Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation

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Scientific communities are straining under mounting challenges—knowledge fragmentation, outdated publication processes, institutional erosion and funding cuts—that threaten our capacity to address urgent global problems.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

Sensemaking Networks: Transforming Social Media into a Sensemaking Layer for Science | Ronen Tamari

Sensemaking Networks is a project aiming to address three interrelated limitations of the current science publishing and communication infrastructure: (1) poor reach and feedback, (2) knowledge fragmentation, and (3) rigid formats. To address these issues, Sensemaking Networks combine two promising trends in science publishing and communication: science social media and semantic nanopublishing. While science social media significantly impacts scientific processes and public engagement, it lacks formal recognition and accessibility. On the other hand, nanopublications offer the benefits of formal scientific publishing and machine-readable knowledge representation. Their integration represents a promising direction that could serve as a transformative sensemaking layer for science, enhancing the collective synthesis, dissemination, and evaluation of research.

Science as Collective Sensemaking

What is Science?

Science as Collective Sensemaking

What is Science?

Beyond the Individual: Understanding the Evolution of Collective Intelligence

This chapter outlines the evolution of collective intelligence, starting from its ancient roots and concluding with modern digital platforms. It discusses intelligence theories, project examples, and the impact of technology on collaborative efforts. Key focuses include the role of the internet and online communities in boosting our collective IQ, with a particular emphasis on Douglas Engelbart's contributions and the open-source movement, as exemplified by Linux's development. The chapter examines how digital transformation has facilitated new forms of community and knowledge sharing, significantly influencing fields such as management, decision-making, and organizational learning. Various scholars and their definitions of CI are discussed, including Pierre Lévy's vision of universally distributed intelligence and the concept of swarm intelligence in biological sciences. We then move on to practically implemented CI projects, exploring crowdsourcing as a manifestation of CI in business and social projects and examining possibilities of harnessing the wisdom of crowds for problem-solving and innovation. The chapter concludes with a presentation of the current state of collective intelligence academic research.

Sensemaking Networks: Project Introduction - Cosmik Labs

Incorporating science social media into the scientific process

AI as the Catalyst of the New Paradigm of Science? | Cadmus Journal

The rapid advancement of Artificial Intelligence (AI) is reshaping the landscape of scientific inquiry. This article examines the challenges confronting modern science, including knowledge fragmentation, diminishing returns of current paradigms, and the disconnect between science and society. It explores how AI offers solutions enhancing collaboration and integrating knowledge—as well as considers the possible “darker side” of using AI in research. However, the advent of AI in science not only enhances scientific research but also redefines the role of human researchers and fosters a new paradigm of human understanding. By embracing these transformations, a more cohesive and responsive scientific enterprise can emerge.

How large language models can reshape collective intelligence

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.

How large language models can reshape collective intelligence

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.

Sensemaking Networks Part 3: building partnerships and prototypes - Cosmik Labs

Semantic cross-posters for re-integrating science social media

Education for collective intelligence

Collective Intelligence (CI) is important for groups that seek to address shared problems. CI in human groups can be mediated by educational technologies. The current paper presents a framework to ...

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

Outcomes Graph: A protocol for applied science coordination - DeepScience Ventures

In this article, we explain how our Outcomes Graph works, including how the functions of scientific knowledge shape the features we've developed and how we're using it to harness the collective intelligence of venture scientists.

Managing scientific knowledge for policy on the ATmosphere - Mathew's newsletter

Two types of Atmosphere toolkit are needed if ATScience is to help scientists do science and better communicate it to other audiences: one serving the researchers and their teams, the other focused on aggregation and synthesis.