搜索结果：Violence and victims

Violence descriptions in literature offer valuable insights for a wide range of research in the humanities. For historians, depictions of violence are of special interest for analyzing the societal dynamics surrounding large wars and individual conflicts of influential people. Harvesting data for violence research manually is laborious and time-consuming. This study is the first one to evaluate the effectiveness of large language models (LLMs) in identifying violence in ancient texts and categorizing it across multiple dimensions. Our experiments identify LLMs as a valuable tool to scale up the accurate analysis of historical texts and show the effect of fine-tuning and data augmentation, yielding an F1-score of up to 0.93 for violence detection and 0.86 for fine-grained violence categorization.

In 2021, psychological violence was the most prevalent form of intimate partner violence (IPV) suffered by women in Mexico. The consequences of psychological IPV can include low self-esteem, depression, and even potential suicide. It is, therefore, crucial to identify the most relevant risk and protective factors of psychological IPV against women in Mexico. To this end, we adopt an ecological approach and analyze the role of a wide range of factors across four interrelated levels: Individual, relationship, community, and societal. We construct a multidimensional data set with 61,205 observations and 59 variables by integrating nationally representative data from the 2021 Mexican Survey on the Dynamics of Household Relationships with nine additional sources. For model estimation and factor selection, we combine model-based boosting with stability selection. Our findings reveal that women who were exposed to violence in childhood and whose partners were exposed to violence in childhood face a heightened risk of psychological IPV. These findings highlight the critical yet often overlooked role of childhood violence exposure for psychological IPV risk. Additionally, we confirm the rol

Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relationships. With the rise of social media as a key outlet for DV victims to disclose their experiences, online self-disclosure has emerged as a critical yet underexplored avenue for support-seeking. In addition, existing research lacks a comprehensive and nuanced understanding of DV self-disclosure, support provisions, and their connections. To address these gaps, this study proposes a novel computational framework for modeling DV support-seeking behavior alongside community support mechanisms. The framework consists of four key components: self-disclosure detection, post clustering, topic summarization, and support extraction and mapping. We implement and evaluate the framework with data collected from relevant social media communities. Our findings not only advance existing knowledge on DV self-disclosure and online support provisions but also enable victim-centered digital interventions.

Existing models of political violence often emphasize discrete transitions, when conflicts emerge, escalate, or subside, without considering the longer trajectories of violence that accumulate across time and space. This paper introduces a spatially explicit longitudinal sequence analysis to address this gap. Using event-level data from the Armed Conflict Location and Event Dataset covering Africa from 1997 to 2024, we classify locations according to the intensity and spatial concentration of violence, tracing how these states evolve into distinct conflict trajectories. Applying optimal matching and clustering techniques, we identify six recurrent patterns ranging from short-lived, localized outbreaks to protracted high-intensity conflicts. We further assess how these trajectories align across neighboring areas, revealing evidence of spatial interdependence, particularly in border regions. By highlighting the temporal rhythms and geographic linkages of political violence, the study advances conflict research beyond isolated transitions and provides a framework for understanding the life cycles of violence.

We study how religious competition-defined as the entry of a religious organization with innovative worship practices into a predominantly Catholic municipality-affects domestic violence. Using municipality-level data from Colombia and a two-way fixed effects design, we find that the arrival of the first non-Catholic church leads to a significant reduction in reported cases of domestic violence. We argue that religious competition incentivizes churches to adopt and diffuse norms and practices that more effectively discourage such violence. Effects are largest in municipalities with smaller, younger, and more homogeneous populations-contexts that facilitate both intense competition and norm diffusion. Consistent with this mechanism, areas with more new non-Catholic churches exhibit greater rejection of domestic violence-particularly among the religiously observant-and higher female labor force participation. These findings contribute to the literature on the cultural determinants of domestic violence by identifying religious competition as a catalyst for cultural change.

We examine how violence affects migration flows and, crucially, how it reshapes the strength of migration networks -- measured by the intensity of migration between areas, accounting for the fact that some routes become more prominent or fade over time -- an aspect traditional studies overlook. Using a novel network algorithm and Mexican census data from 2005 to 2020, we first quantify changes in the strength of domestic and international migration networks across all Mexican municipalities. We exploit variation in local homicide rates, using exogenous fuel price increases and municipalities' proximity to oil pipelines as instruments, to estimate the causal impact of violence on migration. During our study period, following intensified government crackdowns on drug trafficking organizations, many criminal groups fragmented and turned toward large-scale oil theft, driving sharp increases in violence in areas with oil pipelines, particularly when fuel prices rose. The findings show that rising violence increased emigration flows, predominantly within Mexico, and strengthened the intensity of emigration networks both domestically and toward the United States. Although violent municipa

Domestic violence is a silent crisis in the developing and underdeveloped countries, though developed countries also remain drowned in the curse of it. In developed countries, victims can easily report and ask help on the contrary in developing and underdeveloped countries victims hardly report the crimes and when it's noticed by the authority it's become too late to save or support the victim. If this kind of problems can be identified at the very beginning of the event and proper actions can be taken, it'll not only help the victim but also reduce the domestic violence crimes. This paper proposed a smart system which can extract victim's situation and provide help according to it. Among of the developing and underdeveloped countries Bangladesh has been chosen though the rate of reporting of domestic violence is low, the extreme report collected by authorities is too high. Case studies collected by different NGO's relating to domestic violence have been studied and applied to extract possible condition for the victims.

