A Practical Basis for Public Service Ethics J.I.Gow
Bibliographic record
Abstract
The study of public service ethics has received new vigour in recent years because of the changes in values that have been introduced by politicians, business elites in the last twenty five years, in 1 developed countries but also to some extent around the world. It is a difficult subject, but one which rewards attention. As public administration is purposeful action, so a subject that leads one to reflect more systematically on what the purposes may be and how they are affected by what we do. In Canada, it takes temerity to venture into this field, such is the dominance in it of Ken Kernaghan, who stands over it like the colossus of Rhodes ( or Niagara, if you prefer). Nevertheless, the occasions that bring us to study a subject, as well as our own experience mean that each of us has a perspective different although similar to those of others. In this case, my look at a possible practical basis for public service ethics will advance in four stages. First, as always some definitions are needed, both for ethics and for values, as well as the questions these subjects raise and the most useful ways of looking at them. Second, the values identified in public administration will be considered, including a development on the public interest. The third part deals with the practical implications of public service ethics and their feasibility,
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".