Roots, their structure and consequences for derivational timing
Bibliographic record
Abstract
Abstract Recent work in Distributed Morphology, most prominently Harley (2014), argues for roots being able to take syntactic complements, which opens the door for the possibility of having syntactic features within a root’s representation – something most DM literature rejects (Embick 2015). Upon a closer inspection of the arguments presented in the literature, it is not clear whether the disagreement has an empirical underpinning, or whether it stems from the lack of methodological clarity as far as the identification of the precise nature of what constitutes a syntactic feature. This paper takes this methodological question seriously and investigates a type of derivational behavior that, in our view, provides a decisive argument for the presence of syntactic features on roots. We argue that the presence of a syntactic feature on the root can be conclusively established based on a feature’s impact on specific properties within a larger syntactic structure. Based on empirical evidence form gender agreement phenomena, we introduce a model of grammar that distinguishes roots with syntactic features from those which do not have them. We propose that such a distinction between roots will manifest itself in the timing of root insertion – roots without syntactic features are late inserted, while roots with syntactic features must be early inserted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".