Bibliographic record
Abstract
This study aims to analyze and describe infinitive sentences with verbs of volition such as desire, wish, want, hope, intend, promise, determine, command in Early Modern English within a generative framework. It is argued that the infinitival clauses have a thematic subject PRO controlled by the matrix subject. It is proved that complex sentences with infinitive complements of matrix predicates of volition obtain subject control function. The findings show syntactic peculiarities of infinitive complementation of monotransitive verbs of volition as subject control infinitive constructions in the studied period of English. Having taken into consideration subject control properties of matrix verbs of volition, direct object monotransitive infinitive function, complementary nature of infinitives, it has been assumed that an infinitive clause generates in a complementizer phrase CP domain, putting forward three possible variants of syntactic analysis of the infinitive types’ configurations as: SVOd (to / bare INF clause), SVOd (NP to / bare INF clause), SVOd (wh- to INF clause) with ‘two-argument arrangement’ of matrix verbs and the infinitive clause as an object predicative complement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".