Small Strangers: The Experiences of Immigrant Children in America, 1880-1925
Bibliographic record
Abstract
Small Strangers: The Experiences of Immigrant Children in America, 1880-1925. By Melissa R. Klapper. Chicago, IL: Ivan R. Dee, 2007. 219 pages. $27.50 (hardcover). As a group that has been largely ignored by historical literature, Melissa R. Klapper has given children the chance to finally speak in Small Strangers: The Experiences of Immigrant Children in America, 1880-1925. These children, whose lives were predominantly shaped by immigration and adaptation to the United States, were part of the turn-of-the-century mass migration when nearly twenty-five million people emigrated from Mexico, Japan, Canada, and southern and eastern Europe to settle in urban areas throughout America. Their story is one of straddling two worlds: their Old World's ethnic heritage and the New World's promise of opportunity, and the challenges posed by both. Klapper takes a life-cycle approach to study how children balanced these competing worlds from childhood through adolescence. While there was no single immigrant experience, she argues that there were definite shared commonalities, including a desire for economic opportunity and the effects that Americanization had on families and ethnic traditions. Immigrant children experienced the strongest push to assimilate in the public school system, which worked to teach them how to act, speak, think, and live as Americans (47). Although parents wanted better lives for their children and saw education as a means to achieving this end, they feared that Americanization efforts would destroy their children's ethnic ties. In response, these parents worked to reinforce traditions by sending their children to language schools and emphasizing the celebration of ethnic holidays and family events. While these attempts proved successful with younger children, adolescents were more willing to turn their backs on their heritage in favor of American culture. Klapper explains that even though parents did not necessarily approve of this, they understood that this acculturation would ultimately contribute to their ethnic communities' success in America. The need for family income sometimes forced children to forgo their education in favor of wage labor, as family survival took precedence. To illustrate the importance of children's wages, Klapper refers to the example of Massachusetts lawmakers' attempt to ensure that the children working in Lowell's mills put education before work. …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".