Infinitival clauses with dative subjects: goal-oriented directedness in space and time
Bibliographic record
Abstract
Abstract Infinitival clauses are known to represent a caseless domain for the subject. Nevertheless, Russian is often cited as an exception to this property. It has a so-called “dative-infinitive construction” (DIC), in which an overt subject appears in dative case. Dative morphology also appears in certain control environments, resurfacing on a semi-predicate, which has been taken as evidence of case presence on PRO. This paper scrutinizes various types of DIC and proposes their unified analysis, relying on two theoretical tools: the framework of Distributed Morphology and the Universal Spine Hypothesis. Examining the building blocks of the infinitival clause in Russian, this paper argues against a covert-modal hypothesis. The dative case is attributed to a to-like functional head, Goal, which anchors the infinitival clause to a contextually salient point in time or a world of evaluation. Within the clausal spine, GoalP can either immediately dominate VoiceP or be immediately dominated by CP. The proposed analysis builds upon the concept of “goal-oriented directedness”, borrowed from the cognitive-functionalist literature and formalized in a generative perspective. Application of this analysis to control environments leads to a conclusion that two types of infinitival domains should be differentiated in Russian: full-fledged (GoalP-containing) CPs and bare infinitival phrases.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".