1. Asymmetry in English multi-verb sequences: A corpus-based approach
Bibliographic record
Abstract
The English V and V construction provides an ideal opportunity to study asymmetry in the properties of the verbs which enter into each of the two verb slots of this construction. This paper explores the asymmetry evident in this construction by utilizing two corpora of spoken and written New Zealand English (the Wellington Written Corpus and the Wellington Spoken Corpus). The most striking asymmetry which emerges from the corpus data is the dominance of motion verbs and change of position verbs as V1 and their absence in V2. The V2 position shows a preference for verbs referring to activities involving a stationary position. The corpus leads us, therefore, to recognize move (in order) to do as the primary meaning associated with the V and V construction. While speakers of English may sense that this meaning is commonly associated with the V and V construction, only a corpus-based study such as this one is able to quantify the degree to which this meaning is, in fact, present in this construction. The paper also considers the nature of the semantic integration associated with the construction. Examples such as go and tell and go and visit , though superficially similar, illustrate different kinds of semantic integration. Coordinated verbs, in general, present a typical grammaticalizing context, exemplified by the try and V construction in English, as well as examples such as go and prove me wrong typical of spoken language.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| 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".