The Effects of English Language on the Kurdish Language: A Study of the Interacting Terms
Bibliographic record
Abstract
The descriptive method is used in this research on English terms adopted into Kurdish, focusing on the influence and interaction between languages.Then, it explores the many types and forms of English phrases taken.This research looks at the parallels and differences between the Kurdish and English languages.The study is centred on how individuals employ English words and phrases while speaking Kurdish.The first section of this inquiry focuses on "linguistic linkages and effects," a topic that is the tackled front.It emphasizes the most crucial parts of translating words and sentences.The second part will concentrate on the impact of the English language on the Kurdish language.It is made up of the three sections listed below: To get things started, let us look at a few different English phrases and discuss how their pronunciation should differ from one another.Second, the Kurdish and English word sequence is always the inverse of what it should be.This is something that is always done, no matter what.The majority of Kurdish intellectuals and linguists often employ English terminology.Finally, we will present a summary of the findings, emphasizing the importance of the study, the problems highlighted in the article, and some implications and suggestions written as an attempt to fix those issues.In this research, English-to-Kurdish loanwords are imports and substitutes.Imported terms have comparable pronunciations and meanings to the receiving language versions.Substitution patterns are donor-to-recipient language word changes.The linked wordlist contains mostly assimilations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".