Morphosemantic features in Universal Grammar: What we can learn from Marshallese pronouns and demonstratives
Bibliographic record
Abstract
Abstract This article analyzes Marshallese pronouns and demonstratives, arguing that both privative and binary morphosemantic features are necessary, and that the two types coexist in a single domain. Marshallese encodes number with atomic, and person with [ $\pm$ author] and [ $\pm$ participant]. In the complex system of Marshallese demonstratives, atomic and [ $\pm$ human] map to the same head, subject to a constraint that only one feature appears at a time. The element $\chi$ , which derives person orientation in demonstratives and pronouns, does not universally map to the same syntactic position. While in Heiltsuk $\chi$ is a dependent of the person head, in Marshallese it heads a projection above the person head. And while in Heiltsuk the person features occupy the same position in both pronouns and demonstratives, Marshallese pronouns have a different structure, with person and number features mapping to a single syntactic head. The contribution of UG is thus not a set of specific features or specific structures, but a set of more abstract principles.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".