Revolution in the Red Chamber? The Senate of Canada in the 21st Century
Bibliographic record
Abstract
This dissertation investigates the extent and effect of a series of reforms made to the Canadian Senate by the Liberal government of Justin Trudeau beginning in 2015. Previous attempts to reform the Senate have proven almost completely unsuccessful despite widespread and longstanding dissatisfaction with the institution. This failure has largely been the result of an inability to unify the federal and provincial governments behind a particular understanding of the Senate's role in Canadian democracy and federalism. This has led to a corresponding inability to generate the constitutionally required level of support behind any specific reform proposal. In this dissertation, I seek to answer a series of related questions: What institutional changes has this government made to the Senate? How have these changes affected the Senate in the way it carries out its work? And why were these reforms successfully implemented when almost every other attempt at reform failed? I conclude that the reforms were successfully implemented because, when compared to many of the more sweeping changes which have been proposed for the Senate in the past, these reforms are inherently limited. As a result, they did not require provincial consent. At the same time, however, these reforms are the most significant ever made to the Canadian Senate and among the most significant changes ever made to any part of the Canadian Parliament. Furthermore, these reforms entrench a coherent vision of the Senate's role in the Canadian democratic process. These reforms institutionalize and prioritize to an unprecedented degree the enhanced representation of women, Indigenous people, and racialized minorities in the Senate. The reforms are also designed to support the Senate as a complementary chamber of independent second thought. This leads to one last important question: how successful have these reforms been in implementing the Trudeau government's stated goals for the Senate? I find strong evidence of increased descriptive representation for women, Indigenous people, and racialized minorities. Finally, while the reforms have made the Senate a more active complementary legislative chamber, I find mixed evidence of increased senatorial independence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".