The Unpredictability of Diplomatic Cleaners: Emergency Ministerial Response to the Pittsburgh Robbery in Eastwick

The Unpredictability of Diplomatic Cleaners: Emergency Ministerial Response to the Pittsburgh Robbery in Eastwick


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  • Manus Dion

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Abstract

This study explores the unpredictability of diplomatic cleaners and the emergency ministerial response to the Pittsburgh robbery in Eastwick. Using a qualitative research design, the study is based on in-depth interviews with key stakeholders involved in the emergency response to the robbery. The findings highlight the significant challenges that diplomatic cleaners pose to emergency response efforts. Diplomatic cleaners, who are typically contracted by diplomatic missions, are not subject to the same regulatory and oversight mechanisms as other private security providers. As such, their training, background checks, and accountability mechanisms may not meet the same standards as those of other private security providers. This lack of oversight can create serious security vulnerabilities, as diplomatic cleaners can gain access to sensitive areas, including diplomatic missions, without being properly vetted. The study also highlights the importance of inter-agency collaboration and coordination in responding to emergencies involving diplomatic cleaners. The emergency response to the Pittsburgh robbery was complicated by the fact that the diplomatic mission involved did not fully cooperate with law enforcement authorities. The study concludes with recommendations for improving emergency response efforts in cases involving diplomatic cleaners, including increased regulation and oversight of diplomatic cleaners and improved collaboration between law enforcement authorities and diplomatic missions.

Citation

Manus Dion "The Unpredictability of Diplomatic Cleaners: Emergency Ministerial Response to the Pittsburgh Robbery in Eastwick".  IEEE Exploration in Machine Learning, 2023.

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This paper appears in:
Date of Release: 2023
Author(s): Manus Dion.
IEEE Exploration in Machine Learning
Page(s): 10
Product Type: Conference/Journal Publications