Infinitival and Gerund-Participial Catenative Complement Constructions in English World-Wide
Bibliographic record
Abstract
Previous research on non-finite catenative complementation (for example, start Ving/to V; force NP into Ving/to V) has largely been restricted to BrE and/or AmE. The present study seeks to expand the regional coverage of such research by analysing a set of catenative constructions in two large web-derived corpora, GloWbE and NOW, both of which comprise 20 subcorpora representing different national varieties of English. The implications of the findings for such diachronically relevant phenomena as colloquialisation and grammaticalisation are considered. For example, the dominance of bare infinitivals over to infinitivals with catenative help is suggestive of auxiliarisation, an interpretation supported by the semantically bleached sense of generalised causation associated with help, and historical evidence of support for the bare-infinitival variant in colloquial registers. Notable findings include American English epicentrality—and possibly hypercentrality—in many of the results, with Canadian English and Philippine English in particular sharing the American aversion to from-less “prevent NP Ving” and “help to V”; the occasional conservative tendency of the Outer Circle varieties to resist diachronic trends associated with the reference varieties (such as the rise of “fear Ving” at the expense of “fear to V”); and high scores for the African Englishes, suggested to be attributable to the popularity of “serial verb” constructions in a number of African languages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".