72 research outputs found
How Does Citizen Science "Do" Governance? Reflections from the DITOs Project
Citizen science (CS) is increasingly becoming a focal point for public policy to provide data for decision-making and to widen access to science. Yet beyond these two understandings, CS engages with political processes in a number of other ways. To develop a more nuanced understanding of governance in relation to CS, this paper brings together theoretical analysis by social science researchers and reflections from CS practice. It draws on concepts from Science and Technology Studies and political sciences as well as examples from the "Doing-It-Together Science" (DITOs) project. The paper develops a heuristic of how CS feeds into, is affected by, forms part of, and exercises governance. These four governance modes are (1) Source of information for policy-making, (2) object of research policy, (3) policy instrument, and (4) socio-technical governance. Our analysis suggests that these four dimensions represent different conceptions of how science and technology governance takes place that have not yet been articulated in the CS literature. By reflecting on the DITOs project, the paper shows how this heuristic can enrich CS. Benefits include project organisers better communicating their work and impacts. In its conclusion, the paper argues that focusing on the complexity of governance relations opens up new ways of doing CS regarding engagement methodologies and evaluation. The paper recommends foregrounding the broad range of governance impacts of CS and reflecting on them in cooperation between researchers and practitioners
Content-based and Algorithmic Classifications of Journals: Perspectives on the Dynamics of Scientific Communication and Indexer Effects
The aggregated journal-journal citation matrix -based on the Journal Citation
Reports (JCR) of the Science Citation Index- can be decomposed by indexers
and/or algorithmically. In this study, we test the results of two recently
available algorithms for the decomposition of large matrices against two
content-based classifications of journals: the ISI Subject Categories and the
field/subfield classification of Glaenzel & Schubert (2003). The content-based
schemes allow for the attribution of more than a single category to a journal,
whereas the algorithms maximize the ratio of within-category citations over
between-category citations in the aggregated category-category citation matrix.
By adding categories, indexers generate between-category citations, which may
enrich the database, for example, in the case of inter-disciplinary
developments. The consequent indexer effects are significant in sparse areas of
the matrix more than in denser ones. Algorithmic decompositions, on the other
hand, are more heavily skewed towards a relatively small number of categories,
while this is deliberately counter-acted upon in the case of content-based
classifications. Because of the indexer effects, science policy studies and the
sociology of science should be careful when using content-based
classifications, which are made for bibliographic disclosure, and not for the
purpose of analyzing latent structures in scientific communications. Despite
the large differences among them, the four classification schemes enable us to
generate surprisingly similar maps of science at the global level. Erroneous
classifications are cancelled as noise at the aggregate level, but may disturb
the evaluation locally
Type III Secretion Is Essential for the Rapidly Fatal Diarrheal Disease Caused by Non-O1, Non-O139 Vibrio cholerae
Cholera is a severe diarrheal disease typically caused by O1 serogroup strains of Vibrio cholerae. The pathogenicity of all pandemic V. cholerae O1 strains relies on two critical virulence factors: cholera toxin, a potent enterotoxin, and toxin coregulated pilus (TCP), an intestinal colonization factor. However, certain non-O1, non-O139 V. cholerae strains, such as AM-19226, do not produce cholera toxin or TCP, yet they still cause severe diarrhea. The molecular basis for the pathogenicity of non-O1, non-O139 V. cholerae has not been extensively characterized, but many of these strains encode related type III secretion systems (TTSSs). Here, we used infant rabbits to assess the contribution of the TTSS to non-O1, non-O139 V. cholerae pathogenicity. We found that all animals infected with wild-type AM-19226 developed severe diarrhea even more rapidly than rabbits infected with V. cholerae O1. Unlike V. cholerae O1 strains, which do not damage the intestinal epithelium in rabbits or humans, AM-19226 caused marked disruptions of the epithelial surface in the rabbit small intestine. TTSS proved to be essential for AM-19226 virulence in infant rabbits; an AM-19226 derivative deficient for TTSS did not elicit diarrhea, colonize the intestine, or induce pathological changes in the intestine. Deletion of either one of the two previously identified or two newly identified AM-19226 TTSS effectors reduced but did not eliminate AM-19226 pathogenicity, suggesting that at least four effectors contribute to this strainâs virulence. In aggregate, our results suggest that the TTSS-dependent virulence in non-O1, non-O139 V. cholerae represents a new type of diarrheagenic mechanism
Perspective on the Use of LNT for Radiation Protection and Risk Assessment By The U.S. Environmental Protection Agency
The U.S. Environmental Protection Agency (EPA) bases its risk assessments, regulatory limits, and nonregulatory guidelines for population exposures to low level ionizing radiation on the linear no-threshold (LNT) hypothesis, which assumes that the risk of cancer due to a low dose exposure is proportional to dose, with no threshold. The use of LNT for radiation protection purposes has been repeatedly endorsed by authoritative scientific advisory bodies, including the National Academy of Sciencesâ BEIR Committees, whose recommendations form a primary basis of EPAâs risk assessment methodology. Although recent radiobiological findings indicate novel damage and repair processes at low doses, LNT is supported by data from both epidemiology and radiobiology. Given the current state of the science, the consensus positions of key scientific and governmental bodies, as well as the conservatism and calculational convenience of the LNT assumption, it is unlikely that EPA will modify this approach in the near future
Data Science and Management for Large Scale Empirical Applications in Agricultural and Applied Economics Research
Industrial revitalization via industry 4.0 â A comparative policy analysis among China, Germany and the USA
Potentiality of Big Data in the Medical Sector: Focus on How to Reshape the Healthcare System
- âŠ