Jerry M. Stott

Violence remains a central criminal justice and public health concern, yet policy discussions often narrow analysis to crime, punishment, and individual behavior. A broader framework is needed. Manuscripts in the special issue examine violence as a product of upstream political choices, institutional design, fiscal priorities, fragmented governance, unequal exposure to injury, access to health care, government services availability, and political representation. Across varied settings, authors show how local political monopoly, structural inequality, weak reintegration systems, unstable interagency coordination, and contested policy choices shape exposure to injury, death, coercion, victimization, and chronic harm. A second contribution concerns definition and measurement. Categories of violence, rules of reporting, agency jurisdiction, and available data are never neutral. Decisions about what counts, who counts, and which harms become visible influence criminal justice practice, research findings, public narratives, and policy agendas. Public health offers a useful framework because measurable harms, exposures, and institutional contact across health, justice, and social service systems can anchor analysis while still recognizing power, politics, and administrative discretion. Together, the manuscripts move beyond event-based and offender-centered explanations. Violence emerges as politically organized harm, patterned by governance and reproduced through institutions.

Violence remains a long-standing wicked problem because political, institutional, and fiscal choices shape how violence is defined, measured, and addressed. This special issue synthesizes contributions and uses public health framing to reposition violence as a measurable outcome. We draw together work on deaths of despair, structural violence, firearm homicide, restorative justice, recidivism, institutional cycling, and interagency response to identify patterns across settings and outcomes. Violence emerges as a downstream product of political monopoly, fragmented governance, weak accountability, fiscal choices, and unstable institutional support. Violence is also a contested object of definition, measurement, and response, with political actors shaping visible, credible, punishable, or preventable harms. Public health framing does not remove politics; rather, it makes room for partisan reframing, anchoring policy discussion in measurable injury, exposure, and preventable risk. Symbolic policies become the safe political option without reducing underlying harm. Partisan politics rewards symbolic action, creating short policy horizons and selective policy attention, which then makes evolving violence difficult to resolve. Violence reduction requires more than episodic solutions; it requires better measurement of acute and chronic harms, and governance capable of sustaining prevention beyond a single election cycle. Future violence research should bridge public health, criminal justice, political science, jurisprudence, and administrative practices to identify who can act, under what conditions, and with what governing tools so violence problems can be resolved as they emerge.

Emotion detection in social media remains challenging, particularly in polarized public debates where emotional expression is often imbalanced across categories. Addressing this challenge, this study compares the performance of multiple computational approaches using a gold-standard dataset of tweets about an ongoing geopolitical conflict. The dataset reflects the authentic, skewed distribution of emotions observed in real-world online discourse. We evaluated lexicon-based methods, classical machine-learning classifiers, deep-learning architectures, transformer models in both fine-tuned and zero-shot configurations, and a zero-shot large language model to assess their effectiveness in capturing both frequent and less frequently expressed emotions. Across approaches, transformer models, especially those fine-tuned for contextual emotion recognition, demonstrated the strongest overall performance, with emotion-specific fine-tuning offering a particular advantage for detecting rare emotion categories. These findings emphasize the importance of evaluating emotion detection methods under realistic class imbalance and highlight both the comparative strengths and limitations of widely used modeling strategies in applied social media research. This study advances emotion analysis and computational social science by offering practical guidance for selecting appropriate emotion detection methods in complex, imbalanced social media contexts.