Mass protests that have taken place over the past decade in various Western democracies have called into question the role of police in society, as officers have employed measures to contain rallies protesting for or against various issues. A number of these protests have resorted to violent means, resisting the police or protesting directly against their role and methods. The present study sought to investigate the prototypical representations of the police that lay citizens use to forge or desist identification with police officers. Social identification enables citizens to consider the police as ingroup members, facilitating respect for their authority. Conversely, identifying the police as outgroup precipitates resistance. The study involved 41 in-depth interviews carried out with citizens of Malta between May and June 2020. Thematic Networks Analysis revealed various points of consensus as well as a number of controversial themes. In particular, respondents demonstrated sceptical attitudes regarding policing on the beat for fear of overfamiliarity, rooted in introspective attributions projected at the police as merely human. Moreover, respondents expressed support for technological innovations that overcome natural psychological tendencies. The findings of this study suggest that seeking increasing trust in the police may be a red herring for policymakers. Rather, efforts should be directed at developing inter-objective systems, (e.g. body-cams), that overcome individual psychological propensities.
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http://dx.doi.org/10.1007/s12124-021-09632-w | DOI Listing |
Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges in effectively characterizing the modularity organization of brain networks and capturing varying dFC state patterns.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
The Cyprus Institute, Climate and Atmosphere Research Center, 2121, Nicosia, Cyprus.
The production of nitrogen oxides (NO = NO + NO ) is substantial in urban areas and from fossil fuel-fired power plants, causing both local and regional pollution, with severe consequences for human health. To estimate their emissions and implement air quality policies, authorities often rely on reported emission inventories. The island of Cyprus is de facto divided into two different political entities, and as a result, such emissions inventories are not systematically available for the whole island.
View Article and Find Full Text PDFJ Neurosci
January 2025
South China Normal University, Center for Studies of Psychological Application; School of Psychology; Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education
Int J Med Robot
February 2025
Department of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Genova, Italy.
Background: Medical simulation is relevant for training medical personnel in the delivery of medical and trauma care, with benefits including quantitative evaluation and increased patient safety through reduced need to train on patients.
Methods: This paper presents a prototype medical simulator focusing on ocular and craniofacial trauma (OCF), for training in management of facial and upper airway injuries. It consists of a physical, electromechanical representation of head and neck structures, including the mandible, maxillary region, neck, orbit and peri-orbital regions to replicate different craniofacial traumas.
Neural Netw
December 2024
College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, 310027, China. Electronic address:
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods.
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