<i>Phonological development in specific contexts: Studies of Chinese-speaking children</i>. Zhu Hua. Cleveden, UK: Multilingual Matters, 2002, Pp. 218.
Bibliographic record
Abstract
Phonology is usually explained as the study of speech sounds and their patterns and functions in the lexical representation of speakers of languages (Kenstowicz, 1994; Spencer, 1996). Some years ago the question, “Where's phonology?” was raised by Macken (1992) in the context of the large concern with the phonetics of acquisition and the conception of phonological acquisition as acquisition of phonetics. This division between phonology and phonetics may be traced to the work of the Prague School of Trubetzkoy (1939/1969) and earlier. Macken proposed a relatively autonomous phonological component, with perceptual, articulatory, and phonological-based abstract rules and principles, to account for learners' lexical representation and suggested a hierarchy of prosodic words, segments, and features as the basis of phonological acquisition (Macken, 1979, 1992). Recent emphasis is on the interaction among phonology, phonetics, and psychology, and this integrative approach has implications for studying common crosslinguistic speech sound patterns (Ohala, 1999). Phonology is further seen as addressing the questions of rules and representations, which may apply to “compute the phonetic representation” within the framework of universal grammar (Kenstowicz, 1994, p. 10).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".