The Relation Between International Movements and Development: Analysis of Cities in Turkey
Bibliographic record
Abstract
This study aims to analyse the extent to which international mobility impacts the socioeconomic development of cities in Turkey.Firstly, a 27-variable principal component analysis was applied to determine the development index of the cities.The socio-economic development of the cities is classified into six categories.Primarily international mobility, international capital, and population mobility have been evaluated two-dimensionally within the accessible national data covering the years 2018-2019.In this study, the effect of the variables of international mobility on the cities' socio-economic development was identified through multivariate regression and geographic weighted regression (GWR) analysis.Global (OLS) and GWR analyses allow us to investigate the impact of and the relationship between international mobility on socio-economic development.GWR model, which can give placebased regression results and additionally the number of companies with foreign capital, the number of houses sold to foreigners, the number of incentive certificates issued to foreigners, the number of foreign workers, the number of foreigners granted residence permits, and the number of international students were used as independent variables.International capital mobility has a meaningful and positive relationship with the socio-economic development index.The variable of the number of international students used as a part of international population mobility does not have a meaningful effect on socio-economic development index (SEGE).Overall, international mobility has a positive impact on the level of socio-economic development of the cities.However, given the geographical distribution of international capital and population movements in Turkey, western and southern regions seem to have a higher mobility level than the rest of the country.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".