Bibliographic record
Abstract
In Modern Mongolian, the subjects of many subordinate clauses, both complements and adjuncts, may be marked with the accusative case (von Heusinger, Klein & Guntsetseg 2011; Guntsetseg 2016). This study argues that the full empirical picture of these marked subjects necessitates an analysis based on Dependent Case Theory (Marantz 1991; Baker & Vinokurova 2010; Baker 2015), which additionally provides an account of case assignment more broadly in the language, including in differential object marking. Prior syntactic analyses (Bao et al. 2015; Fong 2019) rely on Agree-based case-licensing from v, resulting in ECM-like accounts. However, the appearance of accusative on subjects of adjoined clauses, as well as in clauses with no canonical accusative-assigning verbs (intransitives, passives) rules out v as the case assigner. Instead, following Baker & Vinokurova (2010), this account argues that accusative case is assigned configurationally. Once established that a configurational approach to case-assignment handles subjects, as well as direct objects, the approach is applied to Mongolian-specific issues including voice alternations and converbial adjuncts, showing that the theory predicts case-assignment patterns there. Finally, the study examines data from dative marking and scrambling in ditransitives to refine Baker & Vinokurova’s (2010) original theory, obviating the need for case-stacking by restricting the timing of application of the Dependent Case algorithm.
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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 | 0.002 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".