Understanding street-level managers’ compliance: a comparative study of policy implementation in Switzerland, Italy, Germany, and Israel
Bibliographic record
Abstract
This study focuses on street-level managers’ (SLMs) compliance with COVID-19 measures in Switzerland, Germany, Italy, and Israel, in order to better understand their role during policy implementation. Responsible for the direct delivery of public services, street-level organizations serve as the operational arm of the state in general and as the frontline of government policy in times of crisis. SLMs who occupy the top managerial tier within their organization are understudied, although they exert a significant influence on everyday public life. The data comprise 399 “compliance stories” based on interviews with managers in nurseries, schools, health care and welfare offices, police stations, and care homes. Using “codebook thematic analysis,” we identify various levels of (non)compliance and several prominent explanatory factors that shape (non)compliance. Data show that even when asked about particularly challenging measures, managers reported that their organization had been noncompliant (either full or partial) in only about a quarter of the stories. Three influences emerge as primary barriers to compliance—a lack of resources, managers’ relationships with clients, and the perception of the measure’s effectiveness. Emphasizing that SLMs often act as local policy entrepreneurs using their discretion to solve problems and serve the public, our findings further demonstrate the crucial role they play in shaping the face of the government for the people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".