Almost everyone in New York is raising PRICEs
Bibliographic record
Abstract
Using the data from the new CUNY Corpus of New York City English, we explore a phonological analysis by Kaye (2012) that argues that the New York City English (NYCE) PALM shares an underlying 'stem vowel' with PRIZE. Kaye's proposal is based on two observations: (i) the phonetic similarities of PALM and the nucleus of PRIZE and (ii) the conditioning factors that have led to the historical relexicalization of many Middle English short-o words from LOT to PALM are the same as those that have led relexicalization PRICE words to PRIZE. However, it has previously been observed that PALM is merging with LOT in NYCE. Consequently, it would be likely that if that vowel shares an underlying identity with PRIZE, PRIZE too should be merging. In fact, our data show a complex pattern. First, although PRIZE is backer than PRICE, there is considerable overlap. Also, the PRIZE nucleus tends to coincide with LOT more than PALM. Second, more younger speakers, who have a merged PALM-LOT, do not show a merged PRIZE-PRICE, but a new form of distinction, in which PRICE and PRIZE are now in a Canadian Raising pattern. In this way, NYCE loses a locally distinctive vowel configuration to match a widespread northeastern US regional pattern with a two-vowel low back system and Canadian Raising involving PRICE and PRIZE. In sum, the data show the complexity of the relationship between vowels in subsystems and that reconfigurations may involve multiple elements.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".