The Analysis of Factors Affecting the Residential Mobility of Afghan Immigrants Residing in Mashhad (Case Study: Municipality Regions 4, 5 and 6)
Bibliographic record
Abstract
The settlement of the immigrants, especially foreign immigrants in different cities and city areas has a major influence in shaping and changing socio-spatial structure of these areas. Mashhad has been the target of a large number of Afghan refugees in the past decades (160 thousand people). The initial settlement of immigrants in marginal areas of the city and residential mobility in the early settlement has obvious consequences on the social and spatial structure of different areas targeted by the immigrants. This study aimed to analyze the factors affecting the residential mobility of Afghan refugees residing in districts 4, 5 and 6 of Mashhad- Iran. The research was a survey type and the required data were gathered by field studies using questionnaires and library. The results of this study suggests that a major portion Afghan immigrant (86%) have been settled at the beginning of their arrival to Mashhad in marginal areas and slums including, Golshahr, Panj-tan, Ghaleh Sakhteman and Tollab. In the initial settlement of immigrants in the mentioned places factors such as proximity to fellow coreligionists and affordable rental housing prices are crucial. In terms of residential mobility, 45.7% of immigrant families have changed their location at least once in Mashhad. The highest residential mobility has taken place in the Golshahr areas (28.1%) and Panj-tan (28.1%). Family residential mobility between regions existed in smaller and restricted scale. The stated reasons and motives in relation to residential mobility of immigrants are different in the later stages after primary residence. Generally the factors of insecurity and lack of resources and utilities, improved financial condition and ability to buy a better house, ethnics and religion inconsonance and the tenant conditions are among the reasons stated by the refugees for changing their residence.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".