Bibliographic record
Abstract
Abstract What determined where migrants would move to in the late 19th century? What was the impact of migration on workers’ wages? During the 19th century, nearly forty-five million people moved away from Europe and another fifteen million left their place of birth in Asia for other regions. A host of determinants mattered for where migrants went and when migrants decided to leave. Wage differentials and economic opportunities played a strong role, but so did transportation costs and information. Poor nations did not automatically witness a large exodus since emigration depended on the age distribution of the population, whether anyone from their region had previously migrated and on the status of structural economic change in their country. The United States, Canada, Australia, New Zealand, Brazil, and Argentina were some of the places where immigrants made up a large share of the population. Southern and Eastern European nations were the principal senders to the Americas and Australasia in Europe. In Asia, Chinese and Indian emigrants found opportunity in Southeast Asia, but also in the Caribbean. Wages tended to grow more slowly in receiving places and rose in sending areas. In 1882, the United States imposed the first stringent control on immigration with the Chinese Exclusion Act.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".