Mind the Gap: Understanding the Lack of Descriptive Representation of Asian American Women in Politics
Bibliographic record
Abstract
Inadequate representation of both race and gender in politics remains a prevalent issue that is only further exacerbated by the intersection of these factors. This research attempts to understand precisely where the barriers to achieving this representation lie and focuses on the Asian American community, which saw the lowest levels of descriptive representation across all elected positions in 2020. Are Asian women not running in these elections to begin with? Or is it that their attempts at doing so are frequently met with disappointment? By compiling and analyzing a dataset of 738 Asian American political candidates running in either local, state, or federal elections, a small glimpse of insight was discovered. Women made up 47% of the listed candidates, suggesting that their willingness to become involved in politics was not to blame. This data also revealed patterns in both the primary and general elections. Most notably, Asian women at the federal primaries only secured a spot at the general election 46% of the time but of those candidates that progressed, 83% won their respective race. Arguments have been made that voters do not wish to elect women, and particularly women of colour, into political positions. This line of thinking may dissuade party members from selecting an Asian woman at the primary level, potentially opting for a candidate that they believe is more likely to attract voters at the general election. However, this data suggests that voters are willing to support a female Asian candidate – it is the party who is not. By failing to place confidence in these candidates, the parties themselves are contributing to this lack of descriptive representation. This research presents an opportunity for further investigation into addressing the root causes of this mentality and becoming one step closer to rectifying this gap.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".