Characterizing the social diversity of bilingualism using language entropy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bilingual and multilingual individuals exhibit variation in everyday language experience. Studies on bilingualism account for individual differences with measures such as L2 age of acquisition, exposure, or language proficiency, but recent theoretical perspectives posit that the relative balance between the two or more languages throughout daily life (i.e., INTERACTIONAL CONTEXT) is a crucial determinant for language representation, access, and control. We propose an innovative measure to characterize this construct by using ENTROPY to estimate the social diversity of language use. Language entropy is computed from commonly-collected language history data and generalizes to multilingual communicative contexts. We show how language entropy relates to other indices of bilingual experience and that it predicts self-report L2 outcome measures over and above classic measures of language experience. Thus, we proffer language entropy as a means to characterize individual differences in bilingual (and multilingual) language experience related to the social diversity of language use.
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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