MAPLE: A Multilingual Approach to Parent Language Estimates
Bibliographic record
Abstract
Bilingual infants vary in when, how, and how often they hear each of their languages. Variables such as the particular languages of exposure, the community context, the onset of exposure, the amount of exposure, and socioeconomic status are crucial for describing any bilingual infant sample. Parent report is an effective approach for gathering data about infants’ language experience, however, its quality is highly dependent on how information is elicited. This paper introduces a Multilingual Approach to Parent Language Estimates (MAPLE). MAPLE promotes best practices for using structured interviews to reliably elicit information from parents on bilingual infants’ language background, with an emphasis on the challenging task of quantifying infants’ relative exposure to each language. We discuss sensitive issues that must be navigated in this process, including diversity in family characteristics and cultural values. Finally, we identify six systematic effects that can impact parent report, and strategies for minimizing their influence.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; both teacher heads agree on what is shown here.
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".