Bibliographic record
Abstract
Abstract The pivotal aim of this book is to explain the creation, development, and impact of good governance from a conceptual, principal perspective and in the context of national administrative law. Three lines of reasoning have been worked out: developing the concept of good governance; specification of this concept by developing principles of good governance; and implementation of these principles of good governance on the national level. In this phase of further development of good governance, it is important to have a clear concept of good governance, presented in this book as the third cornerstone of a modern state, alongside the concepts of the rule of law and democracy. That is a rather new national administrative law perspective which is influenced by regional and international legal developments; thus, we can speak about good governance as a multilevel concept. But the question is: how is this concept of good governance further developed? Six principles of good governance (which in a narrower sense also qualify as principles of good administration) have been further specified in a systematic way, from a legal perspective. These are the principles of properness, transparency, participation, effectiveness, accountability, and human rights. Furthermore, the link has been made with integrity standards. The important developments of each of these principles are described on the national level in Europe, but also in countries outside Europe (such as Australia, Canada, and South Africa). This book gives a systematic comparison of the implementation of the principles of good governance between countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".