Türkiye’deki Suriyeli Sığınmacıların Sosyo-Ekonomik Yaşama Etkileri: Fayda Maliyet Ekseninde Bir Bakış
Bibliographic record
Abstract
Since the last quarter of the twentieth century, Turkey has been exposed to different waves of migration after certain regime changes in some of the countries within close proximity to it. In some countries in the Middle East and North Africa regions, anti-regime actions eventually turned into conflict; After these conflicts, also known as the Arab Spring, an influx of immigrants and asylum seekers/refugees started to emerge from these countries to our country. Turkey has been forced to host millions of Syrian refugees in a short space of time due to growing internal turmoil in neighbouring Syria and due to its open-door policy towards people fleeing from other countries. The uncertainty of the deployment of these refugees in Turkey, not to mention the uncertainty surrounding the duration of this situation, has led to the emergence of new problem areas in Turkey’s socio-economic life. This article examines the benefits and costs of Syrian refugees to our country’s social and economic life. In this context, the aim of this study is to demonstrate the effects of Syrian refugees on the socioeconomic life of Turkey, which has been involved in a mass migration movement for the first time in its history. Our primary aim in this study will be to examine the legal status of Syrian refugees and then the positive and negative effects of the refugee situation on education, health, safety, employment and the economy will be shown.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".