Bibliographic record
Abstract
Abstract Large‐scale migration from India began in 1947 after India's independence from the United Kingdom. A substantial proportion of Indian migrants are highly educated, skilled professionals in science‐based fields, especially physicians, engineers, and computer scientists, who have migrated to economically developed nations, predominantly the United States, the United Kingdom, and Canada. Since the 1970s, semiskilled and unskilled workers have also been migrating to the oil‐producing countries in the Middle East. The two migrant flows vary immensely: migrants to the developed nations have the right to citizenship and family reunification and consequently have settled permanently in these nations. In sharp contrast, migration to the Middle East nations is predominantly temporary, and composed primarily of men who rarely have the right to migrate with their families. The largest number of Indian migrants can be found in the United States (1,678,765), the United Kingdom (1,200,000), Canada, (851,000), and Australia (190,000). Indian migrants to the Middle East are predominantly in Saudi Arabia (1,500,000), followed by the United Arab Emirates (950,000), Oman (312,000), Kuwait (295,000), Qatar (131,000), and Bahrain (312,000).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".