Bibliographic record
Abstract
The Politics of Public Management is a 'textbook case' in public administration.In this study David Good describes and analyses in depth the events and circumstances of the scandal surrounding the grants and contributions audit at Human Resources Development Canada (HRDC), which dominated media, parliamentary, and public attention for many months.Good argues that the HRDC crisis of 2000 was the result of a complex series of factors, which transformed a fixable administrative matter into a scandal that generated media headlines alleging that the government had lost close to $1 billion in misallocated funds.The author contextualizes this crisis by looking at the dichotomies and contradictions inherent in public administration, and by proposing that certain trade-offs must be made in the administration of any public organization.Good skilfully weaves together into a coherent and comprehensible whole both theoretical and practical considerations in his analysis, drawing on recent literature in the field and capturing for the reader the nuances and complexities of public administration.The first and only extensive critical examination to date of the events surrounding the scandal at HRDC, this text offers an original and groundbreaking contribution to current scholarship on public administration and management in Canada.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.523 | 0.233 |
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".