Structural realism and generative linguistics

Nefdt, Ryan M.

doi:10.1007/s11229-020-02952-1

Synthese

2021

DOI: 10.1007/s11229-020-02952-1

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Structural realism and generative linguistics

Ryan M. Nefdt

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Cited by 3 publications

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“…Nefdt (2021), for instance, argues for an ontic structural realist position in linguistics in which the chief vehicle of the scientific continuity is structure.37 The corresponding automaton would be a pushdown automaton which incorporates an element of memory or the 'stack'.…”

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confidence: 99%

Biolinguistics and biological systems: a complex systems analysis of language

Nefdt

2023

Biol Philos

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In their recent book, Ladyman and Wiesner (What is a complex system?, Yale University Press, 2020) delineate the bounds of the exciting interdisciplinary field of complexity science. In this work, they provide examples of generally accepted complex systems and common features which these possess to varying degrees. In this paper, I plan to extend their list to include the formal study of natural language, i.e. linguistics. In fact, I will argue that language exhibits many of the hallmarks of a complex system, specifically a complex biological system. Thus, my aim is to advocate contra the the ‘Minimalist Program’ (Chomsky, The minimalist program, MIT Press, Cambridge, 1995), which motivates simple underlying mechanisms (i.e. Merge) in their idealisations, that biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole.

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Biolinguistics and biological systems: a complex systems analysis of language

Nefdt

2023

Biol Philos

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Theoretical Linguistics and the philosophy of linguistics

Nefdt

2024

Theoretical Linguistics

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Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

Halkiopoulos,

Gkintoni

2024

Electronics

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This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.

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Structural realism and generative linguistics

Cited by 3 publications

References 54 publications

Biolinguistics and biological systems: a complex systems analysis of language

Biolinguistics and biological systems: a complex systems analysis of language

Theoretical Linguistics and the philosophy of linguistics

Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

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