“We are Not an Ethnic Vote!” Representational Perspectives of Minorities in the Greater Toronto Area
Bibliographic record
Abstract
Abstract This article examines the representational preferences of the represented, a perspective which has been largely neglected by political scientists in Canada. Using focus group interviews, I assess views of representation among three visible minority communities—black, South Asian and Chinese—within the urban and highly diverse setting of the Greater Toronto Area. Applying Hanna Pitkin's classic conceptual framework, I probe the multiple dimensions of political representation, examining the relative importance of its formal, descriptive, substantive and symbolic elements from minorities' perspective. Drawing on more recent advances in representational theory, I examine how minorities evaluate political actors' claims to represent them, and look at the “mobilizing objections” citizens raise in the face of such claims. Finally, I compare the three communities to assess similarities and differences in perceptions of representation along its various dimensions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".