Bibliographic record
Abstract
To honour the distinguished career of Donald Savoie, Governing brings together an accomplished group of international scholars who have concerned themselves with the challenges of governance, accountability, public management reform, and regional policy. Governing delves into the two primary fields of interest in Savoie's work - regional development and the nature of executive power in public administration. The majority of chapters deal with issues of democratic governance, particularly the changing relationship over the past thirty years between politicians and public servants. A second set of essays addresses the history of regional development, examining the politics of regional inequalities and the promises and pitfalls of approaches adopted by governments to resolve the most vexing policy problems. Contributors provide readers with a valuable primer on the key issues that have provoked debate among practitioners and students of government alike, while reflecting on government initiatives meant to address inadequacies. Showcasing the practical experience and scholarly engagement of its authors, this collection is a valuable addition to the fields of public administration, public policy, political governance, and regional policy. Contributors include Peter Aucoin (Dalhousie University), Herman Bakvis (University of Victoria), James Bickerton (St Francis Xavier University), Jacques Bourgault (École nationale d'administration publique/UQAM), Thomas Courchene (Queen's University), Ralph Heintzman (University of Ottawa), Mark D. Jarvis (University of Victoria), Lowell Murray (Senate of Canada, retired), B. Guy Peters (University of Pittsburgh), Jon Pierre (University of Gothenburg) Mario Polèse (INRS-UCS), Christopher Pollitt (Leuven University), Donald J. Savoie (Université de Moncton), and Paul G. Thomas (University of Manitoba).
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.258 | 0.132 |
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".