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Input elaboration, head faithfulness, and evidence for representation in the acquisition of left-edge clusters in West Germanic

2004· book-chapter· en· W259643470 on OpenAlexfundno aff
Heather Goad, Yvan Rose

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSonority hierarchyHead (geology)LinguisticsMathematicsHistoryPhilosophy

Abstract

fetched live from OpenAlex

Several recent investigations of the development of left-edge clusters in West Germanic languages have demonstrated that the relative sonority of adjacent consonants plays a key role in children's reduction patterns (e.g., Fikkert 1994, Gilbers and Den Ouden 1994, Chin 1996, Barlow 1997, Bernhardt and Stemberger 1998, Gierut 1999, Ohala 1999, Gnanadesikan this volume). These authors have argued that, for a number of children, at the stage in development when only one member of a left-edge cluster is produced, it is the least sonorous segment that survives, regardless of where this segment appears in the target string or the structural position that it occupies (head, dependent, or appendix). To illustrate briefly, while the more sonorous /S/ is lost in favour of the stop in /S/+stop clusters, /S/ is retained in /S/+sonorant clusters; similarly, the least sonorous stop survives in both /S/+stop and stop+sonorant clusters, in spite of the fact that it occurs in different positions in the two strings. To account for reduction patterns such as these, a structural difference between /S/-initial and stop-initial clusters need not be assumed. This would seem to fare well in view of much of the recent constraint-based literature which de-emphasises the role of prosodic constituency in favour of phonetically based explanations of phonological phenomena (see, e.g., Hamilton 1996, Wright 1996, Kochetov 1999, Steriade 1999, Côté 2000).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.330
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations165
Published2004
Admission routes1
Has abstractyes

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