Street-level bureaucrats as strategy shapers in social and health service delivery: Empirical evidence from six countries
Bibliographic record
Abstract
In this research article, we discuss the social construction of public services within the conceptual and theoretical framework provided by Lipsky. We are interested in what it means if/when street-level bureaucrats (SLBs) have an active role in the construction of a service system. We argue that there are multiple realities in terms of the construction of public services and we approach the question by deploying Lipsky’s notion on SLBs by empirically analysing middle managers’ views on how SLBs act and their role in this construction process. This paper is based on empirical interviews (N=100) collated in 2012 from Barcelona, Den Bosch, Glasgow, Melbourne, Toronto, Vancouver, the Greater London area, and the US state of Vermont. The research collation strategy was to include reform-oriented cities and countries in terms of developing and delivering public services. We found that SLBs have three different kinds of strategies in the construction process: policy-making, working practices, and professionalism. We found that there are no conflicts arising from SLBs’ beliefs, organisational demands, and rules and regulations. Instead, SLBs try to solve conflicts or bridge gaps between policy-making and practical work in the boundaries between SLBs and service users. Based on this research, the role of SLBs and the built-in flexibility and agility of public service leadership and organisations must be addressed and developed further. The role of organisational learning and changing organisational cultures must also be scrutinized in the context of public service systems. The analysis of professional resilience in the context of public services planning needs more theoretical and empirical attention. The resilience of organisations and the capacities of SLBs need to be researched more. Finally, there is the need for better cultivation of the role of the SLBs and service users with regard to accountability aspects (horizontal and vertical).
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".