Explaining bilingual learning outcomes in terms of exposure and input
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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