Transforming Public Administration in Canada
Bibliographic record
Abstract
This book explores the intersection of social equity-related issues with concerns within the field of public administration in Canada. Shifts in public sector governance, populist discourse and political extremism, democratic instability, and super-wicked challenges such as climate change and environmental disasters, demographic transformations (aging society along with increased migration), financial crises, terrorism, housing crises, religious conflicts and racial inequities, dominate the landscape. This book challenges scholars from schools of public administration to use a social equity lens to reimagine and rethink the ways in which public administration is currently practiced, with the goal of abandoning colonial logics that betray social equity. The chapters in this volume include contributions from a diversity of authors, each contributing their unique perspectives on social equity as they relate broadly to their areas of expertise. The authors share a common interest in critically exploring how social equity intersects with their areas of public administration research or practice including concerns with environmental justice, Indigenous rights, education and decolonization. This book is intended for three audiences: (1) scholars who are also involved in teaching public administration; (2) students of public administration who want to learn more about what they are learning, and why social equity matters to their field of study; and (3) public administrators who are managing public institutions and who may also be involved in training public administrators.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".