A Study of the Co-Ordination of Mission, Objectives and Targets in UK Executive Agencies
Bibliographic record
Abstract
The growth of Executive Agencies (agencies) in the UK public sector has been rapid. While the first agency was established as recently as 1988, by September 1998 over 75 per cent of civil servants were working in such organisations. They were created to improve the efficiency and effectiveness of the delivery of central government services. The efficacy of a rational management model was promulgated and the need to plan and control the performance of agencies, as a basis for performance improvement, articulated. This led to a greater focus on targeting and measuring performance. This paper provides an outline of the case for, and potential problems, of the use of a highly rational model of management in the public sector, with its focus on quantification. Through an empirical analysis of 48 planning documents, the degree to which the mission statements, objectives and targets in the Corporate and business Plans of agencies are coordinated, or linked, in a rational manner is identified. The main finding of the research is that although significant gaps exist, the planning documents of executive agencies appear to be more coordinated in terms of their use of objectives and targets than was evidence in earlier research in other parts of the public sector. Key Words: Execuitve agencies; Public sector accounting; Performance Targets; Performance measurement; Planning and control
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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 | 0.010 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".