Fethiye’de Yaşayan İngilizlerin Türkçe Kullanımları ve Türkçeye Yönelik Tutumları
Bibliographic record
Abstract
In recent years, people from different countries have migrated to Turkey. They started \nto learn Turkish in order to get by which in turn has increased the number of bilinguals \nin Turkey. Langugae use and bilingualism have been studied in many respects in the \nUSA, Canada and Germany. However, the number of studies on language use and \nbilingualism in Turkey is very few when compared to these countries. \nAfter 2000, many English people started to live in Fethiye for different reasons. Those \npeople began to learn Turkish which resulted in the appearance of adult English-Turkish \nbilinguals in society. In this thesis, Turkish language use of adult bilinguals and their \nattitudes towards Turkish have been studied. Beforehand, 15 participants who were \nbelieved to serve the purpose of the study were determined and then they were \ninterviewed. In the interviews, those participants were asked questions about their \napproaches and attitudes towards Turkish. Interviews were recorded with a tape \nrecorder and then they were transcribed. \nIn this descriptive study, by taking the socio-linguistic variables into consideration it is \naimed to identify grammatical deviations of adult English bilinguals from standard \nTurkish, determine their approaches and attitudes towards Turkish and find out how the \ngathered data can serve to teaching of Turkish as a foreign language. \nAs a result of the analysis it was found out that the participants made phonological \nmorphological, lexical and syntactic copies from English.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".