Ongoing school closures and gradual reopenings have been occurring since the beginning of the COVID-19 pandemic. One substantial cost of school closure is breakdown in channels of reporting of violence against children, in which schools play a considerable role. There is, however, little evidence documenting how widespread such a breakdown in reporting of violence against children has been, and scant evidence exists about potential recovery in reporting as schools re-open. We study all formal criminal reports of violence against children occurring in Chile up to December 2021, covering physical, psychological, and sexual violence. This is combined with administrative records of school re-opening, attendance, and epidemiological and public health measures. We observe sharp declines in violence reporting at the moment of school closure across all classes of violence studied. Estimated reporting declines range from -17% (rape), to -43% (sexual abuse). While reports rise with school re-opening, recovery of reporting rates is slow. Conservative projections suggest that reporting gaps remained into the final quarter of 2021, nearly two years after initial school closures. Our estimates s

We investigate whether female judges analyze domestic violence cases differently from their male peers. Using data from São Paulo, Brazil, between 2011 and 2019, we find that a domestic violence case assigned to a female judge is 28% (9.7 p.p.) more likely to result in a conviction than a case assigned to a male judge with similar career characteristics. To show that this decision gap rises due to different gender perspectives about domestic violence and not because female judges are stricter than their male counterparts in all rulings, we compare it against the gender conviction-rate gap in similar types of crime. We find that this gap for domestic violence cases is larger than the same gap for other physical assault cases (8.3 p.p.). Furthermore, we analyze two explanatory channels for this gender conviction-rate gap for domestic violence cases: gender-based differences in evidence interpretation and gender-based sentencing criteria. We also find that female judges write longer sentences, schedule more hearings, and write more judicial documents than their male peers when analyzing domestic violence cases. Lastly, we find that the gender conviction-rate gap has no significant imp

It might be intuitive to expect that small or reimbursed financial loss resulting from credit or debit card fraud would have low or no financial impact on victims. However, little is known about the extent to which financial fraud impacts victims psychologically, how victims detect the fraud, which detection methods are most efficient, and how the fraud detection and reporting processes can be improved. To answer these questions, we conducted a 150-participant survey of debit/credit card fraud victims in the US. Our results show that significantly more participants reported that they were impacted psychologically than financially. However, we found no relationship between the amount of direct financial loss and psychological impact, suggesting that people are at risk of being psychologically impacted regardless of the amount lost to fraud. Despite the fact that bank or card issuer notifications were related to faster detection of fraud, more participants reported detecting the fraud after reviewing their card or account statements rather than from notifications. This suggests that notifications may be underutilized. Finally, we provide a set of recommendations distilled from victim

This paper examines the long-term effects of prenatal, childhood, and teen exposure to electoral violence on health and human capital. Furthermore, it investigates whether these effects are passed down to future generations. We exploit the temporal and spatial variation of electoral violence in Kenya between 1992 and 2013 in conjunction with a nationally representative survey to identify people exposed to such violence. Using coarsened matching, we find that exposure to electoral violence between prenatal and the age of sixteen reduces adult height. Previous research has demonstrated that protracted, large-scale armed conflicts can pass down stunting effects to descendants. In line with these studies, we find that the low-scale but recurrent electoral violence in Kenya has affected the height-for-age of children whose parents were exposed to such violence during their growing years. Only boys exhibit this intergenerational effect, possibly due to their increased susceptibility to malnutrition and stunting in Sub-Saharan Africa. In contrast to previous research on large-scale conflicts, childhood exposure to electoral violence has no long-term effect on educational attainment or hou

When the prices of cereal grains rise, social unrest and conflict become likely. In rural areas, the predation motives of perpetrators can explain the positive relationship between prices and conflict. Predation happens at places and in periods where and when spoils to be appropriated are available. In predominantly agrarian societies, such opportune times align with the harvest season. Does the seasonality of agricultural income lead to the seasonality of conflict? We address this question by analyzing over 55 thousand incidents involving violence against civilians staged by paramilitary groups across Africa during the 1997-2020 period. We investigate the crop year pattern of violence in response to agricultural income shocks via changes in international cereal prices. We find that a year-on-year one standard deviation annual growth of the price of the major cereal grain results in a harvest-time spike in violence by militias in a one-degree cell where this cereal grain is grown. This translates to a nearly ten percent increase in violence during the early postharvest season. We observe no such change in violence by state forces or rebel groups--the other two notable actors. By fu

