Many scientific fields share common interests for research and education. Yet, very often, these fields do not communicate to each other and are unaware of the work in other fields. Understanding the commonalities and differences among related fields can broaden our understanding of the interested phenomena from various perspectives, better utilize resources, enhance collaboration, and eventually move the related fields forward together. In this article, we present a conceptual framework, namely the Information-Model or I-model, to describe various aspects of information related fields. We consider this a timely effort in light of the evolutions of several information related fields and a set of questions related to the identities of these fields. It is especially timely in defining the newly formed Information Field from a community of twenty some information schools. We posit that the information related fields are built on a number of other fields but with their own unique foci and concerns. That is, core components from other fundamental fields interact and integrate with each other to form dynamic and interesting information related fields that all have to do with information, technology, people, and organization/society. The conceptual framework can have a number of uses. Besides providing a unified view of these related fields, it can be used to examine old case studies, recent research projects, educational programs and curricula concerns, as well as to illustrate the commonalities and differences with the information related fields.

We analyze the online response of the scientific community to the preprint publication of scholarly articles. We employ a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and April 2011. We study three forms of reactions to these preprints: how they are downloaded on the arXiv.org site, how they are mentioned on the social media site Twitter, and how they are cited in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether significant differences exist between them. Second, we run correlation tests to investigate the relationship between Twitter mentions and both article downloads and article citations. We find that Twitter mentions follow rapidly after article submission and that they are correlated with later article downloads and later article citations, indicating that social media may be an important factor in determining the scientific impact of an article. 1