Bibliographic record
Abstract
Determining the linguistic background of participants is a very important step in language research.Developed by Bosch & Sebastin-Galls (2001), the Language Exposure Questionnaire (LEQ) determines the percentage of input a child is exposed to per language.This interview-style questionnaire indirectly measures the language exposure of infants through parental estimates and is frequently used in bilingualism research (Byers-Heinlein, 2015).However, these parental reports can be biased and can inaccurately depict language use in bilingual households.New technology now allows for a more direct measure of a child's language environment.The Language Environment Analysis (LENA) system is a digital language processor that records and analyzes the child's audio environment.The LENA system has been used in research to measure the speech style of input but has yet to be used as a measure of language exposure (Weisleder & Fernald, 2013; Ramrez-Esparza et al., 2014).This study aims to compare the estimated percentages of exposure obtained using the LEQ and using the LENA system.The estimates are hypothesized to differ and such a result may lead us to question the validity of using indirect measures to evaluate language exposure.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".