This paper explores the spatial and temporal diffusion of political violence in North and West Africa. It does so by endeavoring to represent the mental landscape that lives in the back of a group leader's mind as he contemplates strategic targeting. We assume that this representation is a combination of the physical geography of the target environment, and the mental and physical cost of following a seemingly random pattern of attacks. Focusing on the distance and time between attacks and taking into consideration the transaction costs that state boundaries impose, we wish to understand what constrains a group leader to attack at a location other than the one that would seem to yield the greatest overt payoff. By its very nature, the research problem defies the collection of a full set of structural data. Instead, we leverage functional data from the Armed Conflict Location and Event Data project (ACLED) dataset that, inter alia, meticulously catalogues violent extremist incidents in North and West Africa since 1997, to generate a network whose nodes are administrative regions. These nodes are connected by edges of qualitatively different types: undirected edges representing geogr

Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new Police Violence Frames Corpus of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim's criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim's race and that they were unarmed. We discover temporary spikes in these injustice frames near high-profile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions.

Objective: Gun violence is a serious public health problem in the United States. The Gun Violence Archive (GVA) provides detailed geographic information, while the National Violent Death Reporting System (NVDRS) offers demographic, socioeconomic, and narrative data on gun homicides. We developed and tested a method for merging datasets to inform analysis and strategies to reduce gun violence rates in the United States. Methods: After preprocessing the data, we used a probabilistic record linkage program to link records from the GVA (n = 36,245) with records from the NVDRS (n = 30,592). We evaluated sensitivity (the false match rate) by using a manual approach. Results: The linkage returned 27,420 matches of gun violence incidents from the GVA and NVDRS datasets. Because of restricted details accessible from GVA online records, only 942 of these matched records could be manually evaluated. Our framework achieved a 90.12% (849 of 942 accuracy rate in linking GVA incidents with corresponding NVDRS records. Practice Implications: Electronic linkage of gun violence data from 2 sources is feasible and can be used to increase the utility of the datasets.

Over the last decade, the number of randomized trials of programs to reduce intimate partner violence (IPV) has grown precipitously. However, most trials continue to measure and code violence using standards originally designed for global prevalence surveys. This choice may have consequences in terms of bias, power, and efficiency of trial estimates and may limit what we can learn about how programs are working. In this paper, we return to first principles to develop a generative model for violence reduction. We then use this model to better understand trade-offs in outcome coding choices via simulation. We re-analyze results from seven recent trials in Southern and Eastern Africa to highlight some of our findings. We conclude with a discussion of key take-aways for trialists.

This paper applies new and recently introduced approaches to study trends in gun violence in the United States. We use techniques in both the time and frequency domain to provide a more complete understanding of gun violence dynamics. We analyze gun violence incidents on a state-by-state basis as recorded by the Gun Violence Archive. We have numerous specific phenomena of focus, including periodicity of incidents, locations in time where behavioral changes occur, and shifts in gun violence patterns since April 2020. First, we implement a recently introduced method of spectral density estimation for nonstationary time series to investigate periodicity on a state-by-state basis, including revealing where periodic behaviors change with time. We can also classify different patterns of behavioral changes among the states. We then aim to understand the most significant shifts in gun violence since numerous key events in 2020, including the COVID-19 pandemic, lockdowns, and periods of civil unrest. Our dual-domain analysis provides a more thorough understanding and challenges numerous widely held conceptions regarding the prevalence of gun violence incidents.

According to WHO (2013), in general 30% of all women worldwide who have been in a relationship have experienced physical and/or sexual violence by their intimate partner. However, only a small percentage of intimate partner violence (IPV) victims report it to the police. This phenomenon of under-reporting is known as ``dark figure''. This paper aims to investigate the factors associated with the reporting decision of IPV victims to the police in Brazil using the third wave of the ``Pesquisa de Condições Socioeconômicas e Violência Doméstica e Familiar contra a Mulher ($PCSVDF^{Mulher}$)''. Using a bivariate probit regression model with sample selection, we found that older white women, those who do not tolerate domestic violence, and women who have experienced physical violence are more likely to report IPV to the police. In contrast, married women, those with partners who abuse alcohol and those who witnessed or knew that their mothers had experienced IPV, are less likely to report it to law enforcement.

Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provided by emergency radiology fellowship-trained physicians. Our dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. Our best model predicts IPV a median of 3.08 years before violence prevention program entry with a sensitivity of 64% and a specificity of 95%. We conduct error analysis to determine for which patients our model has especially high or low performance and discuss next steps for a deployed clinical risk model.