Bibliographic record
Abstract
Abstract This article considers the treatment of causatives in Functional Discourse Grammar (FDG) in relation to questions regarding the division of labor between the Grammar and the Lexicon in Functional Discourse Grammar (FDG). The focus is on causative constructions in which the causativizer affixes to the verb in so-called polysynthetic languages. In this respect the article also contributes to the treatment of polysynthesis in FDG. The central question is whether the causative construction is a derived lexeme created in the lexicon and inserted into the grammatical structure as a single lexical unit, or whether it is created in the grammar as a synthetic construction involving two lexical units. The article describes the pertinent morphosyntactic and semantic properties of causative constructions in Blackfoot, a polysynthetic language belonging to the Algonquian language family. I show that such constructions contain two events, each with their own semantic properties including argument structure and modifiability. In FDG this is accounted for by analyzing the semantic configuration at the Representational Level of analysis as a complex Episode consisting of two States-of-Affairs, while analyzing the morphosyntactic configuration at the Morphosyntactic Level as a complex verbal Word containing two verbal Roots, one of which is the causativizer. The causativizer is analyzed as an independent verbal lexeme stored in the lexicon rather than as derivational morpheme. This analysis follows logically from the way in which FDG conceptualizes polysynthesis, namely as a morphological type which allows the presence of more than one lexical element within a single word.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".