A target-oriented approach to neutrality in vowel harmony: Evidence from Hungarian
Bibliographic record
Abstract
This paper provides a novel perspective on neutrality in vowel harmony, using evidence from Hungarian. Despite the extensive study of Hungarian vowel harmony, the intermediate neutrality of [e:], which can alternate harmonically with [a:], is rarely addressed in existing analyses. While many standard accounts of harmony assume that front unrounded vowels like [e:] are neutral due to the lack of back counterpart, the [a:]~[e:] alternation makes this approach unsupportable. Specifically, since both [a:] and [e:] lack harmonic counterparts, but [a:] participates in harmony by re-pairing to [e:], the theory must explain why [e:] is not consistently harmonic. I argue that this pattern forces a new, target-focused approach, where participation is based on the vowel-specific drive to undergo harmony; neutrality results when this drive is insufficient to force unfaithfulness. This idea is motivated by cross-linguistic and phonetic facts suggesting that vowels that are low and/or rounded are inherently better targets of front/back harmony. I implement this approach formally in Harmonic Grammar; the harmony constraint is scaled by the quality of a vowel as a potential target, parallel to Kimper’s (2011) trigger strength scaling. This account can capture not only the basic Hungarian facts, but also the gradience of neutrality (the height effect) and the variability in Hungarian harmony. Moreover, I argue that this view of harmony is necessary beyond Hungarian and beyond front/back harmony: neutrality is crucially about the quality of a vowel as a potential target of harmony, where target quality is determined in a cross-linguistic, phonetically motivated way.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".