Ethno-Racial Origins of Candidates and Electoral Performance
Bibliographic record
Abstract
This article uses the Canadian case to assess whether voter bias plays a role in accounting for the underrepresentation of ethnic minorities, especially racial minorities, in the national legislatures of diverse societies. Two sets of empirical analyses are performed, drawing on the results of the 1993 Canadian general election: survey information on candidates who ran for the major parties and census data on constituency characteristics. The first set tests for overt voter bias against minority candidates by employing several categories of minority origins, party and constituency variables to control for contextual effects, and candidate vote-share as the dependent variable. The second set tests for a more subtle form of bias that is sometimes associated with the need for minority candidates to be exceptionally qualified (‘compensation hypothesis’). The evidence indicates that minorities do not lose votes in elections because of their background and do not need to have more personal credentials in order to gain votes. The results suggest that any misgivings party officials may have about the electoral performance of minority candidates are misplaced.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".