Rational Management, Performance Targets and Executive Agencies: Views from Agency Chief Executives in Northern Ireland
Bibliographic record
Abstract
The way in which central government services are delivered in both Britain and Northern Ireland has changed significantly since 1988. Executive Agencies have been created with the aim of improving the efficiency and effectiveness of service delivery, with changes being supported by an increasing focus on the rational management model as a basis for improving management in the public sector. This paper is a case study of nine agencies within the Northern Ireland ‘family of agencies’ and is the first study of its type in the UK. It presents the results of a series of interviews with agency Chief Executives that attempted to identify perceptions with respect to the development, use and impact of mission statements, objectives, targets and performance measures (components of a rational management approach). The main findings of the research include: Northern Ireland Chief Executives perceive an increased focus on quantification since agencification; this focus is viewed as helpful in providing a basis for improving management; systems in practice are considered to be much more flexible than a rigid management model would normally suggest; and, it is thought that the potential adverse consequences of such an approach can be managed.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".