Tense and control interpretations in gerund-participle and to-infinitive complement constructions with verbs of risk
Bibliographic record
Abstract
Abstract This study investigates temporal and control interpretations with verbs of risk followed by non-finite complements in English. It addresses two questions: Why does the gerund-participle show variation in the temporal relation between the event it denotes and that of the main verb whereas the to-infinitive manifests a constant temporal relation? Why does the gerund-participle construction allow variation in control while the to-infinitive shows constant subject control readings? The study is based on a corpus of 1345 attested uses. The explanation is framed in a natural-language semantics involving the meanings of the gerund-participle, the infinitive, the preposition to, and the meaning-relation between the matrix and its complement. Temporal and control interpretations are shown to arise as implications grounded in the semantic content of what is linguistically expressed. It is argued that the capacity of a natural-language semantic approach to account for the data obviates the need to have recourse to purely syntactic operations to account for control.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".