Bibliographic record
Abstract
In 2011, Parliament altered the formula for distribution of seats in the House of Commons to the provinces by passing the Fair Representation Act. The law adds 30 seats to the House, with 27 going to Ontario, Alberta, and British Columbia to account for population growth and 3 to Quebec to ensure its proportionate representation is not diluted. The legislation was the fifth version of a new representation formula to be proposed by the Conservative government since coming to power in 2006. Its various iterations attracted significant political controversy along the way, with the government House Leader notably calling the Premier of Ontario the “small man of Confederation” for objecting to Ontario’s treatment in an earlier bill. Canada’s once-a-decade redrawing of electoral boundaries proceeded in 2014 in accordance with the Fair Representation Act’s distribution of seats to the provinces and the 2015 election will be contested under a new electoral map.This chapter investigates the implications of the Fair Representation Act for democratic representation at the federal level. It asks two main questions: 1) What are the implications of the Fair Representation Act for the principle of voter equality (also known as representation by population); and 2) Was the unilateral amendment of the representation formula by Parliament constitutional? The chapter concludes that while it takes an important step toward voter equality, the Act makes large deviations from the fundamental principle of representation by population not just possible, but likely in future redistributions. The constitutionality of the Act has also been placed in doubt on federalism grounds by the Supreme Court of Canada’s reasoning in the recent Reference re Senate Reform. Given that the amendments to the representation formula were done by Parliament alone, and the Supreme Court in the Senate Reference significantly narrowed Parliament’s unilateral constitutional amendment power, whether provincial consent was required is a live issue.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".