Bilingual beginnings as a lens for theory development.
Bibliographic record
Abstract
Phonetic categories become language specific across the first months of life. However, at the onset of word learning there are tasks in which infants fail to utilize native language phonetic categories to drive word learning. In 2005, we advanced a framework to account for why infants can detect and use phonetic detail in some tasks but not in others (Werker and Curtin, 2005; see also Curtin and Werker, 2007). In this framework, PRIMIR (processing rich information from multidimensional, interactive representation), we argue that by their first birthday, infants have established language-specific phonetic category representations, but also encode and represent both subphonetic and indexical details of speech. Initial biases, developmental level, and task demands influence the level of detail infants use in any particular experimental situation. On some occasions phonetic categories are accessed, but in other tasks they are not given priority. Recently, we have begun studying infants who are exposed to two native languages from birth (Werker and Byers-Heinlein, 2008). In the current paper, we will review recent work on speech perception and word learning in bilingual-learning infants. This will be followed by a discussion of how this research has lead to advances in, and changes to, PRIMIR.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".