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📡 On our radar
Interesting pieces we have come across
Collection
Interesting pieces we have come across
Reinventing Discovery: The New Era of Networked Science
In Reinventing Discovery, Michael Nielsen argues that w…
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.
supercritical | shishyko!
Supercritical is a newsletter about what happens when systems produce faster than they can coordinate. AI is making the generation of knowledge abundant, but generation was never the whole system. Validation, dissemination, credit, trust: these were bundled into institutions designed for scarcity, and this legacy architecture is now the primary bottleneck. If we fail to redesign it, progress will be sawtooth rather than smooth. Using the crisis in modern science as an early warning, this newsletter imagines the capabilities and infrastructure that come next, including provenance, trust-graded disclosure, and new protocols of coordination.
Holdfast 0: Situating
This post introduces the written version of some optimistic and constructive thinking on knowledge in networks that I’ve been wrestling with since the end of last year.
In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!
In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...
Mastodon pilot for research and education
SURF and Universities of the Netherlands are jointly exploring Mastodon as an open source platform for education and research in the Netherlands.
Holdfast 0: Situating
This post introduces the written version of some optimistic and constructive thinking on knowledge in networks that I’ve been wrestling with since the end of last year.
Coordination Tech in Science: Letters, Journals, and Whatever Comes Next | shishyko!
To modernize our scientific infrastructure, we need new contextualization and coordination technologies that decouple trust from legacy branding — shifting from gatekeeping on write to algorithmic contextualization on read.
In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!
In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...
Creative Reading: Scaffolding Reading for Transformation
Reading augmentation systems increasingly help readers process text at scale. While these tools address real constraints of time and cognitive load, they often implicitly frame reading as...
Publish and Perish: How AI-Accelerated Writing Without Proportional Verification Investment Degrades Scientific Knowledge
Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize this asymmetry through a minimal two-variable ordinary differential equation model coupling review queue evolution and verification quality degradation via an endogenous, queue-pressure-driven review AI adoption mechanism. The causal chain is: writing AI adoption increases submissions, growing the review queue, which drives reviewer AI adoption under pressure, degrading verification quality and reducing net knowledge output. Under empirically informed parameters (writing acceleration γ = 2.0, review acceleration δ = 0.5), the model predicts a deceptive honeymoon where knowledge output peaks at 1.10K0 (circa 2026), followed by paradox onset at t = 6 years (2028) and long-term degradation to 0.68K0 (32% loss), approaching a steady state of 0.60K0 (40% loss). The critical condition for net benefit is δ > γ; the current operating point lies deep in the paradox regime. Empirical validation against NeurIPS, ICLR, arXiv, and bioRxiv submission data shows qualitative consistency with observed post-ChatGPT acceleration patterns. Policy analysis reveals that only combined interventions such as review infrastructure investment paired with institutional quality standards can restore positive knowledge production.
Chasing RATs: Tracing Reading for and as Creative Activity
Creativity research has privileged making over the interpretive labor that precedes and shapes it. We introduce Reading Activity Traces (RATs), a proposal that treats reading -- broadly defined to include navigating, interpreting, and curating media across interconnected sources -- as creative activity both for future artifacts and as a form of creation in its own right. By tracing trajectories of traversal, association, and reflection as inspectable artifacts, RATs render visible the creative work that algorithmic feeds and AI summarization increasingly compress and automate away. We illustrate this through WikiRAT, a speculative instantiation on Wikipedia, and open new ground for reflective practice, reader modeling, collective sensemaking, and understanding what is lost when human interpretation is automated -- towards designing intelligent tools that preserve it.
AI is turning research into a scientific monoculture
Generative AI deserves scientific attention. But the rush to study it is producing a feedback loop of topical and methodological convergence, flattening scientific imagination and crowding out the pluralism needed to keep research adaptive, resilient, and intellectually generative.
COS goes FOSS The sorry state of scientific publishing and how we could move to an open and resilient infrastructure
“The data files remains our property and are not deposited for free access.”
"The truly visionary AI for Science company is not automating experiments or AI-generating Nature papers, but building technology to improve the collective sensemaking ability of scientists @cosmik.network and alphaXiv are both examples of startups trying to build new sensemaking infrastructure"
Science as Collective Sensemaking
republicofscience.substack.com"the crucial step in crisis engineering is to re-establish a common view which corresponds to reality – to restore sense-making. Once that step has been taken, actually solving the problem becomes a tractable task, and without it nothing is going to work" -- @dsquareddigest.bsky.social
a failure of sense making
backofmind.substack.com
