On Difference, Social Generation, and the Void of the Universal ‘Legislator’
Difference seems closely related to generativity.
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Economic relations, generative capacity, and the formation of value; a developing chapter series.
Conversions between forms of value, procedural ordering, and their implications for justice.
Social contracts, asymmetric generative capacity, and the conditions of effective alternatives.
Clarifying dialogue and theory of mind, problem formation, and the education of judgment.
Intervention, evidence-to-action, and distributed possibilities under critical-transition uncertainty.
Updated:
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Difference seems closely related to generativity.
Traditional Western metaphysics begins with a fundamental question: "What is being?" and habitually answers with substance.
The gendered tendency toward relational versus competitive orientations may be understood through the psychoanalytic structure of subject formation.
Anthropological research reveals a thought-provoking possibility: human society may have originated from a matriarchal structure in which women once held higher status than men.
Contemporary society is undergoing a profound crisis of trust. This crisis does not merely manifest as a localized decay in the moral domain, but penetrates comprehensively across all dimensions of interpersonal relations, gender relations, state relations, and class relations.
It appears that we require a universal language capable of articulating both the structural outcomes of various generative systems and the processes by which such structures are produced. Among existing paradigms, the framework of dynamical systems is arguably one of the most powerful.
Wanhong Huang, Group Field Theory of Universal Grammar Symmetry Breaking and Language System Emergence, 2025, 10.17605/OSF.IO/N8Y9B
In physics, a symmetry transformation refers to an operation that does not alter the properties or fundamental laws of a system [1].
Parallel MRI reconstruction relies heavily on knowing the sensitivity of each coil. ESPIRiT offers a data-driven way to learn these sensitivities, directly from acquired k-space data.