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Standard approaches to the analysis of crisis situations either take some psychological stance, where the individual is the unit of analysis, or they investigate groups of actors taking turns, where individuals act following their own interpretation of what others have done. Philosophers have characterized these two approaches as self-actional and interactional. Actions and interpretations clearly can be assigned to one or the other actor, which allows allocating the responsibility for a violent event to someone “culprit.” A radically different, rarely chosen approach is a transactional one, where each action is understood as joint action both in space and in time that cannot be decomposed into independent individual contributions. In this paper, following a sketch of the differences in the epistemological underpinnings between standard and transactional approaches, exemplifying analyses are presented and discussed from a violent encounter that left a streetcar passenger dead and a police officer before the courts of justice for homicide. Discussion topics include the attribution of cause and effect, understanding the historical trajectories of participant actors, and the consequences of analyzing events in terms of events (not substantive entities, and inter-actions).

Fatality resulting from violent conflicts poses a critical public health challenge in Nigeria, straining healthcare systems, disrupting resource allocation, and necessitating targeted interventions to curb the prevalence and strengthen community resilience. According to the Global Organized Crime and Terrorism Indices, Nigeria is ranked among the top countries most impacted by terrorism in 2020. Despite numerous studies on crimes in Nigeria, adequate attention has not been given to quantifying the patterns of fatalities due to conflict events. This work aims to unveil the subtle spatio-temporal pattern of fatality resulting from violent events in Nigeria over a quarter-century. A spatio-temporal mixed model within a Bayesian framework was adopted, and data was sourced from the Armed Conflict Location and Event Data Project. The study found existing temperate seasonality in the pattern of fatalities, with a high fatality impact in Autumn and Winter. Among all the events, sexual violence was the leading cause of fatality in the country. Findings identified spatial and temporal disparities in fatality, with the North-East geopolitical zone being the most exposed region over the years, and uneducated members of poorest households are relatively more at risk of these events. The identified factors and patterns could be relevant for designing sustainable intervention programs or response policies to mitigate violent events in Nigeria.

Background Africa is blessed with vast arable land and enriched with valuable natural resources encompassing both renewable (like water, forests, and fisheries) and non-renewable (such as minerals, coal, gas, and oil). Under the right conditions, a natural resource boom should serve as an important driver for growth, development, and the transition from cottage industry to factory output. However, despite its wealth, Africa is often associated with the notion of a resource curse. Negative outcomes are often linked with mineral wealth. This paper investigates the causes of adverse health outcomes in resource-rich regions. The study provides empirical support for the natural resource curse with particular emphasis on the environmental health risks in Africa. We explore the multifaceted connections among mineral deposits, environmental risks, conflict events and population dynamics, shedding light on the complexities of resource-rich areas. Results We amalgamate georeferenced data pertaining to 22 specific mineral deposits with information on the prevalence of reliance on compromised infrastructures at a spatial resolution of 0.5 ∘ × 0 . 5 ∘ for all of Africa between 2000 and 2017. Through comprehensive econometric analysis of environmental health risk factors, including reliance on contaminated water sources, open defecation, unimproved sanitation, particulate matter concentration, and carbon concentration, we uncover the intricate pathways through which mineral deposits impact public health. Our findings revealed the significant role of in-migration in mediating environmental health risks. Moreover, we found that the activities of extractive companies amplify certain environmental risks including reliance on unimproved sanitation and practices and particulate matter concentration. Conflict events emerge as a key mediator across all environmental health risks, underlining the far-reaching consequences of instability and violence on both local communities and the environment. Conclusion The study contributes to the discourse on sustainable development by unraveling the nuanced associations between mineral wealth and health challenges. By drawing attention to the intricate web of factors at play, we provide a foundation for targeted interventions that address the unique environmental and health challenges faced by mineral-rich communities.

Background Female Genital Mutilation/Cutting (FGM/C) concerns over 200 million women and girls worldwide and is associated with obstetric trauma and long-term urogynaecological and psychosexual complications that are often under-investigated and undertreated. The aim of this study was to assess the pelvic floor distress and the impact of pelvic floor and psychosexual symptoms among migrant women with different types of FGM/C. Methods This cross-sectional study was conducted between April 2016 and January 2019 at the Division of Gynaecology of the Geneva University Hospitals. The participants were interviewed on socio-demographic and background information, underwent a systematic gynaecological examination to assess the presence and type of FGM/C and eventual Pelvic Organ Prolapse (POP), and completed six validated questionnaires on pelvic floor and psychosexual symptoms (PFDI-20 and PFIQ7 on pelvic floor distress and impact, FISI and WCS on faecal incontinence and constipation, PISQ-IR and FGSIS on sexual function and genital self-image). The participants’ scores were compared with scores of uncut women available from the literature. The association between selected variables and higher scores for distress and impact of pelvic floor symptoms was assessed using univariate and multivariable linear regression models. Results 124 women with a mean age of 31.5 (± 7.5), mostly with a normal BMI, and with no significant POP were included. PFDI-20 and PFIQ-7 mean (± SD) scores were of 49.5 (± 52.0) and 40.7 (± 53.6) respectively. In comparison with the available literature, the participants’ scores were lower than those of uncut women with pelvic floor dysfunction but higher than those of uncut women without such disorders. Past violent events other than FGM/C and forced or arranged marriage, age at FGM/C of more than 10, a period of staying in Switzerland of less than 6 months, and nulliparity were significantly associated with higher scores for distress and impact of pelvic floor symptoms, independently of known risk factors such as age, weight, ongoing pregnancy and history of episiotomy. Conclusions Women with various types of FGM/C, without POP, can suffer from pelvic floor symptoms responsible for distress and impact on their daily life. Trial registration . The study protocol was approved by the Swiss Ethics Committee on research involving humans (protocol n°15-224).

