Brain Drain Or Brain Gain -Understanding Overseas Migration of Students from Kerala
Bibliographic record
Abstract
It is generally a disinguished fact that Kerala is renowned as one of India's most literate states, abundant with efficient and hardworking young generation. In accordance with NIRF 2020 evaluation, Twenty Kerala institutions are considered among the top 100 universities in India. However, in general perception students in Kerala have the strong aspiration to pursue higher education abroad and to obtain appropriate jobs / career advancement consistent to their inclinations. In the present scenario, Student migration becomes was an unfathomed aspects of global migration flows and trends in Kerala. This drive accelerated further in the last five years generating a fast peak in the swarm of Kerala students seeking higher education in various professions among various foreign countries especially in Canada, the United States, the United Kingdom, Australia, New Zealand and China. It is a common tendency of the majority of students pursuing higher education overseas aiming for securing permanent residency in the respective country and settle life there appropriately congenial to their desire and attitude. This study is an elaborate attempt to elucidate the reason for the migration of students in Kerala and cherishing the ambition in obtaining permanent residence after graduation/further studyabroad. The present study is both narrative and analytical in nature by exploring this scenario vehemently
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".