2006, Spoken syntax: The phonetics of giving a hand
Bibliographic record
Abstract
the ONZE team. The work done by members of the ONZE team in preparing the data, making transcripts and obtaining background information is gratefully acknowledged. We are very grateful to Andrea Sudbury and Dani Schreier for allowing us to use their phonetic analyses. We are also grateful for the University of Canterbury Visiting Erskine Fellowship to Bresnan during February and March of 2005, which enabled our collaboration. This paper has benefited from the comments of Susanne Gahl and two anonymous reviewers. 1 This paper considers the exemplar theories which are independently developing in phonetics and in syntax, and argues that they jointly make some predictions that neither does alone. One of these predictions is explored in the context of two sound changes which occurred in the history of New Zealand English. We show that both of these phonetic changes were affected by phrase-level factors. The raising of /æ / was more advanced in the word hand when it referred to a limb, than when used in phrases such as give a hand or lend a hand. And the centralization of the /I / vowel was more advanced in utterances of give involving abstract themes (give a chance), than when it had a meaning of transfer of possession (give a pen). We argue that existence of such effects lends support both to the idea (from syntactic exemplar theory) that phrases are stored, and the idea (from phonetic exemplar theory) that lexical representations are phonetically detailed. 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".