The lexicon in Functional Discourse Grammar: Theory, typology, description
Bibliographic record
Abstract
Abstract This paper discusses the treatment of the lexicon in Functional Discourse Grammar (FDG) and serves to provide a general introduction to the theoretical framework and its formalizations, in particular for readers who may not be intimately familiar with it. After outlining the general architecture of the model, we discuss the position, content and function of the FDG lexicon in more detail. The FDG lexicon is often called the Fund, as it contains more than just a collection of lexemes. The Fund is conceived of as a storehouse containing all unpredictable linguistic knowledge in the form of various types of primitives. In addition to a lexicon proper this includes structural and grammatical primitives that feed the grammar, such as: pragmatic and semantic frames, functions and operators; morphosyntactic and phonological templates and operators; and suppletive forms. The “lexicon proper” contains grammatical morphemes and suppletive forms in addition to lexemes; the collection of frames and templates is sometimes called the “structicon”; and operators and functions constitute what may be called the “grammaticon”. The division of labor between the Fund and the Grammar is illustrated by showing how FDG treats lexeme, word and frame formation: lexeme formation is located in the Fund, word formation is located in the Grammar, and frame formation may be located in either, depending on the particular frame or the approach of the analyst. We then discuss the form and content of lexical entries. This has been a topic of some discussion recently, and several of the contributions to this special issue contain proposals in this area. The central question here is how best to capture the existence of common or even default associations between primitives at different levels of representation while still allowing for the occurrence of mismatches. Mismatches allow us to account for phenomena like coercion and other creative uses of the linguistic apparatus available to the language user. Next we address the construction of lexical meaning, showing where FDG draws the line between semantics on the one hand and pragmatics, contextual factors, and conceptualization on the other hand. Here again, different points of view coexist and several contributions contain proposals for how to represent lexical meaning. Our final section briefly introduces the other contributions to this special issue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".