Friendly fire: Electoral discrimination and ethnic minority candidates
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
Discriminatory attitudes towards ethnic minorities are widespread, and a common presumption is that ethnic minority candidates suffer electorally as a result. However, some research has shown that little electoral discrimination occurs, because ethnic minority candidates tend to run for parties of the left, while voters with negative attitudes towards minorities are concentrated on the right. This study shows that when ethnic minority candidates do run for right-wing parties they suffer the brunt of electoral discrimination, while those on the left are insulated. To do so it leverages two methods: a candidate experiment and a difference-in-difference analysis of candidate demographic data and aggregate election results. An ideological stereotyping mechanism is also tested, but there is little evidence that right-wing voters reject ethnic minority candidates because they are viewed as left-leaning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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