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"Power (Social sciences) Maps."
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The atlas of global inequalities
\"Drawing on research from around the world, this atlas gives shape and meaning to statistics, making it an indispensable resource for understanding global inequalities and an inspiration for social and political action. Inequality underlies many of the challenges facing the world today, and The Atlas of Global Inequalities considers the issue in all its dimensions. Organized in thematic parts, it maps not only the global distribution of income and wealth, but also inequalities in social and political rights and freedoms. It describes how inadequate health services, unsafe water, and barriers to education hinder people's ability to live their lives to the full; assesses poor transport, energy, and digital communication infrastructures and their effect on economic development; and highlights the dangers of unclean and unhealthy indoor and outdoor environments. Through world, regional, and country maps, and innovative and intriguing graphics, the authors unravel the complexity of inequality, revealing differences between countries as well as illustrating inequalities within them. Topics include: the discrimination suffered by children with a disability; the impact of inefficient and dangerous household fuels on the daily lives and long-term health of those who rely on them; the unequal opportunities available to women; and the reasons for families' descent into, and reemergence from, poverty.\"--Publisher description.
Book
Artificial intelligence may affect diversity: architecture and cultural context reflected through ChatGPT, Midjourney, and Google Maps
This study aims to understand how widely used Artificial Intelligence (AI) tools reflect the cultural context through the built environment. This research explores how outputs obtained with ChatGPT-4o, Midjourney’s bot on Discord and Google Maps represent the cultural context of Stockholm, Sweden. Cultural context is important because it shapes people’s identity, behaviour, and power dynamics. AI-generated recommendations and images of Stockholm’s cultural context were compared with real photographs, GIS demographic data and socio-economic information about the city. Results show how outputs written with ChatGPT-4o mostly listed museums and other venues popular among visitors, while Midjourney’s bot mostly represented cafes, streets, and furniture, reflecting a cultural context heavily shaped by buildings, consumption and commercial interests. Google Maps shows commercial sites while also enabling users to directly add information about places, like opinions, photographs and the main features of a business. These AI perspectives on cultural context can broaden the understanding of the urban environment and facilitate a deeper insight into the prevailing ideas behind the data that train these algorithms. Results suggest that the generative AI systems analysed convey a narrow view of the cultural context, prioritising buildings and a sense of cultural context that is curated, exhibited and commercialised. Generative AI tools could jeopardise cultural diversity by prioritising some ideas and places as “cultural”, exacerbating power relationships and even aggravating segregation. Consequently, public institutions should promote further discussion and research on AI tools, and help users combine AI tools with other forms of knowledge. The providers of AI systems should ensure more inclusivity in AI training data, facilitate users’ writing of prompts and disclose the limitations of their data sources. Despite the current potential reduction of diversity of the cultural context, AI providers have a unique opportunity to produce more nuanced outputs, which promote more societal diversity and equality.
Journal Article
Cultural Intolerance, in Practice: Social Variation in Food and Drink Avoidances in Italy, 2003–2016
Sociological literature on cultural practices seeking to understand the social differentiation of taste pays limited attention to what people avoid consuming, despite its potential as a strategic indicator of taste. Avoidance has special relevance for the understanding of eating and drinking practices which are often characterized by exclusion of items for health, hedonic, reputational, or spiritual reasons. Making use of rich data on twenty-three items commonly consumed by Italian adults, this paper investigates how avoidances—i.e. what people claim never to eat or drink—are clustered, socially patterned and have evolved over time. Methodologically, we propose the novel use and integration of two machine learning techniques—Self-Organizing Maps (SOM) and Boosted Regression Trees (BRT)— to identify nine highly homogeneous avoidance clusters and examine the power of social variables in predicting the probability of individuals’ belonging to various clusters and to further characterize them. We conclude by discussing possible rationales behind avoidance.
Journal Article
Predicting Public Corruption with Neural Networks
We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.
