Bibliographic record
Abstract
Abstract Ethnicity is a concept employed to understand the social, cultural, and political processes whereby immigrants and their children cease to be “foreign” and yet retain practices and networks that connect them, at least imaginatively, with places of origin. From an early juncture in American history, ethnic neighborhoods were an important part of such processes. Magnets for new arrivals, city neighborhoods both emerged from and reinforced connections among people of common origins. Among the first notable immigrant neighborhoods in American cities were those composed of people from the German-speaking states of Europe. In the second half of the 19th century, American cities grew rapidly and millions of immigrants arrived to the country from a wider array of origins; neighborhoods such as the New York’s Jewish Lower East Side and San Francisco’s Chinatown supported dense and institutionally complex ethnic networks. In the middle decades of the 20th century, immigration waned as a result of legislative restriction, economic depression, and war. Many former immigrant neighborhoods emptied of residents as cities divided along racial lines and “white ethnics” dispersed to the suburbs. However, some ethnic enclaves endured, while others emerged after the resumption of mass immigration in the 1960s. By the turn of the 21st century ethnic neighborhoods were once again an important facet of American urban life, although they took new forms within the reconfigured geography and economy of a suburbanized nation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".