Bibliographic record
Abstract
Migration is the movement of people from one place to another. This phenomenon occurs for various reasons, including war, climate change, drought, and economic and political factors. It encompasses more than just the movement of people; it also includes the seasonal movement of birds, fish, and other animals. This phenomenon has persisted throughout history, including the migration of tribes in the fourth century due to drought and climate change. The economic and social dimensions of international migration have come under scrutiny. The rise of globalization has contributed to an increase in international migration, with certain countries, such as the USA, Canada, and Australia, implementing legal immigration policies motivated by economic considerations. Illegal migration from underdeveloped countries has reached substantial levels due to factors such as wars, food shortages, and climate change. This study aims to examine the causes of international human migration, the various types of migration, and the migration systems. The discussion will cover migration processes, agreements between receiving and sending countries, and the legal and economic consequences of migration. It will also address the challenges experienced by our nation as a source and destination for immigration. Migration entails challenges, but it also generates benefits in nations with the right legal frameworks and social measures. This study will cover both sociological and liberal perspectives.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".