Bibliographic record
Abstract
Abstract This chapter proposes a framework for a quantitative approach to the typology of polysynthesis based in Functional Discourse Grammar (FDG). FDG’s strict separation between the Interpersonal, Representational and Morphosyntactic Levels of analysis combined with its approach to morpheme types allows for a detailed examination of the scalar nature of polysynthesis. The analysis refines FDG’s treatment of morphological typology, which characterizes languages according to two parameters, viz. transparency and synthesis. Inspired by recent FDG treatments of transparency (esp. Leufkens 2015 ; Hengeveld & Leufkens 2018 ), and building on FDG work by Fortescue (2007) and Smit (2005) , I propose the following set of parameters: (1) (verbal) lexical density (qualitative and quantitative); (2) anisomorphism between Formulation and Encoding levels; (3) anisomorphism within the Morphosyntactic Level (word-internal layering in the verbal word); (4) alignment restrictions; (5) optionality (availability of analytic alternative). This quantitative approach is intended to complement the qualitative typology developed by Mattissen ( 2004 , 2017 ).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".