Service charters - global convergence or national divergence? A comparison of initiatives in Australia, the United Kingdom and the United States
Bibliographic record
Abstract
This paper examines the transfer of NPM strategies by comparing service charter initiatives in the United Kingdom (UK), United States (US) and Australia. These three countries, together with Canada and New Zealand, are part of what has been described as the "core" New Public Management (NPM) policy community (Common 1998). Service Charters are a NPM strategy intended to change the culture of public service delivery to focus on the needs of the users, identified as 'clients' or 'customers'. The objectives are to make service providers more responsive to users by guaranteeing specific standards for service delivery, providing a substitute for competition and a benchmark for measuring service quality. The first section examines the historical and political context of the development of the Citizen's Charter and Service First programs in the UK, Customer Service Plans in the US and Government Service Charters in Australia. The second section of the paper explores the similarities and differences between these charter initiatives based on analysis of public documents. There is evidence of convergence at the ideological level as managerial values underpin the service charter frameworks in all three jurisdictions (Walsh 1994; Pollitt 1995; Kettl 1997). Despite drawing from a similar toolkit influenced by private sector techniques, significant differences between the country contexts have resulted in divergent strategies. Timing in the three countries examined suggests that national politics rather than global policy convergence is more significant in explaining the development of service charters. This case study provides evidence of policy transfer rather than policy convergence (Common 1998). The final section of the paper considers the limitations of the customer service model. Monitori ng quality is central to the programs in all three countries. Performance monitoring is essentially a quantitative methodology that requires criteria and indicators for measuring the quality of service delivery and program outcomes. Two problems are considered. The first is the difficulty of specifying and measuring service quality. The second problem is that quality indicators derived from services marketing and management research do not take into account the characteristics of public services. 2
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".