The development of phonological memory and language: A multiple groups approach
Bibliographic record
Abstract
Pierce et al. (2017) have proposed that variations in the timing, quality and quantity of language input during the earliest stages of development are related to variations in the development of phonological working memory and, in turn, to later language learning outcomes. To examine this hypothesis, three groups of children who are at-risk for language learning were examined: children with cochlear implants (CI), children with developmental language disorder (DLD), and internationally-adopted (IA) children, Comparison groups of typically-developing monolingual (MON) children and second language (L2) learners were also included. All groups were acquiring French as a first or second language and were matched on age, gender, and socioeconomic status, as well as other group-specific factors; they were between 5;0-7;3 years of age at time of testing. The CI and DLD groups scored significantly more poorly on the memory measures than the other groups; while the IA and L2 groups did not differ from one another. While the IA group performed more poorly than the MON group, there was no difference between the L2 and MON groups. We also found differential developmental relationships between phonological memory and language among the groups of interest in comparison to the typically-developing MON and L2 groups supporting the hypothesis that language experiences early in life are consequential for language development because of their effects on the development of phonological memory.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".