Bibliographic record
Abstract
Abstract This chapter has three goals: (i) to define and delimit the notion of ‘phonologization’; (ii) to determine how phonologization fits into the bigger picture; (iii) to discuss a few examples, e.g. the effects of voiced obstruents (‘depressor consonants’) on pitch; vowel harmony; word and utterance demarcation. The chapter begins by considering the original definition of phonologization (‘A universal phonetic tendency is said to become “phonologized” when language-specific reference must be made to it, as in a phonological rule." (Hyman 1972:170)), a concept which can be traced back at least as far as Baudouin de Courtenay (1895 [1972:184]). Particular attention is paid to the role of contrast in the phonologization process. After presenting canonical examples of phonologization (particularly transphonologizations, whereby a contrast is shifted or transformed but maintained), it is suggested that the term ‘phonologization’ needs to be extended to cover other ways that phonological structure either changes or comes into being. Throughout the article emphasis is on what Hopper (1987:148) identifies as ‘movements towards structure’: the emergence of grammar (grammaticalization) and its subsequent transformations (regrammaticalization, degrammaticalization). After showing that phonologization has important parallels to well-known aspects of ‘grammaticalization’ (Hyman 1984), the chapter concludes that phonologization is but one aspect of the larger issue of how (phonetic, semantic, pragmatic) substance becomes linguistically codified into form.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".