The role of DO-auxiliary in subject-auxiliary inversion: Developing Langacker’s notion of existential negotiation
Bibliographic record
Abstract
Abstract This paper builds on Langacker’s (in press. How to build an English clause. Journal of Foreign Language Teaching and Applied Linguistics 2(2)) analysis of subject-auxiliary inversion (SAI) as involving “existential negotiation”. Langacker’s account is completed by relating it to full verb inversion (FVI). In FVI, non-core elements are fronted, resulting in inversion without an auxiliary, as in Into the room walked Mary ; however, non-core elements are also frontable in SAI, as in Bitterly did we regret our decision . Do is treated as denoting full actualization and SAI is accounted for by focus on an exceptionally intense mode of actualization, whence the use of do to explicitly express what is focused on. The role of into the room in the FVI example is to define a locus into which an entity is introduced. Since this does not involve focus on the fact or manner of the verbal event’s actualization, do is not used. This leads to a different division of inverted structures than that of Chen (2013. Subject auxiliary inversion and linguistic generalization: Evidence for functional/cognitive motivation in language. Cognitive Linguistics 24. 1–32), who distinguishes those that merely reverse subject and auxiliary (argued to denote non-indicative mood) from those where the inverted auxiliary-subject order is accompanied by fronting of a non-subject element (treated as involving focus on the fronted item). It is argued here that fronting do -auxiliary marks focus on the actualization of the verbal event itself.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".