Bibliographic record
Abstract
The federal public service plays a vital role in Canada’s development by helping to shape public policies and deliver programs and services to Canadians. Speaking Truth to Canadians about Their Public Service provides a comprehensive review of the challenges confronting the public service, how the relationship between politicians and career officials has evolved in recent years, and what motivates public servants. Donald Savoie calls on Canadians and their politicians to consider what they want from their federal public service. Answering this question requires a fresh look at the government’s traditional accountability requirements, how policies are shaped, and how government programs and services are delivered. It also requires a review of ambitious modernization and reform measures launched over the past forty years to make the public service more accommodating to political direction and to improve program delivery. Dividing federal public servants into two groups – poets (those who write policy) and plumbers (those who deliver programs and services) – the book establishes who has the upper hand. This division sheds new light on the theories that seek to explain the attitudes and behaviours of career government officials. Amid increasingly strong signs that the public service is in need of a reset, Speaking Truth to Canadians about Their Public Service concludes with practical recommendations to assist Canadians and their politicians in defining what they want their public service to be.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".