Bibliographic record
Abstract
This article deals with the relationships between the exercise of administrative discretion and the implementation of a policy. Chapter I defines administrative discretion as a power to make a choice in a particular case. This choice may be technical or political but in both instances relates to the implementation of a policy. The exercise of discretion is also situated within a system under the Rule of Law using H.L.A. Hart's concepts of primary and secondary rules. Chapter II deals with the exercise of discretion in relation to policy. First, if refers to K.C. Davis' model of confining, structuring and checking discretion. To confine discretion is to set the limits within which it should be exercised. To structure it is define the manner by which it is to be exercised notably in opening the decision-making process. To check discretion is to subject the decision to another authority. The next three sections of this chapter are concerned with legislative, regulatory and administrative policy. The first section studies legislative expressions of policy and their impact on the exercise of discretion. Secondly, the question of the choice between regulation and administrative discretion is analysed as is the control over that choice and the nature of regulation over it is decided to adopt it. Finally, the impact of an administrative discretion is seen when attacked by the citizen on the grounds that it fetters discretion, constitutes bias or when relied upon by the citizen. It is seen that in most cases, the administrator may structure his discretionary power in a manner respected by the courts.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| 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".