Quality and speed in administrative decision-making: Tension or balance?
Bibliographic record
Abstract
In various European countries such as France, Italy, and the Netherlands, lawmakers have adopted legislation in order to deal with the consequences of the economic crisis. These laws contain provisions aimed at speeding up administrative decision making and judicial proceedings which have an impact on various provisions of general administrative law. Alongside the aim of facing the economic crisis, these measures aim to make administrative law more up-to-date and ensure it meets the needs of contemporary society.However, acceleration measures concerning decision-making and judicial proceedings may clash with the need to preserve the quality of these proceedings. On the one hand, swift procedures can be considered to be one aspect of high-quality decision making. On the other hand, other aspects of quality such as public participation and the thorough consideration of all relevant aspects and interests, may be at risk when the speed of decision-making is the only focus of reforms.Quality and Speed in Administrative Decision-Making: Tension or Balance? presents six national perspectives on these issues, together with a comparative overview comparing and contrasting national approaches with regards to finding a balance between the pace of proceedings and the quality of administrative and judicial decisions.The book will be of interest to academics of European and comparative administrative law, as well as policy-makers at the national and European level.
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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 | 0.048 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".