HLs) is an issue for many immigrant families. Many parents want to maintain their HL with their children, but do not necessarily speak it consistently athome. In addition, older siblings may start speaking the HL less once they start school. This study examined the language choice among Tagalog–English bilingual siblings in an English-majority setting. We expected to see greater use of English when at least one of the siblings was in school and when pretending to interact in public settings (like schools or restaurants) rather than private. The results showed that the children spoke mostly English regardless of whether they (or their sibling) were in school and regardless of context (public vs. private). We discuss possible reasons for these children’s high use of English.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it