AI Applied to Human Sciences

The integration of artificial intelligence into the humanities disciplines like history, philosophy, literature, and art criticism. AI tools can now reconstruct damaged historical texts, analyze stylistic patterns across a corpus of literature to identify influences, or generate philosophical arguments for critique. It's both a blessing and a crisis for the humanities: a powerful new method of inquiry that also challenges the very definition of human creativity and interpretation.

Example: "The Shakespeare scholar used AI to prove the authorship question once and for all—a perfect example of AI applied to human sciences, and the English department hasn't forgiven him for it."

by Dumu The Void March 11, 2026

AI Applied to Cognitive Sciences

The use of artificial intelligence as a tool to model, test, and understand the human mind. By building computational models that perform cognitive tasks—recognizing faces, making decisions, learning languages—researchers can create and test theories about how our own cognition might work. If an AI model behaves like a human under certain conditions, it might suggest that the human brain is using a similar computational strategy. It's cognitive science's most powerful laboratory.

Example: "They weren't sure how children learn grammar until they used AI applied to cognitive sciences to build a model that learned the same way, confirming their hypothesis."

by Dumu The Void March 11, 2026

AI Applied to Data Science

The use of artificial intelligence to automate and enhance the practice of data science itself. This includes using AI to automatically clean messy datasets, generate features, select the right models, tune hyperparameters, and even write the code for analysis. It's the field where AI becomes the data scientist's assistant, speeding up routine tasks and uncovering patterns that might take humans weeks to find. It's data science turning its tools back on itself.

AI Applied to Data Science Example: "He used to spend 80% of his time cleaning data; now with AI applied to data science, the machine does it for him, and he just focuses on asking the right questions."

by Dumu The Void March 11, 2026

Anthropology of Science

The study of scientific communities using the tools and perspectives of cultural anthropology. It treats scientists not as pure reasoners but as members of a distinct culture with its own rituals, taboos, initiation rites, kinship systems, and oral traditions. The anthropologist of science might study how lab meetings function as tribal councils, how citation practices serve as gift exchange systems, how conference presentations operate as prestige competitions, and how "revolutionary" discoveries are actually negotiated through complex social processes. It reveals that the white coat is a cultural costume, the lab is a ritual space, and peer review is a sophisticated form of tribal gatekeeping. This approach doesn't deny that science produces truth—it just shows that truth-production is always also culture-production.

Example: "Her anthropology of science dissertation examined how theoretical physicists use hand gestures and whiteboard drawings as a form of ritual communication—a tribal language unintelligible to outsiders but sacred to initiates."

by Abzugal March 11, 2026

Anthropology of the Scientific Method

A focused subfield examining how "the scientific method" itself varies across cultures, disciplines, and historical periods as a set of cultural practices. It asks not "what is the scientific method?" but "how do different groups of scientists perform what they call the scientific method?" The controlled experiment is a ritual in some fields, while in others, fieldwork is the sacred practice. The anthropology of the scientific method reveals that what counts as "doing good science" is learned through apprenticeship, enforced by community norms, and subject to the same cultural variation as any other human practice—even as scientists themselves believe they're following a universal, timeless procedure.

Example: "The anthropology of the scientific method shows that 'reproducibility' means completely different things in particle physics versus ecology—same words, different cultural practices."

by Abzugal March 11, 2026

Ecology of Science

A metascientific framework that studies science as an ecological system—a complex, interdependent network of organisms (scientists), populations (disciplines), communities (fields), and environments (institutions, funding landscapes, social contexts). The ecology of science examines how scientific niches emerge and evolve, how resources (funding, attention, prestige) flow through the system, how competition and cooperation shape research agendas, how species (theories, methods, paradigms) adapt or go extinct, and how disturbances (discoveries, scandals, funding shifts) ripple through the ecosystem. It reveals that scientific change is not just rational progress but ecological succession—driven by interactions between organisms and their environments, by adaptation and selection, by the same dynamics that shape any living system. The ecology of science treats laboratories as habitats, journals as ecosystems, and scientific communities as biomes, each with its own internal dynamics and relationships to the larger environment.

Example: "Her ecology of science analysis showed how the rise of molecular biology created a new niche that drew resources away from traditional organismal biology—not because molecular biology was better, but because it occupied a new ecological space that flourished in the changing funding environment."

by Dumu The Void March 16, 2026

Complex Dynamic Systems of Science

A metascientific framework that studies science as a complex adaptive system—characterized by nonlinear dynamics, feedback loops, emergent behavior, self-organization, and sensitivity to initial conditions. This approach uses tools from complexity science to model how scientific knowledge evolves, how paradigms shift, how consensus forms and breaks, how innovation cascades through research networks, and how small perturbations (a single paper, a single discovery) can trigger phase transitions that transform entire fields. It reveals that science is not a linear accumulation of knowledge but a dynamical system with its own attractors, bifurcations, and critical thresholds—sometimes stable, sometimes chaotic, sometimes poised at tipping points where anything can happen. Understanding science requires understanding these dynamics: how ideas compete for survival, how communities self-organize, how the system as a whole behaves in ways that cannot be predicted from studying individual scientists alone.

Complex Dynamic Systems of Science Example: "His complex dynamic systems model showed how a single retraction could trigger a cascade of replications, further retractions, and eventually a paradigm shift—not because the original finding was important, but because the system was poised at a critical threshold where small perturbations have massive effects."

by Dumu The Void March 16, 2026