Bibliographic record
Abstract
This paper discusses the word order properties of object nouns and adverbs in Mongolian. As is well known, the accusative case marker appears only sometimes on the object. I review Guntsetseg’s (2016) in depth discussion on differential object marking and pseudo noun incorporation in Mongolian and present some prior work on the prosody of these two constructions (Barrie and Kang, 2022). I show that a caseless non-specific object can be separated from the verb by at most a low VP-adverb. A case-marked or specific, caseless object cannot appear between the verb and a low VP-adverb. Furthermore, a case-marked or specific, caseless object can appear above a higher, temporal adverb, but a caseless, non-specific object cannot. I analyze these facts within a Contiguity Theoretic framework (Richards, 2016) starting with the premise that a caseless non-specific object is an nP and that case-marked or caseless, specific object is a full KP. I argue that an nP object must be contiguity prominent with its selector, the verb, only and that a KP object must be contiguity prominent first with the verb and then with v, which assigns it accusative Case. I show that maintaining contiguity prominence gives rise to the patterns discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".