Bilingual language experience as a multidimensional spectrum: Associations with objective and subjective language proficiency
Bibliographic record
Abstract
Abstract Despite the multifactorial space of language experience in which people continuously vary, bilinguals are often dichotomized into ostensibly homogeneous groups. The timing of language exposure (age of acquisition) to a second language (L2) is one well-studied construct that is known to impact language processing, cognitive processing, and brain organization, but recent work shows that current language exposure is also a crucial determinant in these domains. Critically, many indices of bilingual experience are inherently subjective and based on self-report questionnaires. Such measures have been criticized in favor of objective measures of language ability (e.g., naming ability or verbal fluency). Here, we estimate the bilingual experience jointly as a function of multiple continuous aspects of experience, including the timing of language exposure, the amount of L2 exposure across communicative contexts, and language entropy (a flexible measure of language balance) across communicative contexts. The results suggest that current language exposure exhibits distinct but interrelated patterns depending on the socio-experiential context of language usage. They also suggest that, counterintuitively, our sample more accurately self-assesses L2 proficiency than native language proficiency. A precise quantification of the multidimensional nature of bilingualism will enhance the ability of future research to assess language processing, acquisition, and control.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".