Representing People and Not Interests: A Rawlsian Conceptualization of the Right to Vote
Bibliographic record
Abstract
The author advances a Rawlsian rationale for mandating voter equality — that is, “one person, one vote” — as a basic matter of constitutional law in support of a principled and practical interpretation of section 3 of the Canadian Charter of Rights and Freedoms. The author examines prospective bases for the right to vote, chiefly focusing on two interpretive approaches — presumptive and relative equality. Although John Rawls made it clear in his seminal A Theory of Justice that he favoured voter equality as the basis for political participation in a democracy, a complete account of his reasoning and arguments for this position has seemingly only been possible with the benefit of the consideration of his later works in political philosophy. The author first identifies why the need for a reinterpretation of section 3’s right to vote poses a pressing problem for Canada’s constitutional democracy and then explains the two leading accounts of presumptive and relative equality as a matter of first principles. With this essential background in place, the author proceeds to consider three broad parts of a Rawlsian rationale for voter equality. The author concludes that mandating voter equality in a democracy allows for the most stable, cohesive, and robust platform permitting individual citizens to pursue their own conceptions of the good while still allowing for the constitutional protection of the fundamental interests of minorities.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".