Ties That Bind, Ties That Divide: 100 Years of Hungarian Experience in the United States
Bibliographic record
Abstract
The Hungarian community of the United States emerged during the last decades of the nineteenth century and flourished throughout much of the twentieth. Scholarly historical writing about this community, however, did not start to surface until nearly a century after its birth. When such works began to appear, the best came not from its members, but from a historian in Hungary, Julianna Puskás. After publishing a number of specialized articles and monographs, she has presented us with an overview of the subject, which is one of the volumes in the Ellis Island series of American immigration and ethnic histories. From the 1880s to the 1960s, Hungarians came to the United States in relatively large numbers. They brought with them their customs, culture, traditions—along with their religious, linguistic, class, occupational, and ideological ties. Their immigrant experiences reinforced some of those and weakened others. Still other bonds were developed by the newcomers after their arrival in America. Those bonds gave rise to what we might call sub-ethnic identities, which, according to Puskás, were particularly abundant and marked among the people who came to the United States from Hungary. All in all, the ties served both to bind and to divide—in a complex and ever-changing manner—the communities that immigrants from Hungary established here. The story of their interplay is told in this book in an effective and highly readable manner.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".