Explaining bilingual learning outcomes in terms of exposure and input
Bibliographic record
Abstract
I had several goals in writing my keynote “Exposure and input in bilingual development”. The first was to emphasize that there are two components to the study of environmental effects on language learning. The first is the stuff ‘out there’ ( exposure ) that we want to observe and count and whose effects we want to assess; the second is the internal, mentally represented stuff (my input ) that is logically related to a particular learning problem. Both exposure and input are indissociable from assumptions about what language acquisition mechanisms do and the nature of linguistic cognition. Accordingly, for example, a decision to count ‘words’ in child-directed speech (CDS) or via a parental questionnaire is not an innocent one. Not only can one find radically different views on what a ‘word’ is (Krause, Bosch & Clahsen, 2015), one can find work that questions the need to postulate such a unit at all (see discussion in MacWhinney, 2000). It follows that adopting a clear position, about which abstract mentally represented elements are crucial cues to learning some phenomenon, is an essential step in deciding what to count in CDS.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".