Bibliographic record
Abstract
Abstract. The current paper compares Sakha and Turkish through the lens of Distributed Morphology (Halle & Marantz 1993) and outlines the structural differences and similarities between their canonical and impersonal passive constructions. Turkish argument structure has attracted lots of attention in the literature due to the unexpected patterns it exhibits in the domain of passive and impersonal constructions, such as double passives and passives of unaccusatives, which pose a problem for the Unaccsuative Hypothesis (Perlmutter 1978). To account for these structures, different previous approaches have argued that Turkish passive morphemes can in fact function as the overt realization of an argument in synthetic impersonal constructions (Dikmen et al. 2022, Legate et al. 2020). Building on this, I propose a new approach whereby these impersonal arguments are introduced by the general argument-introducing head i* (Wood & Marantz 2017), which allows for a more flexible account of passives and impersonals in Turkic and possibly beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".