On the margins of perception - TO-clauses: a standard construction of perception verbs?
Bibliographic record
Abstract
The main objective of this corpus-based study is to provide an account for the fact that, contrary to what some grammars postulate, TO-infinitive clauses can be – and are – used in the complementation of perception verbs in the active. The analysis seeks to answer the underlying question of whether the norms or usages mentioned in prescriptive or descriptive grammars influence the way speakers use such constructions (perception verb + NP + TO-infinitival), while confronting these norms and usages to evidence provided by attested examples. Three varieties of English – British, American and Canadian English – are thus compared so as to identify: how frequently TO-infinitivals occur as complements; which verbs take this type of complement; and in which variety and in which register they are frequently used. It is shown that the utterances provided by the corpora contradict the norms that are prescribed or described in grammars. The study puts forward a semantic explanation as to the (in)compatibility of perception verbs with TO-infinitivals, partly based on the types of these verbs. It also demonstrates that the sentences sometimes convey a meaning of sensory perception, even if an interpretation of mental judgement or inference – often mentioned in grammars – is more frequent.
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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 | 0.007 |
| 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 | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".