International Migrants from Rural Punjab—Attributes and Characteristics
Bibliographic record
Abstract
International migration is a worldwide phenomenon, and it has influenced every economy in the world. Migration plays a significant role in the development of both home and host countries. Remittances sent by non-resident Indians (NRIs) to the country of origin improve the standard of living of their family members and are also helpful in solving the balance of payment problems of the country. This research article deals with the socio-economic background of the emigrants’ households. The size of the family, housing conditions, age group, earning members in the family, number of migrated persons, etc., is the important variables of this analysis. To analyse the socio-economic characteristics of the migrants’ households and to examine the causes behind international migration are the main objectives of this study. It is observed that prime reason behind the international migration from Punjab to other countries is the lack of suitable work opportunities. This article is based on the sample of 375 households that have been selected from 15 villages of the two districts, namely Jalandhar and SBS Nagar. For the analysis of data, simple percentage method and binary logistic regression model have been used. The analysis and findings of the study revealed that a total of 579 NRIs have migrated from the 375 households, and all these households have their own homes. It is observed that the ratio of male migrants is much higher than the females. The study analysed that the proportion of dependent family members is almost same in both districts. It is also noticed that people of general category went to developed countries like the United States, Canada, Australia and New Zealand, but the OBCs and SCs preferred to go to the United Kingdom, Europe and Gulf countries. The main reasons of this difference are that the visa process of developed countries is more complicated and also contains huge costs, while visa process and cost of Gulf and other countries are simple and low, so OBCs and SCs can easily afford it. JEL Codes: F2, F22, D14, O15, O1
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".