Causative/Inchoative Verb Alternation in Altaic Languages: Turkish, Turkmen, Nanai and Mongolian
Bibliographic record
Abstract
The purpose of the study is two-fold. First, a statistical analysis of the morphology of causative/inchoative verb alternation is carried out in Japanese and in 13 Altaic languages, i.e., Turkish, Turkmen, Nanai, Khakas, Udihe, Uzbek, Sakha, Manchu, Kyrgyz, Mongolian, Kazakh, Ewen, and Azerbaijani. The findings reveal that causative/inchoative verb alternation (a) can be realised via the insertion of an infix (‘-uul-’, ‘-e-’, ‘-g-’, etc.); (b) can be inchoative root-based, with transitive verbs derived via attaching a suffix to the inchoative verb roots (‘-dur-’, ‘-t-’, ‘-ir-’, ‘-dyr-’, ‘-wəən-’, ‘-buwəən-’, ‘-r-’, ‘-wənə-’, ‘-nar-’, ‘-ier-’, ‘-er-’, ‘-bu-’, ‘-ʊkan-’); (c) can be causative verb-based, with inchoative verbs being derived via attaching a suffix to the causative verb roots (‘-p-’, ‘-n-’, ‘-ul-’, ‘-il-’); and (d) can be realised via consonant alternation (‘-r-’ (transitive) / ‘-n-’ (intransitive); ‘-t-’ (transitive) / ‘-n-’ (intransitive)). This study further attempts to pin down the affiliation of these languages with the Japanese language. It compares the morphological findings with Japanese bound morphemes in causative/inchoative verb alternation and then delves into the phonological issues, i.e., consonant alternation and vowel harmony. A proposal is put forward: phonologically and morphologically, Japanese has a good deal of resemblance to the 13 Altaic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".