Predictive Modelling of Personal Remittances Received in India Using Machine Learning
Bibliographic record
Abstract
In 2022, India secured the highest remittances according to the World Migration Report, 2024 on account of migration.The present study examines the impact of net emigration and personal remittances received from abroad considering such factors as higher education enrolment and percentage of population using internet in India over more than two decades, spanning from 2000 to 2022.The distinctive contribution of the paper lies in its methodological use of regression, artificial neural network (ANN) and support vector machine (SVM) methods, using World Bank Development indicators for India.Primary results show a poor correlation of decreasing net emigrations with rising higher education enrolment and internet usage trends.However, personal remittances were found to be strongly correlated to these indicators.The results indicate a growing shift towards lesser emigrations numbers comprising of highly educated/skilled manpower as compared to a low skilled/less educated mass emigration in previous years.ANN maximised predictive accuracy of personal remittance models in comparison to the conventional regression method as well as SVM methods.The study will help formulate policies in the future, by applying effective modelling techniques to capture the complex dynamics trends in global migration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".