Globalization and its links to migration and trafficking: the crisis in India Nepal and Bangladesh.
Bibliographic record
Abstract
In the last three decades migration as a consistent pattern has seen an unprecedented growth and is being increasingly linked to economic growth. According to an International Labour Organization (ILO) publication Workers Without Frontiers the total number of migrants around the world now surpasses 120 million up from 75 million in 1965-and continues to grow. As a macro factor globalization has a profound effect upon international labour migration. Increased technological development is also linked to a heightened movement of goods services and capital across international boundaries. This revolution in information technology and the advancement in transport systems also means that people have more access not only to information but also to opportunities for movement. As more and more multinationals shift their industries to less-developed nations the numbers of those willing to migrate in search of world from rural areas into cities or countries where these industries are based will only increase. However the downside is that while rich developed countries are profiting from the without boundaries phenomenon workers from poor nations-often the least-skilled and most vulnerable are the ones who are being exploited both in their own countries or as migrants to industrialized countries. In fact Peter Stalker states that while governments support the flows of trade and finance they do little to take action when it comes to human beings. (excerpt)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".