Getting the Picture: Defining Race-Based Stereotypes in 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
This article considers electoral inter-group dynamics in Quebec, Canada, by focusing on what White voters expect from political candidates of color. While significant work has been done on the use of political heuristics such as race or gender-based framing by the media, we do not know as much about the way voters interpret and use these stereotypes in a political context. In this article, we consider voters' interpretation of race-based cues using qualitative evidence gathered in six focus groups. First, we explore the content of stereotypes typically associated with politicians of color in the province. Second, this article provides an assessment of some of the ways in which race-based stereotypes are used to understand politics and evaluate politicians of color. We find that race-based stereotypes contribute to defining expectations regarding politicians' behavior. While voters may consciously choose to favor politicians of color, the perception of social distance between a marginalized candidate and them can also lead to negative cross-ethnic attitudes.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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