Feminization of Migration from Rural Punjab: Extent, Factors and Consequences
Bibliographic record
Abstract
The study assessed the trends, causes, and consequences of female migration from rural Punjab. Findings revealed that 13.34 per cent of rural households had at least one migrant member. The analysis of gender factors, including age, education, visa type, year of migration, and destination country, indicated a shift towards the feminization of migration. The student visa route has transformed the roles of women, shifting them from being subordinate and dependent to independent and empowered. Since 2015, females (80.1 per cent) have outnumbered males (70.2 per cent) in migration, with 64.3 and 33.7 per cent of females and males migrating on student visas. Nearly 75 per cent were young (under 30), well-educated, and primarily moved to Canada (64.4 per cent). Low income, unemployment, governance issues, and drug abuse drove youth away. Strong political will, economic reforms, and awareness campaigns could help regulate this migration trend.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".