Risk Management in Practice: A Multiple Case Study Analysis in Italian Municipalities
Bibliographic record
Abstract
This paper aims to analyse the ways in which risk management has been embedded in management control systems in Italian municipalities. Through a qualitative method, this study presents multiple case studies from six municipalities with two levels of analysis: content analysis on the information published on the institutional website, and interviews through a questionnaire with open and closed questions addressed to the public managers of the Italian municipalities selected. Moreover, the municipality respondents were classified into medium (two municipalities with over 50,000 inhabitants), medium-high (two municipalities with between 100,000 and 500,000 inhabitants), and high (one municipality with over 500,000 inhabitants). The multiple case studies reported show how there is not a strong level of integration between risk management and the management control system in use. This research is useful to sustain the debate about risk management in the public sector. It should help practitioners and scholars to cover their municipalities’ needs.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".