Governing from the centre: the concentration of power in Canadian politics
Bibliographic record
Abstract
Redefined during the past thirty years, the centre of government currently extends itself further than ever before. Central governmental agencies are 'where the rubber meets the road', where public service meets politics, and policy becomes reality. So who's driving this car? Agencies such as the Privy Council Office, the Finance Department, and the Treasury Board exert their influence horizontally, deciding how policy is made and how money gets spent According to Donald Savoie, these organizations, instituted to streamline Ottawa's planning processes, instead telescope power to the Prime Minister and weaken the influence of ministers, the traditional line departments, and even parliament, without contributing to more rational and coherent policy-making. This is scholarship at its best: rigorous and riveting. The government operates as a combination of known procedures and the more elusive subtleties of human relationships and unspoken codes of behaviour. Donald Savoie's long-time involvement in government affairs allows him to read through the surface of the results of his extensive research-which included several interviews with elites-in order to expose all the levels of power at play. Indispensable reading for students of politics, public policy, and public administration, Ottawa watchers, journalists, lobbyists, and civil servants who want to know what is really going on.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.067 | 0.043 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".