Resultative constructions : cross-linguistic variation and the syntax-semantics interface
Bibliographic record
Abstract
This thesis examines constructions known as resultative constructions. In addition to the well-known adjectival resultative construction in English, I investigate the resultative V-V compound, found in Japanese, and the resultative serial verb construction, found in Edo. I propose a new classification of these constructions, which focuses on the argument structure of the construction. In Japanese resultative V-V compounds, the argument structure of a compound reflects the argument structure of the second verb only, while in Edo, the argument structure of the construction reflects the argument structure of both verbs involved. With this criterion, English resultative constructions are divided into two classes---a resultative construction containing an intransitive verb is classified with Japanese resultative V-V compounds, and a resultative construction containing an object-selecting verb is classified with Edo resultative serial verb constructions. Based on the classification provided here, I investigate two types of syntactic operations which license the concatenation of the predicates in resultative constructions. I argue that English intransitive resultative constructions and Japanese resultative V-V compounds are formed by adjoining one of the predicates on the other. The adjunction structure is then interpreted as conjunction called event identification. In contrast, English transitive resultative constructions and Edo resultative serial verb constructions are licensed by treating one of the predicates as a causative predicate. I argue that one of the predicates in these constructions undergoes lexical coercion, and acquires a causative meaning. The newly-formed causative verb takes the other predicate of the construction as its complement. This structure is then interpreted with function-application. I hence argue that the structural difference between the two types of resultative constructions also mirrors the difference in the type of semantic operations used to interpret these constructions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".