Bibliographic record
Abstract
This article applies the notion of redeployment in second language acquisition to contact-induced diachronic changes. Of special interest are cases where a marked phonological contrast has spread across neighboring languages. Such cases suggest that listeners can re-weight and re-map phonetic cues onto novel phonological structures. On the redeployment view, cues can indeed be re-weighted, but phonological structures which underlie a new contrast are not expected to be fully novel; rather, they must be assembled from preexisting phonological structures. Emphatics are an instructive case. These are (mostly) coronal consonants articulated with tongue-root retraction. Phonological emphasis is rare among the world's languages but it is famously endogenous in Arabic and in Interior Salish and it has spread from these to not a few neighboring languages. The present study describes and analyzes the genesis of phonological emphasis and its exogenous spread to a dozen mostly unrelated languages—from Arabic to Iranian and Caucasian languages, among others, and from Interior Salish to Athabaskan and Wakashan languages. This research shows that most languages acquire emphatics by redeploying the phonological feature [RTR] (retracted tongue root) from preexisting uvulars. On the other hand, some languages acquire imitations of emphatics by redeploying the consonantal use of [low] from preexisting pharyngeals. Phonological emphasis is apparently not borrowed by neighboring languages where consonants lack a phonological feature fit for redeployment. The overall impression is that a language in contact with emphatics may newly adopt these sounds as [RTR] or [low] only if the relevant feature is already in use in its consonant system. This pattern of adoption in language contact supports the redeployment construct in second language acquisition theory.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".