Ethno-Racial Appeals and the Production of Political Capital: Evidence from Chicago and Toronto
Bibliographic record
Abstract
Ethno-racial appeals mobilize individuals through their social categories. Such appeals matter especially in municipal elections, where partisan cues are often absent and turnout is low. This article presents findings from an analysis of ethno-racial appeals in 914 campaign documents from the 2014 Toronto and 2015 Chicago municipal elections. It reveals that campaigns frequently target non-White and White ethnic voters through explicit appeals. These appeals do not fit into the existing framework of racial priming theory. Drawing instead on Bourdieu’s theory of capital, the article conceptualizes ethno-racial appeals as attempts to produce or destroy a candidate’s political capital among specific groups. Campaigns do this directly by making claims about the group’s purported interests or indirectly by invoking candidates’ relevant cultural or social capital. Analyzing ethno-racial appeals in this way helps to comprehend the mobilization of non-Whites, illuminates the production of ethno-racial voting, and contributes to the understanding of place-based culture.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".