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Author

Josef Van Genabith

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Open access Sep 2026

Exploring Agency in Chinese Environmental Thought through Human–Machine Co-Creation

In an age marked by global crises, a transcultural understanding of the philosophical foun­dations of human thought—especially in epistemology and ethics—has become essential. Contemporary ecological crises unfold on a planetary scale, exceeding both the conceptual limits of nation-states and the historically constituted horizons of dominant Western para­digms. Addressing them requires normative frameworks grounded in global interdepend­ence rather than any single civilizational matrix, emerging through sustained transcultural dialogue that integrates diverse philosophical traditions into a shared ecological perspec­tive. As a major geopolitical actor, China demands serious philosophical engagement in this context. Yet traditional research on Chinese ecological thought has relied on close reading, which limits conceptual analysis. While hermeneutic methods remain indispensable for in­terpreting individual texts, they cannot systematically identify recurring conceptual patterns across extensive corpora spanning diverse discourses and long, diachronically differentiat­ed periods. The result is a fragmentary interpretation focused on isolated authors, concepts, or epochs rather than reconstructed continuities and ruptures. This paper proposes a new model based on human–machine collaboration, employing self-supervised embeddings and large language models to generate empirical insights that complement qualitative analysis. By integrating theoretical and AI-driven approaches, it expands reflective capacities and establishes a framework for evaluating AI-generated philosophical insights, contributing to more robust transcultural ecological frameworks.

Jana S. Rošker, R. Pozzo, Marko Robnik Šikonja et al. · 0 citations

When Tokenization is Secretly Output Supervision

It is argued that framing tokenization as output supervision provides a principled account of why tokenization consistently affects model performance, and that differences in task performance, training dynamics, and model internals are induced by output tokenization and largely invariant to input tokenization.

Tanja Baeumel, Josef van Genabith, Simon Ostermann · 0 citations

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