Journal Article
Place Branding: The State of the Art
This article examines the relevance of place branding as a political phenomenon in international politics. After setting place branding in a historical and conceptual context, it maps out the connections between branding and international politics by looking at three examples. First, it examines the challenges facing the European Union to strengthen its image as a global player. Second, it analyzes the efforts of the United States to deal with its collapsing image in the aftermath of its failing \"war on terror\" and military intervention in Iraq. Third, it examines negative place branding by focusing on the Borat movie that upset Kazakhstan in 2006 and the cartoon crisis that erupted in Denmark in September 2005. This article also aims to situate the practice of place branding in a broader analytical context. It argues that place branding is part of a wider spectrum of postmodern power, where soft power and public diplomacy also have their place.
Journal Article
Making and unmaking masculinities in Cairo through sonic infrastructural violence
This article explores the Egyptian state’s production of desired manhood and destruction of unwanted masculinities in relation to home and displacement through audio-focused analysis and a focus on sonic infrastructures. While sonic infrastructures can be used as a form of political control and violence, my work in Egypt also shows how people, through sound and sonic resistance, navigate and shape sonic landscapes of insecurity, violence and liminality, as well as resisting displacement and claiming space. In Cairo, where political unrest over the past decade has produced new imaginaries and maps of belonging, men opposing the politics of the current regime have been expelled by the state from their own city; deprived of rights, safety, status and dignity. The institutions of state power employ sound as a political representation, and control, monitor, limit as well as threaten the population through the sonic. All of these sound systems operate at auditory, corporeal and sociocultural frequencies. There are countless examples of how materialised sonic experiences are consciously constructed and used by the autocratic military regime in Egypt to discipline and ‘produce’ its subjects, through for example forbidding particular music; monitoring its residents and thereby employing control by listening; using unbearable loud sounds during torture; or closing downtown bars, cafes and bookshops and thereby sonically controlling and limiting parts of the cityscape of Cairo. These sonic materialised experiences are connected to how gendered bodies are excluded, un/remade, produced, expressed and negotiated.
本文通过以音频为中心的分析和对声音基础设施的关注，探讨埃及政府如何在住宅与驱逐方面制造自己想要的男子气概，并破坏自己不想要的男子气概。虽然声音基础设施可以被用作一种政治控制和暴力的形式，但我在埃及的工作也揭示了人们如何通过声音和有声的抵抗利用和塑造不安全、暴力和阙限的声音环境，以及抵抗驱逐和主张空间权利。在开罗，过去十年的政治动荡产生了新的想象和归属地图，反对现政权的男人们被国家驱逐出自己的城市；并且被剥夺了权利、安全、地位和尊严。国家权力机构使用声音作为政治表述的手段，通过声音控制、监督、限制以及威胁人民。所有这些声音系统都以听觉、身体和社会文化频率运行。无数的例子表明，埃及的独裁军事政权如何有意识地构建和使用物质化的声音体验来驯化和“生产”其顺民（例如通过禁止特定的音乐）；监控其居民，从而通过监听来实施控制；使用令人难以忍受的高音作为酷刑；或者关闭市中心的酒吧、咖啡馆和书店，从而在声音方面控制和限制开罗的部分城市景观。这些声音物质化的体验与性别化的身体被排斥、取消、改造、表达和协商有关。
Journal Article
Risk Clusters, Hotspots, and Spatial Intelligence: Risk Terrain Modeling as an Algorithm for Police Resource Allocation Strategies
The study reported here follows the suggestion by Caplan et al. (Justice Q, 2010) that risk terrain modeling (RTM) be developed by doing more work to elaborate, operationalize, and test variables that would provide added value to its application in police operations. Building on the ideas presented by Caplan et al., we address three important issues related to RTM that sets it apart from current approaches to spatial crime analysis. First, we address the selection criteria used in determining which risk layers to include in risk terrain models. Second, we compare the \"best model\" risk terrain derived from our analysis to the traditional hotspot density mapping technique by considering both the statistical power and overall usefulness of each approach. Third, we test for \"risk clusters\" in risk terrain maps to determine how they can be used to target police resources in a way that improves upon the current practice of using density maps of past crime in determining future locations of crime occurrence. This paper concludes with an in depth exploration of how one might develop strategies for incorporating risk terrains into police decision-making. RTM can be developed to the point where it may be more readily adopted by police crime analysts and enable police to be more effectively proactive and identify areas with the greatest probability of becoming locations for crime in the future. The targeting of police interventions that emerges would be based on a sound understanding of geographic attributes and qualities of space that connect to crime outcomes and would not be the result of identifying individuals from specific groups or characteristics of people as likely candidates for crime, a tactic that has led police agencies to be accused of profiling. In addition, place-based interventions may offer a more efficient method of impacting crime than efforts focused on individuals.
