Bibliographic record
Abstract
The grammatical gender system is considered one of the most challenging structures that L2 learners must acquire.Part of this difficulty lies in the complexity of the system itself, and also from the fact that this system is one of the significant areas in which languages differ.Arabic is a language that has a rich grammatical gender system.It is comprised of two gender classes -masculine and feminine -that can be applied to nouns, verbs, adjectives and pronouns.The present study investigates the acquisition of subject-verb gender agreement in Arabic.The participants were adult L2 learners of Arabic with different native language backgrounds at two different levels of proficiency, as well as native speakers of Arabic.The participants were divided into three groups: the first group consisted of learners who have a grammatical gender system in their L1; the second group consisted of learners who do not have a grammatical gender system in their L1; and the third group consisted of native speakers of Arabic serving as a control group.I am also indebted to many others without whom this work could not have been possible.With the outmost respect and appreciation, I thank my supervisor, Professor Kumiko Murasugi, for inspiring and encouraging me, as well as for generously offering me her valuable time.I am also eternally grateful for the continuous support of my beloved uncle, Abdulrahman, who has imparted me with a wealth of invaluable guidance throughout my academic journey.My most sincere gratitude extends to my family members and friends, and particularly my parents and
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".