The Effects of Terrorist Activities on Development in the Southeastern Region of Turkey-Theoretical and Empirical Application
Bibliographic record
Abstract
Terrorist activities affect and continue to cause social, political, cultural and economic problems for Turkey just as they do to many parts of the world. Investors would prefer to move their capital into safer regions due to the problem of terrorism and this affects the distribution of development. This study, aims at demonstrating the extent at which terrorism has affected development in the South-eastern Anatolia region of Turkey. This study will look at the investment volumes in 9 provinces located in southeast Turkey. We will also look at terrorist activities in these provinces as well as discussing the relationship between investment preferences and terrorism. Firstly, we will look at terrorist incidents in these provinces, the number of provinces affected by terrorist activities, number of people dying from terrorist related activities, state and industrial investments as well as determining the number of industrial workers in these provinces. For this purpose, as a case study, we will investigate investments in Gaziantep which is a city located in the Southern eastern Anatolia region and the sixth largest city in Turkey with a lot of private investments. In this study, a questionnaire was administered to ninety-three (93) big companies who are doing foreign trade with at least one country. The questionnaire administered was easy and used a detailed cross-question analysis. According to the study, it was discovered that there is an inverse relationship between the private investment demand and the frequency of terrorist incidences and then this relationship was discussed in detail.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".