Arabic Loanwords in Tatar and Swahili: Morphological Assimilation
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Bibliographic record
Abstract
This article deals with the analysis of the morphological assimilation of Arabic loanwords into Tatar, Altai language family, and Swahili, Bantu language family. The urgency of this review is caused by the fact that the formation of both Tatar and Swahili was influenced by Arabic, which had profoundly influenced them in religious, scientific, cultural and economic aspects. In this paper we apply the comparative approach that is aimed at finding isomorphic and allomorphic features in the languages studied and identifying their peculiarities in the process of Arabic vocabulary assimilation. The morphological assimilation of Arabic loanwords into these languages is realized by verbal nouns, participles, nouns denoting place and action. One of the isomorphic features of the recipient languages is the absence of the category of gender both in Tatar and Swahili; among the allomorphic peculiarities are postposition of adjectives after nouns in Swahili and the use of compound verbs with Arabic nouns as their stems in Tatar. The results of the research will contribute to the loanword studies in these unrelated languages.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it