Bibliographic record
Abstract
Abstract This paper examines the syntactic structure of Arabic vocatives, focusing on case-marking of vocatives. The assignment of accusative and nominative-like case can be accounted for in the light of Hill (2017)’s proposal which provides the basic structure of the vocative phrase. This paper argues that in Arabic vocatives (i) the particle YAA is a transitive probe with valued [ ACC -Case] and unvalued [2nd] and [Distance] features; (ii) The D has the unvalued case feature [u-Case], and it has both the [2nd] and [+Distance] features if it is a free pronoun and (iii) The vocative noun carries the valued [2nd] and [+/-Distance] features. Based on these assumptions, I argued that indefinite vocatives are assigned accusative case only if they are merged with an overt D - n , otherwise a nominative-like case surfaces on the noun by default. Proper names have the same analysis since the presence of the indefinite article - n is a prerequisite for accusative case assignment. Concerning vocatives as heads of Construct States, N-to-D movement takes place in order to assign [+def] feature to D and is assigned accusative case by YAA . Regarding vocatives in demonstrative phrases, the existence of a null D prevents the vocative noun from being assigned an overt accusative case. Concerning vocative pronouns, only accusative case is assigned since the determiner carrying the [u-Case] feature is overt.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".