Bibliographic record
Abstract
Most studies of immigration to the New World have focused on the United States. Samuel L. Baily's eagerly awaited book broadens that perspective through a comparative analysis of Italian immigrants to Buenos Aires and New York City before World War I. It is one of the few works to trace Italians from their villages of origin to different destinations abroad. Baily examines the adjustment of Italians in the two cities, comparing such factors as employment opportunities, skill levels, pace of migration, degree of prejudice, and development of the Italian community. Of the two destinations, Buenos Aires offered Italians more extensive opportunities, and those who elected to move there tended to have the appropriate education or training to succeed. These immigrants, who adjusted more rapidly than their North American counterparts, adopted a long-term strategy of investing savings in their New World home. In New York, in contrast, the immigrants found fewer skilled and white-collar jobs, more competition from previous immigrant groups, greater discrimination, and a less supportive Italian enclave. As a result, rather than put down roots, many sought to earn money as rapidly as possible and send their earnings back to family in Italy. Baily views the migration process as a global phenomenon. Building on his richly documented case studies, the author briefly examines Italian communities in San Francisco, Toronto, and Sao Paulo. He establishes a continuum of immigrant adjustment in urban settings, creating a landmark study in both immigration and comparative history.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".