Development, Immigration, And Social Harms of Iranian Small Towns: A Case Study
Bibliographic record
Abstract
Immigration is compelled by social, political and economic factors. One reason for immigration is claimed to be seeking better future. Then the mentioned transitions could be daunting, affecting social marginalization, loss of social networks, health care access issues and adverse health consequences, including depression and anxiety. It is claimed that immigrants encounter challenges while acclimatizing to their new country and a majority of them might be influenced by the process of immigration. It is claimed that the Islamic revolution, political changes, war, and sanctions from the United States of America have obliged many Iranians to flee their homeland over the last three decades and social harms of this immigration; especially through Iran was required to be studied. This research was a survey conducted in Qiamdasht which is a small town in Ghaniabad Rural District, in the Central District of Rey County, Tehran Province, Iran. In this study, systematic random sampling was applied, resulting in 245 participants to be interviewed and respond to the questionnaires. The design of this research included Survey Research and Ex-Post Facto. Since this was a survey in which the selected families were investigated in terms of economic, cultural and social aspects, interviews and questionnaires were used. To measure the dependent and independent variables through questions, a questionnaire in 8 pages containing 69 open-ended questions on 9-point Lickert scale was established. The data were transferred to SPSS version 21 for further analysis. The results revealed that Immigrants observe the codes of ethics less than the others. Immigrants have lower Socio-economic status. Immigrants play a smaller role in solving the social affairs. Immigrants own low-level desires and wishes. Immigrants apply rationality in their life affairs less than the others. By improving their socio-economic status, immigrants better observe the codes of ethics, their rationality improves, their social mobility improves, their role in social affairs improves, moreover, it was found that by improving their economic development, qualitative development increases and finally immigration rate was found to have a diverse relationship with qualitative development.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".