A Qualitative Research of the Causes of Iranian Female Students Immigration to Developed Countries
Bibliographic record
Abstract
According to the latest data from United Nations population Fund-UNFPA-in 2015, 244 million people, or 3.3 per cent of the world's population lived outside their country of origin. A number of these migrants are skilled and educated women. This movement usually occurs from developing to developed countries in the world like the US, the UK, Germany, England and so on, to achieve new opportunities and a better life. Iran is a developing country, which is suffering from this serious issue. The aim of this study is to identify the causes of Iranian female students' immigration to developed countries from the perspective of female students of Al-Zahra University. The present qualitative study included 20 master and doctoral female students who were completing their degree programs at this University, through purposive sampling. Data was collected via in-depth, semi structured interviews which were audio-recorded and analyzed by Content analysis method. The main themes and sub-themes were “Economic” (including Unemployment, Low income, Inconsistency between field of education and jobs, Gender discrimination in employment and payments delays), “Educational” (Lack of proper facilities in university, professors' lack of knowledge, lack of public respect for well-educated people in society, and Women’s restrictions in selecting certain academic disciplines), “Socio-political” (Limitation of individual freedom, political pressure, Lack of freedom of speech) and “Personal and Familial” Issues (marriage and parental related factors). The findings present a deeper understanding of the main causes of female migration and why these educated women are less likely to return to Iran.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".