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
An epistemology for democratic citizen science
Abstract. More than ever, humanity relies on robust scientific knowledge of the world and our place within it. Unfortunately, our contemporary view of scie
Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop Report
This report is an outcome of a Computing Community Consortium (CCC) visioning workshop on Grand Challenges for the Convergence of Computational and Citizen Science Research conducted on April 8-9, 2025, in Washington, D.C. as well as through several precursor virtual input-gathering sessions. These events brought together experts across relevant disciplines to develop a research agenda that brings to fruition the above vision on how humans and machines may team up to solve some of the world's most pressing scientific problems. Citizen science delivers measurable economic and national value. Public participation in scientific research generates millions of dollars in volunteer labor value, extends government agency capacity, and directly supports federal priorities in areas such as disaster management, public health, water, energy, workforce development, and many more. At the same time, 21st-century scientific infrastructure requirements for citizen science (from hardware and cyberinfrastructure to data and computational frameworks) mirror those for computational science more generally. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world use case for a novel robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. In this report, we outline the key findings, future research directions, and recommendations that emerged from the April 2025 CCC Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop.
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.
Citizen science in environmental and ecological sciences
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.
The Citizen Lab - The Citizen Lab
The Citizen Lab is an interdisciplinary research unit at the Munk School of Global Affairs & Public Policy, University of Toronto. We apply our collective expertise in the fields of law, computer science, cybersecurity, political science, and social sciences to investigate complex issues of the 21st century. Explore focus areas Get the latest research in […]
Mapping citizen science contributions to the UN sustainable development goals
The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.
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.
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.
The Bazaar of Scientific Knowledge | shishyko!
What if we didn't collapse all the knowledge from the scientific process into one paper?
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.
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.
👀 "Rather than viewing citizen science merely as crowdsourced data collection or computational tools as autonomous infrastructure, we advocate for human-machine systems in which citizen scientists, including distributed publics, participate as monitors, interpreters...and co-producers of knowledge."
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Inspiration and existing efforts where citizens drive the whole scientific cycle, from problem definition through to interpretation of results. This level of citizen science has been called "extreme" in this paper: link.springer.com/article/10.1140/epjst/e2012-0… I love this term! :) Esp. interested in concepts/approaches that help with the construction and maintenance of local/individual knowledge (vs. aggregates only)
