Domestication, foreignization, and the cultural politics of translation. Why translation is never neutral. Venuti's discussion of domestication and foreignization changed how I think about what is preserved, and what is erased, when meaning moves between cultures.
Selected bibliography
Reading Nook.
Theory, research, and technical foundations informing the work.
About
Books and papers that shaped how I think.
No reading list is complete, and this one isn't intended to be. These are simply works that have influenced how I approach translation, narrative, computation, law, and artificial intelligence. Some I return to often. Others introduced a question that never really left. If something here interests you, I encourage you to find a copy.
Translation studies
Translation as cultural action rather than simple linguistic conversion. A reminder that translation is a cultural act before it is a technical one.
Norms and the cultural systems that shape translated texts.
Narratology
Voice, focalization, time, narrative distance, and the architecture of storytelling. I return to Genette whenever I need language for discussing narrative structure.
A detailed framework for thinking about narrative time, space, perspective, and character.
Narrative as a cognitive and cultural sense-making form. Shows how stories help people organize and understand experience.
An exploration of immersion, presence, and how readers experience fictional worlds.
Digital humanities
Interpretive computation and speculative digital practice. Helped me see computation as an interpretive practice rather than simply a technical one.
One of the foundational texts for algorithmic criticism and computational reading.
Computer-assisted interpretation in the humanities. Explores how computers can support interpretation without replacing it.
How algorithmic systems organize and shape cultural production. A useful reminder that algorithms do not merely organize culture, they shape it.
Technical foundations
Natural-language processing from linguistic foundations through modern methods.
Neural networks, representation learning, and optimization.
A probabilistic and statistical foundation for machine learning.
Practical language analysis using Python and NLTK; a bridge between computational linguistics and implementation.
Power, ethics, and culture
Scale, opacity, bias, and the social consequences of predictive systems. A reminder that algorithms always operate inside social systems.
The limits of technological solutionism. An argument against technological solutionism and for human judgment.
The material, political, and cultural infrastructure behind AI. Places artificial intelligence within its material, political, and ecological context.
Colonialism, power, relational ethics, and structural bias in AI systems. Insightful work on algorithmic injustice, colonialism, and the ethics of AI.
Editorial note
This list changes from time to time. As the research evolves.