Dataset Information

Communication Regulatory Science: Mapping a New Field.

ABSTRACT: Communication regulatory science is an emerging field that uses validated techniques, tools, and models to inform regulatory actions that promote optimal communication outcomes and benefit the public. In the opening article to this special issue on communication and tobacco regulatory science, we 1) describe Food and Drug Administration (FDA) regulation of tobacco products in the US; 2) introduce communication regulatory science and provide examples in the tobacco regulatory science realm; and 3) describe the special issue process and final set of articles. Communication research on tobacco regulatory science is a burgeoning area of inquiry, and this work advances communication science, informs and potentially guides the FDA, and may help to withstand legal challenges brought by the tobacco industry. This research has the potential to have a major impact on the tobacco epidemic and population health by helping implement the most effective communications to prevent tobacco initiation and increase cessation. This special issue provides an example of 10 studies that exemplify tobacco regulatory science and demonstrate how the health communication field can affect regulation and benefit public health.

SUBMITTER: Noar SM

PROVIDER: S-EPMC6053339 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Communication Regulatory Science: Mapping a New Field.

Noar Seth M SM Cappella Joseph N JN Price Simani S

Health communication 20171213 3

Communication regulatory science is an emerging field that uses validated techniques, tools, and models to inform regulatory actions that promote optimal communication outcomes and benefit the public. In the opening article to this special issue on communication and tobacco regulatory science, we 1) describe Food and Drug Administration (FDA) regulation of tobacco products in the US; 2) introduce communication regulatory science and provide examples in the tobacco regulatory science realm; and 3 ...[more]

PMID: 29236531

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Project description:Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

Dataset Information

Communication Regulatory Science: Mapping a New Field.

Publications

Communication Regulatory Science: Mapping a New Field.

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