Question Affix Analysis in Standard Arabic: A Minimalist Perspective
Bibliographic record
Abstract
The study examines question affix analysis in Standard Arabic within the minimalist framework of Chomsky (1995, 1998, 1999, 2000, and 2001) and shows how Standard Arabic is different from English in terms of feature strength, feature checking, and I-raising to Q (i.e., raising of the head INFL to the head COMP). The objective is to present a unified treatment of question affix analysis in Standard Arabic and illustrate to what extent possible the Arabic data interacts with Chomsky’s minimalist analysis. It also demonstrates how feature licensing takes place in the right checking domains in the derivation of yes-no questions. It points out that Standard Arabic resorts to ‘Merge’ because it does not have auxiliary inversion, while English resorts to ‘Adjunction’ because of auxiliary inversion. Besides, question particles in Standard Arabic are viewed as merely morphological affixes placed sentence-initially to form yes-no questions. Furthermore, we argue that the interrogative particles in Standard Arabic have one function (that of showing interrogativity) since they do not stand for any DP, PP or AP argument. Given this, we propose that the question particles in Standard Arabic are base-generated in the head C position of CP, since they never undergo any morpho-syntactic movement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".