Arabic Loanwords in Tatar and Swahili: Morphological Assimilation
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".