The Cognitive Structure of Full-Verb Inversion and Existential Structures in English
Bibliographic record
Abstract
The goal of this study is to build on the Cognitive Grammar analysis of full-verb inversion (FVI) and existential structures proposed by Chen (2003, 2011 and 2013). Close attention will be given to two characteristics of these constructions not discussed by this author – lack of subject-verb agreement and the type of pronominal forms that occur in them – and their consequences for FVI’s cognitive structure will be worked out. Further parallels between FVI and the existential there-construction will be brought to light concerning the type of verbal predicate allowed, negation, transitivity, agreement patterns, presentational function, pronominal forms and heaviness of postverbal NPs. The cognitive structure of FVI with lack of S-V concord is argued to be: (1) ground-setter, (2) verb heralding presence/appearance of a generic third-person figure in the ground, (3) nominal identifying the generic figure. Chen’s Invertability Hypothesis is shown to generate false predictions with fronted adjectives and adverbials, and the claim that the preverbal element is in focus is shown to be problematic in the light of its usual status as given information. FVI is argued to be a construction in Goldberg’s (2006) sense of the term, although it does not constitute a meaning-form pairing which is completely independent of the lexical items that instantiate it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 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".