Feature Geometry and Head Splitting in the Wolof Clausal Periphery
Why this work is in the frame
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Bibliographic record
Abstract
This article is a study of the morphosyntax of the clausal periphery in Wolof, specifically the two layers commonly labeled CP and IP. It has long been noted that (a) C and I share a number of properties, and (b) languages differ in the amount of structure over which functional features are distributed. I propose a structure-building mechanism that can both explain the C-I relationship and derive the variation in the distribution of features over syntactic heads. I argue that features of C and I are bundled together and that this feature bundle can be divided into multiple heads via Head Splitting, which allows parts of feature bundles to reproject. The proposal is illustrated through a detailed exploration of the C-I domain in Wolof, which bundles C and I into one head in some structures and splits them in others.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it