<i>To</i>‐Infinitive and Gerund‐Participle Clauses with the Verbs <i>Dread</i> and <i>Fear</i>
Bibliographic record
Abstract
Abstract This paper investigates the variation between to‐infinitive and gerund‐participle complements with the verbs dread and fear. Specifically, it aims to explain the reasons underlying complement selection with these two verbs as well as a number of semantic effects which arise with these complement clause constructions. The approach taken here argues that an explanation of these constructions must be predicated upon an analysis of the semantic content of the items of which each one is composed. Three factors are proposed as being relevant to explaining the issues: (1) the meaning and function of the gerund‐participle, (2) the meaning and function of the to‐infinitive, and (3) the meaning of the main predicate. The findings of this study are in line with those of previous studies which have applied the same approach. This paper is intended as a small contribution to ongoing efforts to crack the complementation issue in English.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".