Bibliographic record
Abstract
This paper provides a unified syntactic account of the distribution of Englishhavein causative constructions (e.g.John had Mary read a book) and experiencer constructions (e.g.John had the student walk out of his classroom). It is argued thathaveis realized in the context of anapplicative head(Appl) and an event-introducer v, regardless of the type of v.Haveis spelled out in the causative when Appl merges under vCAUSE, and in the experiencer construction when Appl merges under vBE. This proposal is extended tohavein possessive constructions (e.g.John has a hat/a brother):haveis realized in the context of vBEand Appl. The proposed account provides empirical evidence for expanding the distribution of Appl: (i) a causative can take ApplP as a complement, which was absent in Pylkkänen's (2008) typological classification, and (ii) Appl can merge above Voice, contrary to Pylkkänen's analysis in which Appl is argued to always merge below VoiceP, never above. Moreover, the proposed account supports the theoretical claim that argument structure is licensed by functional syntactic structure; in particular, it shows that the relevant functional heads are not aspectual heads, but Appl and v.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".