Stepping backwards in development: integrating developmental speech perception with lexical and phonological development – a commentary on Stoel-Gammon's ‘Relationships between lexical and phonological development in young children’*
Bibliographic record
Abstract
Within the subfields of linguistics, traditional approaches tend to examine different phenomena in isolation. As Stoel-Gammon (this issue) correctly states, there is little interaction between the subfields. However, for a more comprehensive understanding of language acquisition in general and, more specifically, lexical and phonological development, we must consider relations between multiple subfields. That is, by examining the interactions between these subfields, a greater understanding of lexical and phonological development can emerge. For instance, the interaction between phonology, syntax and semantics is demonstrated in recent work looking at how phonological patterns can provide a basis for inferring a word's lexical category (such as nouns and verbs) (Christiansen, Onnis & Hockema, 2009; Lany & Saffran, 2010).
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.040 | 0.047 |
| Insufficient payload (model declined to judge) | 0.003 | 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".