Bibliographic record
Abstract
I began thinking about this book thirty years ago.I was asked by a Government of Canada agency to work with colleagues to assemble different teams of leading scholars from Europe, the United States, Australia, New Zealand, and Canada to review various measures governments in several countries introduced to overhaul their operations.The teams published three books of essays on governance and public administration.The experience piqued my interest in comparative research.My research interest led me to Paris (at oecd); to several visits to Washington, dc, to carry out interviews; to extended stays in Britain (Oxford and London); and to Ottawa, Canada, for several years as a visiting fellow in a central agency and as deputy head of a Management Development Centre with the Government of Canada.This book flows out from the encounters and interviews I conducted over the years with government officials.What struck me from the experience was how career civil servants react to change.I soon realized that it was simply not possible to define an all-encompassing theory to explain their behaviour.Public choice theory and the politics-administration dichotomy theory explain some situations some of the time but not all situations all the time.We still do not have a grand general theory to explain the behaviour of government officials, and I do not see one on the horizon.This is not to suggest that the public administration field has little to offer.In fact, it has a great deal to offer, and we should continue working toward a general theory.Examining what works and what does not is a step in that direction.It is a good thing the
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.270 | 0.133 |
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".