Bibliographic record
Abstract
Legislatures, and the men and women who serve in them, form the very heart of Canadian democracy. After all, with the very rare exception of nationwide referendums, Canadians speak collectively only when voting for the people who will be representing their interests in Ottawa. The same is true provincially. But how “democratic” are legislative assemblies in Canada? After we elect our representatives, are we comfortable that we are being properly, and democratically, represented? Apparently not -- respect for legislatures and legislators in Canada has steadily declined, and this perception is only aggravated by the current political climate. Legislatures provides a democratic audit of Canada’s provincial and national representative assemblies. It argues that the problem existing in these bodies is not a lack of talent so much as a lack of institutional freedom. Specifically, the problem is largely one of resources and rules. The move to a more multi-party system nationally and the increasing tendency to downsize provincial assemblies has placed additional hurdles in the path to good governance. Docherty uses the series’ criteria of responsiveness, inclusiveness, and participation to evaluate critically the performance of legislatures in Canada, and makes recommendations for legislative reform in Canada. A crucial and timely overview of legislatures, this book will appeal to students and scholars of Canadian politics, as well as politicians and policymakers and anyone interested in the current state of Canadian democracy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.048 |
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".