Trusted, curated, subject-specific models are needed for academia
#predictingthefuture #newfutureofwork | Jaime Teevan
🌱 Prediction: Knowledge will outgrow publication. We’re already seeing academic publication start to buckle under AI, sometimes absurdly. I still publish research more or less the way Darwin did. I run a study, write it up, a few other scientists check it over, and the result gets filed away as a document with my name on the front. Faster than Darwin, with better figures, but the same basic shape. I predict that shape won’t last another decade. Academic authors are starting to slip hidden instructions into papers to flatter the AI that might review them. Reviewers are spending time checking whether citations exist or were hallucinated. Researchers asking AI to tell them about a paper instead of reading it directly. These are signs that the creation of new knowledge is outgrowing the articles that used to contain it. An academic paper serves many purposes at once. It makes an argument legible. It lets strangers check one's reasoning. It assigns credit and responsibility. It records who knew what and when. A paper was the only container we had for these different jobs, so it carried all of them together. With AI, they can be separated. My guess is that means the unit of publication will get smaller. Much of my research has focused on microproductivity, developing the idea that large accomplishments can be built from many small contributions. Publication will start to become a form of microproductivity. Instead of holding onto a result until it can be wrapped in a narrative large enough to justify a paper, researchers will publish it the moment it’s solid. Each finding, method, or negative result will be citable and carry its own provenance, so credit and reasoning travel with it. Reviewing will shrink to match, so claims get checked as they’re made instead of in one verdict at the end. But more than changing publication, the deeper change will be to how research itself is done. You may have heard the term “compound engineering,” where every bug fixed, evaluation written, workflow documented, or lesson learned becomes part of the system’s memory. I predict we’re about to see “compound science,” where every experiment, evaluation, insight, artifact, and learned capability becomes a reusable asset for future discovery. Findings will become evidence. Methods will become building blocks. Failed approaches will become constraints. For centuries, science has relied on humans to navigate an ever-growing body of knowledge. Soon that body of knowledge will help navigate itself. Scientists will spend less time searching for hypotheses and more time deciding which opportunities to pursue. AI systems will propose explanations, design experiments, run analyses, and explore many possibilities in parallel. Every discovery will become a part of the machinery that produces the next one. Papers ten years from now will look less like my current papers than my current papers look like Darwin’s. If they exist at all. #PredictingTheFuture #NewFutureOfWork
Scientific sleuths come in from the cold
Research integrity investigators are starting to organize, but the field, and the people, remain idiosyncratic
Metagov x Future of Science Seminar - Discourse Graphs with Matt Akamatsu
Governing the scholarly AI Commons – Open Future
New report explores how academic communities can shape AI governance in research and publishing, from regulation to community-led approaches.
Discourse Graphs and the Future of Science | Protocol Labs Research
Interview between Tom Kalil, Chief Innovation Officer of Schmidt Futures, Dr. Evan Miyazono, Research Team Lead at Protocol Labs, and Dr. Matt Akamatsu, Assistant Professor of Biology at the University of Washington.
NLnet; Nanoarguments
Scientific knowledge is currently scattered across papers, repositories, and disconnected platforms, with no structured way to trace how claims connect to evidence or how arguments develop. Nanoarguments builds a framework and tools for creating, browsing, and contributing to a global, federated graph of scientific discourse and evidence. Researchers and their communities can collaboratively structure claims, evidence chains, and discussion as nanopublications, which are small, cryptographically signed Linked Data snippets with precise provenance and authorship, published to a decentralized peer-to-peer network. The project builds upon the Nanodash interface to help users browse, edit, and aggregate discourse and evidence graphs, and integrates with dokieli to enable in-context authoring of nanopublications as inline annotations while reading or writing a document. A bidirectional ActivityPub connector bridges the nanopublication network and the fediverse, allowing discourse threads to start as social exchanges and crystallize into persistent, machine-readable evidence records. The project will be piloted with early adopter research groups in discourse and evidence modeling. All components will be released as open-source modules that other systems can build upon.
MCP Research & Discussion Night · Luma
We're hosting a reading and discussion group for Anthropic's Model Context Protocol. We'll look at the fundamental protocol affordances, its consequences, and…
A budding discipline around how science gets read and judged - Aris
Scholarly publishing spent thirty years arguing about access. It has barely begun arguing about what readers do with the paper once they have it. At Aris we call that second argument scholarly interface design. Mike Morrison's community calls it ScienceUX. Either way it is becoming a field, and here is why it is worth your attention.
A Science Funding System Beyond the Linear Model
Research funders should embrace a new ontology of scientific work to improve the relationship between science and the public.
The Paper Factory
How can large language models (LLMs) contribute to social science research, and what parts of research remain stubbornly human? Building on existing LLM tools, we offer a multi-agent workflow capable of producing a full quantitative social science paper from an initial prompt. The workflow relies on researchers codifying their heuristics for doing data analysis, and we suggest some core design principles for researchers interested in building on this scaffolding. Using this case, we also examine what current LLM capabilities reveal about the organization of research. LLM agents can lower the cost of pursuing high-risk ideas, expand robustness and transparency, reduce concerns about the scientific file drawer, and force scholars to articulate the heuristics that create valuable work. But they also pose challenges, both in terms of the quality of papers and in the adequacy of scientific institutions to adapt. Meeting these challenges will require new institutional norms that make use of these tools observable, auditable, and accountable.
The new way we’ll do science
Papers should become human-readable views over a graph of data, tools, results, and certificates.
The Journal of Scientific Integrity
by Laura Luebbert and Lior Pachter Background (by LL) Four years ago, during the first year of my PhD at Caltech, I participated in a journal club organized by the lab I was rotating in. I was assi…
New study finds that when people help collect data or contribute to research it can build public trust by making scientists feel personally familiar and approachable, and that trust then spreads to how local and tangible the research feels. jcom.sissa.it/article/pubid/JCOM_2506_2026_…
How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts
jcom.sissa.it
