A Comparative Approach to Explaining Gender Disparities in Asian American and Asian Canadian Politics
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
In 2020, Asian Americans were the least descriptively represented at all levels of elected office compared to whites, Blacks, and Latinos (Sedique, Bhojwani, and Lee 2020). In this context, Asian women lagged behind Asian men in holding local-level positions, yet they surpassed Asian men in holding federal and statewide offices, and they led 81% of state- and local-level Asian civil rights organizations (AAPI Power Fund 2020; Reflective Democracy Campaign 2021). Do gender disparities in Asian American political representation arise because Asian women are less likely to run for office than Asian men, or because they are less likely to win elections? Do these disparities vary across levels of office? And are they unique to Asian Americans ?
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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