Re-Examining the Distribution of Decision-Making Power Within Canadian Political Parties
Bibliographic record
Abstract
Political parties make representative democracy possible.They develop policy agendas, nominate candidates seeking elected office, choose leaders who organize the parliamentary party (and perhaps even serve as prime minister), and support their electoral bids during campaigns.In accomplishing these democratic functions, parties are essentially private clubs free to choose how each task is executed.A perennial issue is, therefore, identifying where real decisionmaking power rests between members 'on the ground,' and elites in the central party bureaucracy and parliamentary caucus.Despite the supposed dominance of elites 'at the centre,' scholars have begun recognizing that decision-making power maybe be 'stratified.'Each face may control some, but not all, of the pillars of intra-party democracy (Carty, 2004; Bolleyer, 2012).Recent studies go further in finding that power and authority over each core task may, in fact, be shared (Cross, 2018).This thesis builds on these studies by asking two important questions.How do parties develop power-sharing arrangements between their principal faces?What explains variation in these arrangements?Building from the comparative literature on party reform, this thesis makes original theoretical and empirical contributions to the study of Canadian politics.The theoretical contribution pushes existing frameworks to view the formation of power-sharing arrangements as a cyclical process.As the drivers of reform exert pressures onto party organizations, parties encounter complimentary pressures for decentralizing some aspects of an individual decision while centralizing others.The empirical contributions stem from applying this model to reforms undertaken within the Conservative, Liberal, and New Democratic parties since the Canadian party system fragmented in the early 1990s.Like every student, I have incurred many debts throughout the course of this project.While these debts can only truly be settled by paying it forward, I wish to acknowledge and thank those who helped bring this project to successful completion.Thanks to my supervisory committee-Professors William Cross, Jonathan Malloy, and Steve White-for overseeing this project and lending me their expertise
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".