Bibliographic record
Abstract
Argument structure In the previous chapter, the interface between morphology and syntax was considered, including the relationship between features of items drawn from the lexicon and abstract features in the syntactic representation. The present chapter explores other properties of the L2 lexicon, particularly the relationship between lexical semantics, argument structure and syntax. The concern is with how certain aspects of meaning (the semantic primitives by which word meanings can be expressed, the event types expressed by verbs, the thematic roles of arguments) are realized in syntax, as well as the morphological forms by which such meanings are expressed. Detailed investigation of L2 argument structure in the generative framework is relatively recent. In this chapter, the following issues will be discussed: (i) semantic constraints on argument-structure alternations; (ii) crosslinguistic differences in how semantic primitives may combine or conflate; (iii) thematic properties of arguments and how they are realized syntactically; (iv) the effects of morphology which adds or suppresses arguments. We begin with a consideration of the kind of information that is encoded in a lexical entry and how this information is mapped to the syntax. Lexical entries Lexical entries include distinct types of information, semantic and syntactic (e.g. Baker 1997; Grimshaw 1990; Hale and Keyser 1993; Jackendoff 1990; Levin and Rappaport-Hovav 1995; Pinker 1989). At one level, sometimes referred to as lexical conceptual structure (LCS) (Jackendoff 1983, 1990) or the thematic core (Pinker 1989), meaning is represented, particularly aspects of meaning that have consequences for other areas of the grammar.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.028 |
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".