Diversity Matters: The Election of Asian Americans to U.S. State and Federal Legislatures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite substantial research on descriptive representation for Blacks and Latinos, we know little about the electoral conditions under which Asian candidates win office. Leveraging a new dataset on Asian American legislators elected from 2011 to 2020, combined with pre-existing and newly conducted surveys, we develop and test hypotheses related to Asian American candidates’ ingroup support, and their crossover appeal to other racial and ethnic groups. The data show Asian Americans preferring candidates of their own ethnic origin and of other Asian ethnicities to non-Asian candidates, indicating strong ethnic and panethnic motives. Asian candidates have comparatively strong crossover appeal, winning at higher rates than Blacks or Latinos for any given percentage of the reference group. All else equal, Asian American candidates fare best in multiracial districts, so growing diversity should benefit their electoral prospects. This crossover appeal is not closely tied to motives related to relative group status or threat.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it