How does the culture of science change and improve? Many people have identified shortcomings in core social processes of science, such as peer review, how grants are awarded, how people are selected to become scientists, and so on. Yet despite often compelling criticisms, strong barriers inhibit widespread change in such social processes. The result is near stasis, and apathy about the prospects for improvement. People sometimes start new research institutions intended to do things differently; unfortunately such institutions are often changed more by the existing ecosystem than they change it. In this essay we sketch a vision of how the social processes of science may be rapidly improved. In this vision, metascience plays a key role: it deepens our understanding of which social processes best support discovery; that understanding can then help drive change. We introduce the notion of a metascience entrepreneur, a person seeking to achieve a scalable improvement in the social processes of science. We argue that: (1) metascience is an imaginative design practice, exploring an enormous design space for social processes; (2) that exploration aims to find new social processes which unlock latent potential for discovery; (3) decentralized change must be possible, so outsiders with superior ideas can't be blocked by established power centers; (4) ideally, change would align with what is best for science and for humanity, not merely what is fashionable, politically popular, or media-friendly; (5) the net result would be a far more structurally diverse set of environments for doing science; and (6) this would enable crucial types of work difficult or impossible within existing environments. For this vision to succeed metascience must develop and intertwine three elements: an imaginative design practice, an entrepreneurial discipline, and a research field. Overall, it is a vision in which metascience is an engine of improvement for the social processes and ultimately the culture of science.
Metaventory — the meta-science community
A comprehensive community of the people building meta-science and the tools they make — psychology and economics first, growing science-wide.
How can citizen science reduce psychological distance to science? Insights from three projects in contested environmental contexts
Citizen science (CS) is a participatory mode of knowledge production, enabling non-scientific actors to contribute to and sometimes contest scientific agendas and interpretations, making it a way to bridge science and society. This paper examines how that potential unfolds by analysing the individual perspectives of citizen scientists through the lens of Psychological Distance to Science (PSYDISC). Drawing on three case studies of contested environmental CS, we identify which contextual aspects of CS shape citizen scientists' experiences of social, spatial, temporal, or hypothetical distance to relevant science, and how these experiences may relate to trust. Our findings underscore the role of science communication as both a channel for dissemination, and as a constitutive element of participatory research; crucial for reducing psychological distance and enabling socially robust knowledge production, especially in contested, policy-relevant science settings.
Artificial intelligence and illusions of understanding in scientific research
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
Metascience for whom? A question as old as science.
Before we fix science, we need to ask who built it!
Peer Production in Citizen Science: A Community-Centered Approach on the Example of Personal Science
Citizen science encompasses a wide range of practices where online collaboration for knowledge production plays a significant role. However, the study of forms of online collaboration other than crowdsourcing in citizen science has remained largely unexplored. This thesis aims to fill this gap by investigating peer production as a form of collaboration in online citizen science communities of practice. First, peer production theory was operationalized as a working model and used to analyze collaboration in citizen science case studies. This was followed by a comprehensive participatory design process for a specific use case involving the personal science community of practice. This process resulted in the creation of the “Personal Science Wiki”, an online space for consolidating community knowledge through peer production. Subsequently, a usability and card sorting study identified and resolved issues with the wiki implementation, and provided insights into mental models and content requirements regarding self-research knowledge. The lessons learned from the participatory design process were generalized as process recommendations for designing peer production solutions and knowledge management systems with communities of practice.
A New Paradigm for Scientific Publishing, Peer Review, and Impact Assessment
Scientific publishing and peer review have evolved little in three centuries, while the demands placed on them have grown profoundly. The growing role of artificial intelligence has underscored deep, systemic shortcomings of an aging system that has largely evaded innovation, a system whose origins are appallingly closer to the invention of the printing press than to the internet. We can do better – much better. This article is intended as the beginning of a communal experiment: a living document that critically reviews the modern academic publishing and peer-review system and presents a concrete framework to address what bibliometrics experts¹ have characterized as "the pervasive misapplication of indicators to the evaluation of scientific performance". Building on the Leiden Manifesto, DORA, and a body of scholarship spanning many disciplines and decades, we present a community-governed, non-profit platform organized around three trust-weighted impact factors, for articles, authors, and reviewers, with full algorithmic transparency, an open development log, and structural decoupling of credibility scoring from content moderation and from monetization. We invite the community to discuss, critique, and help shape it.
Empowering science communities with open, democratic, researcher-owned infrastructure.
We’re building communities and tech for publishing, curating, sharing, and discussing research online using ATProto and other decentralized protocols.
Empowering science communities with open, democratic, researcher-owned infrastructure.
We’re building communities and tech for publishing, curating, sharing, and discussing research online using ATProto and other decentralized protocols.
Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Request PDF
Request PDF | On Jan 1, 2013, Jessica McCallum Breen and others published Civic, Citizen and Grassroots Science: Towards a Transformative Scientific Research Model | Find, read and cite all the research you need on ResearchGate
Science Must Decentralize
Knowledge production doesn’t happen in a vacuum. Every great scientific breakthrough is built on prior work, and an ongoing exchange with peers in the field. That’s why we need to address the threat
Building Collective Intelligence Networks for Flourishing Scientific Communities
Scientific communities are straining under mounting challenges—knowledge fragmentation, outdated publication processes, institutional erosion and funding cuts—that threaten our capacity to address urgent global problems. Traditional scientific process
Field Theory: AI as Social Science Question, Object & Tool
Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes
Scientific progress relies on the effective accumulation, synthesis, and critical evaluation of knowledge. Traditionally, the well-documented, peer reviewed publication served as the primary standard for filtering and disseminating credible findings within the scientific community. Recently, however, we are witnessing an unprecedented acceleration in research output, a veritable explosion of scientific publications across all disciplines [1]. Yet, this very abundance creates a paradox: the sheer volume threatens to overwhelm the mechanisms designed for its assimilation and synthesis. Researchers, even within highly specialized subfields, face an almost insurmountable challenge in keeping abreast of relevant developments, integrating disparate findings, and identifying the truly novel signals amidst the noise [2]. This information overload contributes to disciplinary fragmentation, hindering the cross-pollination of ideas essential for disruptive innovation [3]. Furthermore, persistent concerns regarding "reproducibility crisis" [2], predatory journals, inflation of research areas[4], growing retractions and the potential influences of bibliometrics on research direction [5] highlight systemic challenges in validating and prioritizing scientific contributions to fundamental knowledge.
Mapping an Ecology of Integrative Approaches to Addressing the Metacrisis
Dear friends, The Institute of Applied Metatheory (IAM) is pleased to announce the release of a new white paper that charts the landscape of leading-edge responses to the global metacrisis. In “Mapping an Ecology of Integrative Approaches to Addressing the Metacrisis”, IAM scholars Brandon Norgaard, Nicholas Hedlund, PhD, and Claudia Meglin provide a sweeping (albeit
⚡ Metaventory: building better science ⚡ Had many discussions about the need for mapping out all that's meta-science: people, tools, events, jobs, simulations, etc. in one place. Metaventory aims to help connect those interested in meta-science & improving science. metaventory.vercel.app