Journal Article
Artificial intelligence as planetary assemblages of coloniality: The new power architecture driving a tiered global data economy
We present a framework for viewing artificial intelligence (AI) as planetary assemblages of coloniality that reproduce dependencies in how it co-constitutes and structures a tiered global data economy. We use assemblage thinking to map the coloniality of power to demonstrate how AI stratifies across knowledge, geographies, and bodies to influence development and economic trajectories, impact workers, reframe domestic industrial policies, and reconfigure the international political economy. Our post-colonial framework unpacks AI through its (1) global, (2) meso, and (3) local layers, and further dissects how these layers are vertically integrated, each with its horizontal dependencies. At (1) the global layer of international political economy maps a new digital bipolarity expressing Sino and American global digital corporations’ strategic and dominant positions in shaping a tiered global data economy. Then, at (2) the meso layer, we have a mosaic of domestic industrial policies that fund, frame markets, and develop AI talent across industries, sectors, and organizations to competitively integrate into AI value chains. Finally, incorporating into these are (3) the localized labor processes and tasks, where workers and users enact various AI-mediated tasks and practices driving further value extraction. We traced how AI is an interlaced system of power that reshapes knowledge, geographies, and bodies into dependencies that reinforce stratifications in developing underdevelopment. This commentary maps the current digital realities by laying out an uneven techno-geoeconomic power architecture driving a tiered global data economy and opening new research avenues to examine AI as planetary assemblages of coloniality.
Journal Article
Natural disasters, salience and public support for climate change policy
This paper examines whether or not public support for climate change mitigation policy can be affected by salient events such as natural disasters. We test this hypothesis using detailed, county-level data from the 2018 Yale Climate Opinion Maps, which documents both the degree to which residents of a county support climate change policy. We show that while natural disasters lead to statistically significant increases in both the share of a county’s population that support climate change mitigation policy and/or believe that climate change is happening, the magnitude of these estimated effects are economically small and perhaps not robust to hidden bias. As a result, and even assuming our results are in fact causal, the magnitude of our findings suggest that support as a policy objective by targeting agent’s beliefs about the risks climate change poses may ultimately be an ineffectual approach at achieving policymakers’ goals.
Journal Article
Asymmetric Power of the Core: Technological Cooperation and Technological Competition in the Transnational Innovation Networks of Big Pharma
This article theoretically and empirically analyzes leader corporations' innovation processes in contemporary capitalism. We highlight three characteristics: their transnational scope, the primacy of power or asymmetric relations exercised by leaders over the participants of their innovation circuits or networks, and the relevance of what we called technological competition and technological cooperation between leaders. Focusing on the latter, our theoretical contribution integrates the concepts of innovation circuit, global innovation network and modularity of knowledge production in order to elaborate a preliminary model for synthesizing leader's technological competition and collaboration behaviors. This model is the general framework used for studying three big pharma's innovation networks (Roche, Novartis and Pfizer). In particular, we study those networks by considering two outputs: scientific publications and patents. Network maps are constructed based on institutions' co-occurrences, thus looking at who is co-authoring their publications and co-owning these corporations' patents. We find that big pharmaceuticals co-produce together mainly generic knowledge modules, thus develop a strong technological cooperation. Simultaneously, to succeed in their technological competition they outsource stages of their innovation networks to subordinate institutions that, even if they contribute to achieve the innovation, will not be co-owners of the resulting patents, while big pharmaceuticals enjoy associated innovation rents.
Journal Article