This paper highlights the dynamics in sequences of violence using the perspective of communicative constructivism as a theoretical framework. The main focus of this paper is on the role of third parties in violent sequences. Using the example of two smartphone videos that show different types of violence (a brawl in a less institutionalized setting, execution in a military context), the third parties in these violent encounters are analyzed. Considerations from communicative constructivism are pursued to make a distinction between contextuRal and contextual third parties. The former are physically and performatively involved in the sequences of violence while the latter have consequences for the dynamics of violence due to their semantic and symbolic representation. ContextuRal and contextual thirds are to be understood not as static roles but as “modes of action” that have a different influence on the sequences of violence and can be better comprehended in their specific temporal-sequential embeddedness into the trajectories of violence. Therefore, the analyses emphasize that violence can be understood as communicative action in which those involved within the “triad of violence” refer to each other in their interactions.

Research shows a strong association between traumatic life experience and mental health and important gender differences in that relationship in the western European Diaspora; but much less is known about these relationships in other settings. We investigate these relationships in a poor rural Asian setting that recently experienced a decade-long armed conflict. We use data from 400 adult interviews in rural Nepal. The measures come from World Mental Health survey instruments clinically validated for this study population to measure depression, posttraumatic stress disorder, and intermittent explosive disorder. Our results demonstrate that traumatic life experience significantly increases the likelihood of mental health disorders in this setting, and that these traumatic experiences have a larger effect on the mental health of women than men. These findings offer important clues regarding the potential mechanisms producing gender differences in mental health in many settings.

In this paper, we present the overview of the DA-VINCIS 2023 shared task which was organized at IberLEF 2023 and co-located in the framework of the 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023). The main aim of this task is to promote the research on developing automatic solutions for detecting violent events in social networks. Two subtasks were considered: (i) A binary classification task aimed to determine whether or not a tweet is about a violent incident; and (ii) A multi-label multi-class classification task in which the category(ies) of a violent incident must be identified. A multimodal manual annotated corpus comprising both tweets and images associated to them was provided to the participants. A total of 15 systems were submitted for the final evaluation phase. Competitive results were obtained for both subtasks, the higher ones were in the binary classification task. Corpora and results are available at the shared task website at https://codalab.lisn.upsaclay.fr/competitions/11312.

Background Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V) vs . non-violence-related injuries (NV) is thus a relevant task to the ends of devising alerting mechanisms to track (and prevent) violence episodes. Methods We present ViDeS (so dubbed after Violence Detection System ), a system to detect episodes of violence from narrative texts in emergency room reports. It employs a deep neural network for categorizing textual ER reports data, and complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of terms herein, along with syntactic and semantic information. The system has been validated on real data annotated with two sorts of information: about the presence vs. absence of violence-related injuries, and about some semantic roles that can be interpreted as major cues for violent episodes, such as the agent that committed violence, the victim, the body district involved, etc.. The employed dataset contains over 150K records annotated with class (V,NV) information, and 200 records with finer-grained information on the aforementioned semantic roles. Results We used data coming from an Italian branch of the EU-Injury Database (EU-IDB) project, compiled by hospital staff. Categorization figures approach full precision and recall for negative cases and.97 precision and.94 recall on positive cases. As regards as the recognition of semantic roles, we recorded an accuracy varying from.28 to.90 according to the semantic roles involved. Moreover, the system allowed unveiling annotation errors committed by hospital staff. Conclusions Explaining systems’ results, so to make their output more comprehensible and convincing, is today necessary for AI systems. Our proposal is to combine distributed and symbolic (frame-like) representations as a possible answer to such pressing request for interpretability. Although presently focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.

In everyday life, it is rather rare for conflicts to escalate and for violent acts to occur between the parties involved. And when it does, there are usually only a few people involved. Videos of such events are therefore still relatively easy to analyse (despite their complexity) because the events have a centre (monocentric) and the action is sequential, driven only by the interaction dynamics of the participants. However, this looks completely different when the videos show a very large number of people (i.e., everything from 20 people upwards), who belong to different groups with different interests, meet in a specific, pre-structured confined space, and conflicts and violent actions repeatedly arise at different places in the action. Such events often have several and changing centres (polycentric), and there is often an alternation between escalation and relaxation. In addition, several strands of action run parallel to each other and also influence each other (intermediary). Videos of such events pose enormous challenges to social scientists. In my article, based on the analysis of a video capturing the storm of the singling out facility in a soccer stadium, I will show how such complex escalation events can be effectively analysed. The analysis itself consists of a combination of video, interview, and dispositive analysis. The paper will also show why escalation processes cannot be understood and explained solely from what happens in the situation, but the meso and macro levels in which the situation is embedded must always be taken into account as well.

This paper presents the overview of the DA-VINCIS 2022 task, organized at IberLEF 2023 and co-located with the 38th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2022). DA-VINCIS challenged participants to develop automated solutions for the detection of violent events mentioned in social networks. We released a novel corpus collected from Twitter and manually labeled with 4 categories of violent incidents (plus the no-incident label). The shared task focused on the Mexican variant of Spanish and it was divided into two tracks: (1) a binary classification task in which users had to determine whether tweets were associated to a violent incident or not; and (2) a multi-label classification task in which the category of the violent incident should be spotted. More than 40 teams registered for the task and 12 participants submitted predictions for the final phase. Very competitive results were reported in both sub tasks, where transformer-based solutions obtained the best results. Corpora and results are available at the shared task website at https://codalab.lisn.upsaclay.fr/competitions/2638.