How efforts to repair violence have created new forms of inequality in Colombia’s post-conflict countryside What happens when efforts to repair the harms of war fall short of transforming the inequalities that caused the conflict? In Landscapes of Repair, Max Counter addresses that question by focusing on the process of returning land to rural Colombians who lost it due to armed conflict. Examining the current land restitution program in Colombia’s Magdalena Medio region, this book offers a new perspective on the effects of repair in the aftermath of violence. Drawing on extensive ethnographic fieldwork and analysis of judicial records, Counter studies the struggles among displaced conflict victims, current landowners, and rural elites to demonstrate how reparations can simultaneously repair past harms and create new inequities. This book reveals how restitution, even when it fails to radically reorient unequal agrarian economies, nonetheless reshapes geographies of land dispossession, access to territory, and whose memories of conflict count as valid. At a time when governments and international organizations increasingly rely on transitional justice to address mass violence, Landscapes of Repair provides vital insights into the relationships among land, justice, memory, and peace, with implications far beyond Colombia.
PurposeThis study explores how initial exposure to immersive spatial computing experiences using AR headsets generates lasting inspiration and shapes consumers expected long-term life consequences (i.e., enhancement of reality, perceived substitutability and social impact).Design/methodology/approachThe study uses a time-lagged research design based on 148 first-time users of spatial computing devices (AR headsets). Respondents were interviewed once shortly after being exposed to AR and a few days later. Data is analyzed using partial least squares structural equation modeling (PLS-SEM).FindingsUsers' immediate "inspired-by" experiences predict increased "inspired-to" intentions days later. Such inspiration translates into anticipated consequences such as virtually customizing their physical environments, substituting physical products with AR content, and influencing social relationships with other users.Research limitations/implicationsThe current research focuses on positive life outcomes for consumers. However, the ubiquitous and pervasive use of AR may also lead to negative, undesired effects.Practical implicationsThe study demonstrates that AR experiences can produce detectable effects long after initial exposure and underscores that AR adoption results from the synergy of hardware and content, providing insights for future research in immersive spatial computing technologies.Originality/valueThe current research is one of the first to study AR users over time. Drawing on inspiration theory, findings show that an initial exposure to spatial computing through AR can have lasting effects when consumers think about how these technologies could impact their lives.
Since the start of the U.S. Conservation Reserve Program (CRP) in 1985, producers have enrolled environmentally sensitive land in exchange for annual rental payments, supporting multiple dimensions of sustainability through reduced soil loss, improved water quality, enhanced habitat provision, and strengthened climate resilience through land stewardship. Recent declines in enrollment raise concerns about whether participation remains spatially aligned with local environmental need and economic incentives. This study examines regional variation in CRP participation and its sustainability implications by identifying spatial patterns in participation and key drivers using exploratory spatial data analysis (ESDA). We analyze county-level CRP participation rates alongside three key drivers (CRP rental rates, soil erosion risk on cultivated cropland, and farm income) and assess spatial dependence using Global Moran's I, univariate Local Indicators of Spatial Association (LISA), and bivariate LISA (BiLISA). Framed as an assessment of agri-environmental policy effectiveness for sustainable land management, the framework is applied to counties in the U.S. Midwest, a region with historically substantial CRP enrollment. Global Moran's I statistics indicate significant positive spatial autocorrelation for CRP participation (I = 0.491), CRP rental rates (I = 0.892), and soil erosion (I = 0.503), confirming pronounced regional clustering across Midwestern counties. LISA results further show that more than 60% of counties fall into high-high (HH) or low-low (LL) clusters for CRP rental rates, while BiLISA results indicate that 22.9% of counties form HH clusters between CRP participation and soil erosion, suggesting only partial alignment between CRP participation and the environmental need. These findings indicate that the environmental benefits of CRP may vary across the region depending on where participation occurs. Overall, the findings support a shift toward a data-driven, spatially explicit CRP strategy that integrates environmental risk, economic incentives, and regional context to strengthen sustainability outcomes and enhance environmental effectiveness, economic efficiency, and the spatial equity of conservation benefits in the United States.
This article investigates the widespread practice of using exercise as punishment in educational athletics, emphasizing its negative impact on the psychological and physical well-being of young athletes. Through a review of current research, it demonstrates how punitive exercise fosters fear, anxiety, and diminished intrinsic motivation, ultimately reducing athletes' enjoyment and engagement in sports and physical activity. This approach sends a damaging message that failure leads to punitive consequences, which can exacerbate performance anxiety, feelings of shame, and low self-esteem. Additionally, the article highlights that using exercise as punishment fails to address the root causes of undesirable behaviors, such as skill deficits and external stressors. Instead of promoting accountability, punitive measures can lead to disengagement and decreased motivation, further perpetuating a cycle of negative behavior. This practice also creates harmful associations with physical activity, potentially leading to a long-term aversion to exercise. In contrast, the article advocates for alternative strategies that foster a positive culture within athletic environments. By reframing exercise as a beneficial and enjoyable activity, coaches (and educators) can enhance athletes' motivation, promote teamwork, and build resilience. The shift from punishment to empowerment encourages student-athletes to appreciate running and other forms of exercise as an integral part of their training, leading to improved performance and a lifelong commitment to fitness and well-being. Ultimately, this article calls for a transformative change in educational athletics practices to prioritize athlete development and mental health.
Code smell detection is vital for ensuring software quality, but the imbalance between smelly and non-smelly code instances impairs detection, especially for minority smells like Data Class and Feature Envy. Existing oversampling techniques, such as Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE (BL-SMOTE), and Adaptive Synthetic (ADASYN), attempt to mitigate this issue but often introduce noise or semantically irrelevant samples. This study proposes DIR-SMOTE (Density and Influence-based Resampling using SMOTE), a density and explanation-guided resampling framework that integrates local density estimation and SHapley Additive exPlanations (SHAP)-based feature importance to improve the quality of synthetic minority samples. Initially, DIR-SMOTE filters out noisy or isolated minority instances using density metrics. It then employs SHAP to identify the most influential features per instance. Synthetic samples are generated by interpolating between dense neighbors while perturbing only top-ranked SHAP features, thereby preserving semantic integrity. DIR-SMOTE is evaluated on five benchmark datasets, namely, Apache, jEdit, EDTForCSD, DesigniteJava, and MLCQ, across multiple smells such as Long Method, Feature Envy, and Data Class. Compared to nine standard resampling methods, DIR-SMOTE achieves up to 6.7
