Bibliographic record
Abstract
Abstract We find vowel harmony systems in many non-standard varieties of modern Armenian. It has been speculated that these may have acquired vowel harmony due to contact with Turkic varieties ( Scala 2018 ). On the basis of an exploration of the synchrony and typology of Armenian vowel harmony, consideration of historical changes that could have caused harmony to develop, and evaluation of new data bearing on the origins of backness and rounding harmony in Oghuz, we propose that the vowel harmony systems of the modern Armenian dialects show evidence of having been influenced by Turkish, but the numerous differences between Armenian and Turkish vowel harmony point against a straightforward copying of the Turkish phonological system. We theorize that vowel harmony in Armenian arose due to a combination of language-internal and ‑external factors: vowel shifts in some Armenian dialects, alongside universal analytic and channel biases, provided the necessary preconditions for the development of vowel harmony by the 11th century AD, prior to the arrival of Turkic speakers in the Armenian homeland. Extensive contact with Turkic vowel systems may then have encouraged the phonologization of this assimilation process, but in strikingly different ways than are found in Turkic languages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".