Bibliographic record
Abstract
Asians have settled in every country in the Western Hemisphere; some are recent arrivals, other descendents of immigrants who arrived centuries ago. Bringing together essays by thirteen scholars from the humanities and social sciences, Displacements and Diasporas explores this genuinely transnational Asian American experience-one that crosses the Pacific and traverses the Americas from Canada to Brazil, from New York to the Caribbean. With an emphasis on anthropological and historical contexts, the essays show how the experiences of Asians across the Americas have been shaped by the social dynamics and politics of settlement locations as much as by transnational connections and the economic forces of globalization. Contributors bring new insights to the unique situations of Asian communities previously overlooked by scholars, such as Vietnamese Canadians and the Lao living in Rhode Island. Other topics include Chinese laborers and merchants in Latin America and the Caribbean, Japanese immigrants and their descendants in Brazil, Afro-Amerasians in America, and the politics of second-generation Indian American youth culture. Together the essays provide a valuable comparative portrait of Asians across the Americas. Engaging issues of diaspora, transnational social practice and community building, gender, identity, institutionalized racism, and deterritoriality, this volume presents fresh perspectives on displacement, opening the topic up to a wider, more interdisciplinary terrain of inquiry and teaching.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".