The Morphosyntactic Interface of Determiner Phrases
Bibliographic record
Abstract
The functional category of determiners has undergone a number of representational changes in the last half century. Beginning with Abney in 1987 and as early as work by Brame (1981, 1982) and Postal (1966), linguists began to adapt the notion that determiners were a type of functional category with phrasal structure, and not specifiers of noun phrases. The flexibility allotted to this category to hold a significant role in syntactic structure has led to theories of feature and feature strength and the development of these features in first and second language acquisition. This paper seeks to review the current theories of syntactic structure of determiner phrases in English and universally. In particular, it examines one area of controversy regarding this category, namely nominal gender agreement, and how this affects applied areas of linguistics. Recent studies seem to favor specific transfer theories, however the default hypothesis that arises leaves much to be considered. From the discussion, we argue that gender feature agreement in L1 and L2 acquisition is distinct and merits further investigation, perhaps benefiting from the recent developments in the area of psycholinguistics.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".