Adaptation of Turkish Loanwords Originating from Arabic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.045 |
| 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