Imperfect democracies : the democratic deficit in Canada and the United States
Bibliographic record
Abstract
Introduction / Patti Tamara Lenard and Richard Simeon 1 The Deficit: Canada and the United States in Comparative Perspective / Pippa Norris 2 Citizen Expectations and Performance: The Sources and Consequences of Deficits from the Bottom Up / Neil Nevitte and Stephen White 3 Defining and Identifying a Democratic Deficit / David Beetham 4 Democracy in American Elections / Michael McDonald 5 Can Canada's Past Electoral Reforms Help to Understand the Debate over Its Method of Election? / John C. Courtney 6 Regulating Political Finance in Canada: Contributions to Democracy? / Lisa Young 7 Campaign Finance Reform in the United States / Robert C. Boatright 8 Imperfect Legislatures / David C. Docherty 9 Democracy's Wartime Deficits: Presidential Prerogatives and Liberal Democracy in America / Daniel J. Tichenor 10 The Centre of the Deficit: Power and Influence in Canadian Political Executives / Graham White 11 Extending the Franchise to Non-Citizen Residents in Canada and the United States: How Bad is the Deficit? / Patti Tamara Lenard and Daniel Munro 12 Citizen Representation and the American Jury / Ethan J. Leib and David L. Ponet 13 Supplementary Democracy? Deficits and Citizens' Assemblies / Amy Lang and Mark E. Warren 14 Reflections on the Democratic Deficit in Canada and the United States / Simone Chambers and Patti Tamara Lenard Index
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".