Harry G. West

We used a novel methodology for analyzing peace representations in global media by integrating a Retrieval-Augmented Generation (RAG) model with refined concepts of Positive and Negative Intergroup Reciprocity (PIR/NIR). Traditional analyses often overlook nuanced intergroup dynamics. By refining PIR and NIR definitions, we provide a precise framework to assess media portrayals of intergroup relations influencing peace. Using the News On the Web dataset of 700,000 articles from 18 countries, we employed machine learning to embed articles and identify PIR and NIR instances. This allowed us to classify countries based on their media’s alignment with these concepts. Our findings reveal significant variations, with some media emphasizing intergroup tolerance and others highlighting aggression. Comparing our PIR/NIR analysis with traditional peace indices, we demonstrate that our approach offers deeper insights into factors influencing national peace. This has significant implications for peace building, high lighting the critical role of media in shaping peace.

We used a machine learning approach to classify countries as peaceful or non-peaceful by analyzing linguistic patterns in global media articles. Utilizing vector embeddings and cosine similarity, we develop a supervised classification model that achieves 94% accuracy in distinguishing peaceful contexts. We also examine the impact of dataset size on model performance, finding that while larger datasets improve accuracy, smaller datasets still provide valuable insights. Our calculated peace percentages correlate strongly with the Human Development Index (HDI), validating our methodology. This study highlights the potential of advanced AI techniques in peace studies and underscores the importance of data quality and representativeness. Future work includes developing real-time monitoring tools and addressing training data biases to enhance model accuracy.

We used data from LexisNexis to determine the words in news media that were most important in classifying countries as higher or lower peace using logistic regression, support vector machines, decision trees, and random forest as classifiers. We then embedded those words using the multilingual-e5-large-instruct model, obtaining high-dimensional representations of each word’s semantic meaning. PCA allowed us to transform the word embeddings into a two-dimensional space, facilitating visual interpretation of the relationships and clusters among the words. With the data transformed into a 2D space, we applied K-means clustering to identify distinct groups or clusters of words that shared similar characteristics. We found that higher peace news is characterized by themes of finance, daily activities, and health and that lower peace news is characterized by themes of politics, government, and legal issues. This work provides a starting point to measure levels of peace and identify the social processes that underly those words.

Treatments of the relationship between war and food have often emphasized the effects of food upon war, which may certainly be profound. Such perspectives, however, often over-simplify the dynamic between war and food, suggesting for example that food is a factor in war that can and should be managed by wartime leaders to desired effects. Drawing on Tolstoy’s view of war as the sum of a multitude of infinitesimal units of activity, I suggest the relationship between war and food is much more complex. Actors in wartime are driven by myriad motives, and waging war is not their only end. In such contexts, food itself may be the focus of some actions, and in any case, food may be as profoundly shaped by war as war is shaped by food. This article considers the complex relationship between food and war through a comparative historical examination on one foodstuff, namely cheese.

We used data from LexisNexis to determine the words in news media that best classifies countries as higher or lower peace. We found that higher peace news is characterized by themes of finance, daily actitivities, and health and that lower peace news is characterized by themes of politics, government, and legal issues. This work provides a starting point to measure levels of peace and identify the social processes that underly those words.

Green and sustainable food procurement has benefits for human health, the environment and economies. Public sector actors have purchasing power behind procurement decisions, and there is significant support for sustainably sourced food from consumers and the third sector. A sustainability transition in the public procurement of food would appear to be achievable, yet change remains incremental. This paper analyses supply chain stakeholder narratives about pathways to more localised public food procurement. Based on forty interviews with actors in the procurement supply chain in the South West of England, we examine the barriers and opportunities for more localised food supply and sourcing. Our findings indicate that if public food procurement is to become a viable, feasible and desirable market channel for operators of regional food businesses, we need to give greater attention to supply chain stakeholders' experiences of the interface between procurers and suppliers. Tensions exist between stakeholders’ shared need for efficiency and logistical convenience, and their mutual desire for closer procurer-supplier relations and aspirations for a regional economic community. Results demonstrate that alongside the need for new physical and digital infrastructure, there is an urgent need to address socio-cultural barriers to change.