Bibliographic record
Abstract
Immigrants from Asia have been a defining feature of demographic change over the last quarter century in the United States. The 2000 US Census identified Asian Americans as the fastest growing immigrant group in the nation and the Pew Research Center estimates that Asian Americans will become the largest immigrant group in the country by 2055. With that growth has come the development of a vibrant scholarly literature examining Asian American political participation in the United States. This article is designed to provide an overview of the major foundational studies that explore Asian American political behavior, including mobilization and participation in American politics. The earliest research began in the fields of political science and sociology and consider the viability of a panethnic Asian American identity as a unit of analysis for group-based behavior and political interests. Numerous scholars have considered the circumstances under which panethnic Asian American identity can be activated toward group behavior, and how differences in national origin can lead to variations in behavioral outcomes. Participation in American politics, however, is rooted in many other factors such as socioeconomics, one’s experience as an immigrant, ties to the home country, and structural barriers to activism. Individual resources have long been considered an essential component to understanding political participation. Yet, Asian Americans present a puzzle in American politics, evincing higher education and income while participating in politics at a more modest rate. In response to this puzzle, scholars have theorized that structural conditions and the experience faced by Asian immigrants are powerful mechanisms in understanding the determinants of Asian American political participation. Once considered to have relatively weak partisan attachment and little interaction with the two major parties in the United States, studies that examine the development of partisan attachment among Asian Americans are explored which, more recently, find that a growing majority of Asian Americans have shown a preference for the Democratic Party. Finally, we detail studies examining the conditions under which Asian American candidates emerge and are successful, the co-ethnic electorate who supports them, and conclude by detailing the opportunities and constraints for cross-racial collaboration and conflict.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".