Predicting group consciousness among Asian Americans: Considering commonalities, shared interests, panethnic group identification, and linked fate
Bibliographic record
Abstract
Abstract Identifying factors linked to the development of group consciousness is important toward bettering our understanding of group formation processes among marginalized ethnoracial groups. This study examines predictors of group consciousness among Asians and Asian Americans in the United States, focusing on numerous dimensions of this concept, including linked fate, panethnic group identification, and four specific sources of perceived group commonality and interests: (1) cultural, (2) economic, (3) political, and (4) racial. We use data from a national survey to examine socio‐structural, political, discrimination, and immigration correlates associated with separate dimensions of Asian group consciousness. We found that perceiving interpersonal discrimination increased the importance of being Asian; heightened the odds of feeling linked fate with other Asian people; and enhanced the odds of identification as “Asian American.” Republicans and Independents were less likely to perceive different elements of Asian group consciousness compared to Democrats. Educational attainment, income, gender, employment status, ethnicity, and English‐speaking comfortability had varying effects across certain measures of Asian group consciousness. For Asians and Asian Americans, interpersonal discrimination and certain socio‐structural, political, and immigration factors may be especially meaningful toward the development of linked fate, shared group interests and commonalities, and panethnic identification, all of which are key toward activating group consciousness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".