The <i>Done</i>‐State Derived Stative: A Case Study in Building Complex Eventualities in Syntax
Bibliographic record
Abstract
Abstract A basic question for theories of the syntax–semantics interface is whether the relationship between the form and meaning of complex aspectual expressions is mediated by the pieces that make up hierarchical syntax or whether complex forms and their meanings can pair directly, in templatic or constructional representations, for example. This paper examines the string I’m done writing chapter 3 with this issue in mind. The (be) done V‐ing structure does not seem to have been analyzed before, and I refer to it as the done state. The done state expresses a complex stative eventuality. Notably, the transitive object of the done state can hold a target state, an unexpected interpretation given the ‐ing verb (contrast I’ve been writing chapter 3). Apparent form–meaning mismatches of this type are regularly taken as evidence for listed, constructional mappings. I show, however, that the done state has the syntactic pieces of a stative passive of a present participle, and I argue that the done state is a previously unnoticed type of derived stative. The analysis provides a compositional account of the structure’s semantics and morphology. I further argue that the syntactic items that make up the done state have the same properties that they have elsewhere in the grammar; there is no need to postulate new grammatical objects. The predictable properties of the done state find explanation in models in which complex eventualities are built up out of the minimal units that make up complex phrases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".