PRIMIR: A Developmental Framework of Infant Speech Processing
Bibliographic record
Abstract
Over the past few years, there has been an increasing emphasis on studying the link between infant speech perception and later language acquisition. This research has yielded some seemingly contradictory findings: In some studies infants appear to use phonetic and indexical detail that they fail to use in other studies. In this article we present a new, unified framework for accounting for these divergent findings. PRIMIR (a developmental framework for Processing Rich Information from Multidimensional Interactive Representations) assumes there is rich information available in the speech input and that the child picks up and organizes this information along a number of multidimensional interactive planes. Use of this rich information depends on the joint activity of 3 dynamic filters. These filters-the initial biases, the developmental level of the child, and requirements of the specific language task the child is facing-work together to differentially direct attention to 1 (or more) plane. In this article we outline the contradictory data that need to be explained, elucidate PRIMIR, including its underlying assumptions and overall architecture, and compare it to existing frameworks. We conclude by presenting core predictions of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".