Predatory Value: Economies of Dispossession and Disturbed Relationalities

This essay introduces and theorizes the central concerns of this special issue, “Economies of Dispossession: Indigeneity, Race, Capitalism.” Financialization, debt, and the accelerated concentration of wealth today work through social relations already configured and disposed by imperial conquest and racial capitalism. In the Americas broadly and the United States specifically, colonization and transatlantic slavery set in motion the dynamics and differential racialized valuations that continue to underwrite particular forms of subjection, property, commerce, and territoriality. The conception of economies of dispossession introduced in this essay draws attention to the overriding importance of rationalities of abstraction and commensurability for racial capitalism. The essay problematizes the ways in which dispossession is conventionally treated as a self-evident and circumscribed practice of unjust taking and subtractive action. Instead, working across the lethal confluences of imperial conquest and racial capitalist predation, this essay critically situates the logic of propriation that organizes and underwrites predatory value in the historical present. Against the commensurabilities and rationalities of debt and finance capitalism, conditioned through the proprietary logics of settler colonialism and racial capitalism, the essay gestures toward alternative frameworks for building collective capacities for what the authors describe as a grounded relationality

Complex Logical Reasoning over Knowledge Graphs using Large Language Models

Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and abstract logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area.Comment: Code available at https://github.com/Akirato/LLM-KG-Reasonin

Local/Global Conference Stages Conversation about Queer Future

On April 23rd and 24th, CLAGS hosted Local Politics and Global Change: Academics and Activists Thinking About a Queer Future. The conference employed an innovative structure within which panelists, rather than delivering papers on their individual skill area or academic interest, were asked to respond from their located standpoint to prepared questions. These questions elaborated upon the broad topic of each panel and roundtable, which also included extended Q&A periods that encouraged conversation between audience, moderator, and panelists. To describe the format seems noteworthy because it contributed in part to one of the most outstanding features of this conference: There were no tired rehearsals of bridging the gap between a monolithic academy and the all-too-reified street