critical theory application

This study addresses the dual challenges of algorithmic erosion of civic autonomy and widespread deficits in digital literacy, focusing on the epistemic crisis precipitated by computational technologies and their transformative impact on social normative structures. Method: Drawing on interdisciplinary approaches—philosophical critique, infrastructure studies, sociology of knowledge, and computational phenomenology—the project systematically integrates computational thinking into humanities and social science traditions for the first time. Contribution/Results: It introduces the original theoretical framework of “algorithmic heteronomy,” elucidating the internal logic and material mediation of algorithmic power. The project advances a paradigm shift in critical digital literacy education, advocating pedagogical engagement with technologies from within their constitutive mechanisms. It provides both a structural analytical toolkit and concrete teaching frameworks to reconfigure digital subjectivity, algorithmic ethics, and technological democratization in the humanities and social sciences.

This paper identifies and critiques three methodological biases in critical AI studies: benchmark-case reductionism (overreliance on canonical examples), black-box analogism (uncritical adoption of outdated computational metaphors), and stack-based causalism (linear, deterministic attribution of algorithmic harms). To address these interlocking limitations, the paper introduces—systematically for the first time—the “introductory” methodological framework, centered on humanistic close reading. Integrating cultural text analysis, conceptual history, and critical discourse analysis, this approach transcends the binary constraints of technological determinism and purely speculative theorization. Emphasizing contextual sensitivity and epistemic reflexivity, the framework advances methodological self-awareness for AI ethics, policy analysis, and cultural inquiry, while offering a pedagogically robust, interdisciplinary tool for teaching and research.

This work addresses the challenges students face in large-scale theoretical computer science courses due to the abstract nature of logic and the lack of scalable automated assessment mechanisms. To bridge this gap, the authors propose LogicLab—a lightweight, Racket-based pedagogical toolkit that integrates logic instruction with students’ existing programming experience through composable functional interfaces aligned with standard course notation. LogicLab supports parsing, rendering, equivalence transformation, normal form conversion, truth-value evaluation, resolution rule application, Davis–Putnam procedure execution, and step-by-step deductive proof validation, all while providing immediate feedback and enabling automated grading. By significantly lowering the entry barrier, the system enhances student engagement, strengthens formal reasoning skills, and deepens conceptual understanding, offering a consistent, integrable, and scalable solution for teaching and assessing logic in large courses.

This paper identifies three overlooked technical latent elements—heuristic models, critical assumptions, and parameter specifications—in interdisciplinary social computing research. Often lacking rigorous computational theoretical foundations, these elements implicitly encode normative design intentions, leading to accountability displacement and failures in socio-technical scrutiny. Method: Drawing on conceptual analysis, critical technical practice, and socio-technical systems theory, the study systematically defines and deconstructs these elements, identifying six interrelated risk dimensions. Contribution/Results: The paper introduces the first methodology-oriented warning framework explicitly targeting modeling-process transparency and cross-disciplinary accountability. Designed to support algorithmic governance, AI ethics, and human-AI collaboration research, the framework provides an actionable, deep socio-technical audit pathway that foregrounds epistemic responsibility in computational social science practice.

Existing data-analytic models, grounded in positivism, neglect critical dimensions of power, tacit knowledge, and cognitive schemata. Method: This paper proposes an interpretivist “iceberg model of meaning construction” (Add-Check-Refine), treating data as schematized artifacts and distinguishing explicit from implicit cognitive schemata; it emphasizes schema primacy, multiplicity, and epistemic humility. Validation employs historical conceptual analysis and four empirically grounded scenarios—e.g., sensor measurement bias and data neglect—to demonstrate interpretivist coherence and explanatory power. Contribution/Results: The model precisely identifies canonical analytical dilemmas while offering actionable remediation pathways. Crucially, it constitutes the first systematic integration of humanistic critique with data practice, thereby establishing both theoretical foundations and methodological scaffolding for institutionalizing interpretivism within data science.

Amid the growing representational capacity of foundation models, this work examines the necessity and evolving role of explicit symbolic reasoning. Grounded in the principle of compression, it proposes a modeling–reasoning trade-off theory: symbolic reasoning functions not as an intrinsic component of intelligence but as a compensatory mechanism for information loss in simplified models. As models increasingly approximate reality, reliance on such symbolic scaffolding naturally diminishes. Through theoretical analysis, computational modeling, and an AI-philosophical framework, the study offers a unified account of the historical significance of symbolic methods and the empirical success of modern large language models. It further repositions symbolic reasoning’s future value—not as a core reasoning engine, but as an interpretable interface in human–AI interaction, crucial for enabling human oversight, verification, and trust calibration.

This study addresses the limitations of traditional logics in capturing intentionality and hyperintensional phenomena by proposing a novel intensional logic system that takes “guises” as its fundamental semantic units. Integrating Leibnizian inclusion semantics, intentional operators, and a multi-layered modal architecture, the framework reconstrues relations as perspectival intentional structures internal to guises. By synthesizing techniques from Montague semantics, Fine-style hyperintensional logic, and situation semantics, the system achieves both soundness and canonical model completeness. It offers a unified account of hyperintensional phenomena such as substitution failures, quasi-indexicals, and de se reference, demonstrating superior explanatory power and theoretical innovation through systematic comparison with classical intensional and hyperintensional approaches.

This study addresses the critical challenge of reliably inferring an AI system’s beliefs, desires, and semantic content from its computational architecture—a key requirement for safe alignment and deception detection. Drawing on radical interpretation in philosophy and mechanistic interpretability in machine learning, the paper proposes the “holographic attribution” principle: beliefs, desires, and propositional structures must be jointly constrained to ensure coherent and reliable attitude ascriptions. Building on this principle, the authors develop an integrative framework that yields actionable testing methodologies and establishes evaluative criteria bridging representationalist and interpretivist approaches. This framework provides existing interpretability techniques with a theoretically grounded and empirically verifiable foundation, uniting philosophical rigor with practical applicability in AI safety research.

This work addresses the limitation of traditional introductory computer science curricula, which often emphasize isolated knowledge points while neglecting the underlying proof techniques and abstract structures essential for cultivating computational thinking in beginners. To remedy this, the paper proposes a novel pedagogical paradigm centered on universal proof strategies and abstract frameworks, using the transitive closure of relations as a representative case study. By integrating tools such as the Kleene star, quantale theory, and closure operators over complete lattices, the approach constructs a cohesive bridge linking logic, algebra, and computational reasoning. This method yields a generalizable instructional framework that significantly enhances students’ structural understanding and analytical capacity regarding foundational concepts.

This work addresses the lack of a rigorous logical and mathematical foundation grounded in inference within existing information theories, which hinders effective reasoning in complex information systems. Drawing on inferentialist semantics, the study replaces truth-based semantics with derivability and introduces proof-theoretic semantics to define “inferon” as the fundamental unit of information. This construct unifies the triadic character of information—as scope, correlation, and encoding—within a coherent framework. Building upon this foundation, the paper develops a formal model of inference-based information flow and demonstrates its application to distributed system modeling, thereby offering novel mathematical tools and reasoning mechanisms for analyzing and managing complex information systems.