Bibliographic record
Abstract
Italian migrants in the United States have been often associated to the tendency to neglect the importance of culture as an instrument of upward social mobility. Traditionally perceiving culture as a hegemonic tool of the elites, Italian migrants in the United States, who had a predominantly peasant background, were supposedly uninterested in educating their children, rather preferring that they drop out school to add with their work a supplemental income necessary to face the daily necessities of migrant families. Based on a long-standing prejudice, this supposed Italian-American disinterest towards culture and education has been revisited by some scholars, who have revaluated the migrant attitude towards the cultural realm. In the first part, this essay will offer an overview of the different scholarly views on the relationship between Italian-Americans, culture and education. In the second part, it will discuss how Italian-Americans approached the usage of the Italian language, the native idiom that was disappearing in the Little Italies with the progression of newer generations, which inevitably favoured the recourse to English. Finally, the essay will take into account how Italian governments in the Liberal Age (1861–1921) connected to the communities of the Italians in the United States through programs addressed to fostering the Italian language overseas as a way to preserve the Italianità, namely the Italian character of migrants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".