Bibliographic record
Abstract
The pattern is familiar: cries evolve to babbles, babbles are shaped into words, and words are joined to create sentences. This sequence describes the path taken by all children as the language they hear around them is examined, internalized, and eventually developed into native-speaker competence. Although recent research has shown the immense variability in both rate and achievement for children learning their first language (Fenson et al., 1994), the process nonetheless has an enviable consistency about it, especially compared with the erratic and idiosyncratic variability of second-language acquisition. But these visible landmarks of progress in themselves reveal little of the internal complexities and mental revolutions that are propelling the child into linguistic competence. How do children learn language? We begin by trying to understand how a child learns one language in a relatively simple cognitive and social environment, so that when the stakes are raised, we have a basis for describing and interpreting a child's experience with multiple languages in complex social circumstances. The formal study of language acquisition began with the scrupulous observation of young children learning to talk. Before there was a single hypothesis probing the nature of this process, researchers were recording the speech of their children and creating a database. The most famous of these was Leopold (1939–49) whose four-volume report remains a classic in the field. Interestingly, Leopold's daughter, the subject of the study, was being raised bilingually, although it took several decades for the study of bilingualism and second-language learning to gain a respectable position in studies of language acquisition. Beginning in the late 1950s, Roger Brown and his students carried out the first major program of research into child language acquisition that incorporated both observation and experimental manipulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".