Bibliographic record
Abstract
This study discusses the adaptation of loanwords in Persian from donor languages of Arabic, French and English. The main claim is that loanwords adapt to the host language phonology based on the duration of the time they have been used in that language (Kemmer 2017). Thus, I propose that Arabic loans which are borrowed earlier are more nativized than recent loans, like Russian, French and English words. Compared to the former studies (Shademan 2002; Perry 2005, 2011; among others), this is the first account that aims to show the hierarchical and gradual nativization process of loans in Persian by use of Core-Periphery model (Itô and Mester 1999). This model categorizes words from core stratum to periphery stratum based on the satisfaction of constraints in the host language as systematic comparison criteria of older loans with recent ones. In this study I show that the Persian lexicon is stratified into three strata: core, middle and periphery. This lexicon stratification reflects the gradual nativization of loanwords where the core stratum includes frequent native items, the middle stratum includes older loans (Arabic) and the periphery stratum includes recent loans (Russian, French and English). Furthermore, this paper shows that highly frequent Arabic loans satisfying all constraints have become completely nativized and thus are categorized as part of the core showing the great influence of the donor language.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".