Adaptation of Turkish Loanwords Originating from Arabic
Bibliographic record
Abstract
This study investigates the phonological and morphological adaptation of Turkish loanwords of Arabic origin to reveal aspects of native speakers’ knowledge that are not necessarily obvious. It accounts for numerous modification processes that these loanwords undergo when borrowed into Turkish. To achieve this, a corpus of 250 Turkish loanwords was collected and analyzed whereby these loanwords were compared to their Arabic counterparts to reveal phonological processes that Turkish followed to adapt them. Also, it tackles the treatment of morphological markings and compound forms in Turkish loanwords. The results show that adaptation processes are mostly phonological, albeit informed by phonetics and other linguistic factors. It is shown that the adaptation processes are geared towards unmarkedness in that faithfulness to the source input—Arabic—is violated, taking the burden to satisfy Turkish phonological constraints. Turkish loanwords of Arabic origin undergo a number of phonological processes, e.g., substitution, deletion, degemination, vowel harmony, and epenthesis for the purpose of repairing the ill-formedness. The Arabic feminine singular and plural morphemes are treated as part of the root, with fossilized functions of such markers. Also, compound forms are fused and word class is changed to fit the syntactic structure of Turkish. Such loanwords help pave the way to invoke latent native Turkish linguistic constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".