Is Every Ballot Equal? Visible Minority Vote Dilution in Canada
Bibliographic record
Abstract
Canada’s visible minority population is increasing in absolute terms and as a proportion of the national population, and this increase is fueled by immigration. Moreover, Canada’s visible minority population is growing where Canada’s population growth is increasingly concentrated—in Ontario, Alberta, and British Columbia and in its urban areas. The question is whether the underrepresentation of these provinces and urban areas in the House of Commons also translates into the dilution of the votes of citizens from visible minority communities. The authors conclude that visible minority vote dilution exists in Canada. The worth of an average vote in Canada is 1. In 1996, the weight of a rural vote was 1.15, while urban visible minority voters had a voting power of 0.95. In 2001, while the weight of a rural vote increased to 1.22, the weight of an urban visible minority vote declined to 0.91. While the trend for urban visible minority voters is downward, the voting strength of urban voters remained largely unchanged, standing at 0.97 in 1996 and 0.96 in 2001. This suggests that urban visible minority voters are concentrated in certain urban ridings. Visible minority vote dilution arises from the way seats in the House of Commons are distributed interprovincially and intraprovincially. Visible minority vote dilution must be addressed for three reasons: some of the rules and practices giving rise to minority vote dilution may violate the Charter; visible minority communities are increasingly disadvantaged economically in comparison to the population as a whole, so the way that those interests are represented in the legislative process counts; and to successfully integrate visible minority immigrants, Canada’s political institutions must be scrupulously fair in how they represent the interests of the newest members of the Canadian political community. The paper concludes dilution, Pal and Choudhry by considering three reform options: amending federal and provincial legislation to minimize variances in riding size; pressuring the electoral boundary commissions to limit deviations from voter equality; and increasing the size of the House of Commons to 327 members to accommodate population growth in British Columbia, Alberta and Ontario.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".