Managing for Legitimacy: Agency Governance in Its “Deep” Constitutional Context
Bibliographic record
Abstract
Abstract Recent literature on bureaucratic structure has gone further than studying discretions given to bureaucrats in policy making, and much attention is now paid to understanding how bureaucratic agencies are managed. This article proposes that the way in which executive governments manage their agencies varies according to their constitutional setting and that this relationship is driven by considerations of the executive's governing legitimacy. Inspired by Charles Tilly (1984), the authors compare patterns of agency governance in Hong Kong and Ireland, in particular, configurations of assigned decision‐making autonomies and control mechanisms. This comparison shows that in governing their agencies, the elected government of Ireland's parliamentary democracy pays more attention to input (i.e., democratic) legitimacy, while the executive government of Hong Kong's administrative state favors output (i.e., performance) legitimacy. These different forms of autonomy and control mechanism reflect different constitutional models of how political executives acquire and sustain their governing legitimacy.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".