Bibliographic record
Abstract
Arabic Subject Markers are interface phenomena (spe-cifically between morphology and syntax). In this pa-per, I describe them briefly, I give my linguistic ana-lysis within the framework of the Head-Driven Phrase Structure Grammar and I show how I implement them in the LKB system. I show that this system, despite its strength, does not allow for a proper implementation of these units. Standard Arabic (henceforth Arabic) Subject Markers (henceforth SMs) are morphemes that convey information on the gender, number and the person of subject or topic1. They are attached to perfective and imperfective verbs and respect well-defined morphological patterns. They are suf-fixes when attached to perfective verbs and prefixes and / or suffixes when attached to imperfective verbs. Two examples of these units are provided in (1) and (2). In (1) the SM-ta, which encodes the features 2MS, is attached to the perfec-tive stem katab "wrote". In (2), the SM ti: , which encodes the features 2FS, is attached to the imperfective indicative stem ktub "write". (1) katab wrote-ta-2MS
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.023 |
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".