Bibliographic record
Abstract
Abstract Lexical-Functional Grammar (LFG) is a theory of generative grammar. The goal is to explain the native speaker's knowledge of language by specifying a grammar that models the speaker's knowledge explicitly and which is distinct from the computational mechanisms that constitute the language processor. This chapter is organized as follows. Section 15.2 discusses the two syntactic structures posited by LFG: constituent structure (c-structure) and functional structure (f-structure). LFG distinguishes between formal structures and structural descriptions that well-formed structures must satisfy. The structural descriptions are sets of constraints. A constraint is a statement that is either true or false of a structure. Section 15.3 provides an overview of the most important sorts of constraints. Section 17.4 explains how c-structure and f-structure are related by structural correspondences. Section 17.5 describes the Correspondence Architecture. Section 17.6 considers some recent developments in LFG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".