Dutch mayors are expected to act both as moral person and as moral managers. However, the extent to which council members express such requirements when selecting candidates remains underexplored. To identify possible changes in these expectations following the implementation of a 2016 integrity law, which made the mayor responsible for ‘advancing the administrative integrity of the municipality’, the current article quantitatively analyses 349 vacancy texts for Dutch mayoralty for the time period 2008-2019. Unexpectedly, the authors find that moral person requirements still feature prominently in job advertisements, but that attention is declining. In addition, they find a significant shift from moral-person requirements to moral-management requirements, which indicates that vacancy texts mirror the increasing importance of moral leadership requirements for Dutch mayors. Further, whereas the complex integrity concept requires tailoring to the unique circumstances in municipalities, the authors find that councilors make little effort to provide their own definition of integrity in vacancy texts, which leaves ample room for local customization. |
Artikel |
Moreel persoon of moreel manager?Een kwantitatieve analyse van de aan burgemeesters gestelde integriteitseisen, 2008-2019 |
Tijdschrift | Beleid en Maatschappij, Aflevering 4 2020 |
Trefwoorden | ethical leadership, moral management, Integrity, Mayors, The Netherlands |
Auteurs | Simon Jacobs BSc en Dr. Niels Karsten |
SamenvattingAuteursinformatie |
Artikel |
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Tijdschrift | Beleid en Maatschappij, Aflevering 3 2020 |
Trefwoorden | dirty data, predictive policing, CAS, discrimination, ethnic profiling |
Auteurs | Mr. Abhijit Das en Mr. dr. Marc Schuilenburg |
SamenvattingAuteursinformatie |
Predictive tools as instruments for understanding and responding to risky behaviour as early as possible are increasingly becoming a normal feature in local and state agencies. A risk that arises from the implementation of these predictive tools is the problem of dirty data. The input of incorrect or illegally obtained information (‘dirty data’) can influence the quality of the predictions used by local and state agencies, such as the police. The article focuses on the risks of dirty data in predictive policing by the Dutch Police. It describes the possibilities to prevent dirty data from being used in predictive policing tools, such as the Criminality Anticipation System (CAS). It concludes by emphasizing the importance of transparency for any serious solution looking to eliminate the use of dirty data in predictive policing. |