Bibliographic record
Abstract
Herold (1990) discusses three mechanisms by which phonemic merger can take place: expansion, approximation, and transfer. A fourth possibility Herold touches on but does not explore might be called phonological transfer: as in (lexical) transfer, words move abruptly from one phonemic class to another; but rather than one lexeme at a time being transferred, all words of a particular phonological class move simultaneously. This paper provides evidence that phonological transfer is playing a role in the movement toward merger of /o/ (as in lot) and /oh/ (as in thought) in Upstate New York. Words containing (olF)—i.e., historical /o/ followed by /l/ plus a labiovelar, as in golf and revolve—are produced with /oh/ rather than /o/ in 74 percent of tokens; this use of /oh/ is increasing in apparent time. Many speakers using /oh/ in (olF) words have an otherwise clear phonemic distinction between /o/ and /oh/; however, the geographic distribution of this phonological transfer is correlated with other indices of progress toward the low back merger. This indicates that phonological transfer can be regarded here as an early sign of merger in progress, and that a single merger can proceed by two mechanisms simultaneously (here, approximation and phonological transfer).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".