Bibliographic record
Abstract
The book discusses the positive effects of migration and its potential for economic, social and cultural contribution to development. By illustrating numerous migrants’ life stories, Phillipe Legrain seeks to eliminate existing myths, prejudices and fears. *** Migration is an increasingly global issue. However, while governments are intensively promoting international business and flow of goods and services, they are creating ever higher national barriers to the free movement of people. The fear that foreigners are stealing jobs, abusing the welfare system, ignoring the local way of life and threatening freedom and security of the host country is still present. In “Immigrants: Your Country Needs Them” Phillipe Legrain 1 , a London-based economist and journalist, demonstrates the beneficial effects of migration and seeks to eliminate the existing myths, prejudices and fears. Legrain's book is an important contribution to the migration debate, as it demonstrates a positive view of migration and concentrates on the benefits for the rich West. It examines the impact of migration on individual countries such as the United States, Canada, Australia, Britain and other Western European Countries, but also tries to find out what they can learn from each other’s experiences. Legrain has interviewed immigrants across the world and researched migration policies in rich countries. Throughout his book he is going through the detailed arguments that are commonly made against migration and is disproving them, one by one. The immigrants’ personal life stories are used to strengthen his line of reasoning. Moreover, this approach can appeal to the conscience and self-interest of those who live in rich countries. Legrain uses case studies and other practical examples to support his ideas and to explode prejudices and fears. Legrain rejects the opinion that migrants cost natives their jobs. They complement their efforts and allow them to pursue better careers. In advanced economies, there is a mismatch
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".