About the Journal

JQD:DM is an open access, peer-reviewed scholarly journal hosted on the University of Zurich's HOPE platform. All articles will be freely available online immediately upon publication and we plan no submission or publication fees for at least the first two years, but hopefully longer.

The journal publishes quantitative descriptive social science. It does not publish research that makes causal claims.

The production of descriptive knowledge is currently undersupplied in quantitative social science. As John Gerring documents in the case of political science in his 2012 article "Mere Description", scholarship in the postwar era has seen a steady trend from description to causality. We applaud developments in method that discredit spurious causal research; this has led to a much-needed advance in rigor for claims of causation. As this shift illustrates, social science has a momentum that takes years to redirect, and graduate training, journal space, prestige, and grant funding have all been shifting away from quantitative description. Our hope is that this journal is the beginning of another course correction.

Descriptive knowledge is necessary for the following steps in the social science process:

Hypothesis generation: Trivially, we need to know what is before we can derive hypotheses about why it is or what it does. Too often, experiments are designed without first establishing the prevalence of the causes or effects being studied.

Topic importance: We want to study the most important questions, but relying on the intuitions of social scientists about "importance" is baldly unscientific. Quantitative description offers a framework for rigor.

Generalizability: The goal of many social scientists is to create "generalizable" knowledge. There are open questions about how best to do this, but an essential component of any generalizability project is the knowledge of how the target context differs from the known contexts. This knowledge is quantitative description, which thus serves as a complement to causal knowledge, enabling its applicability to novel contexts.

Notice that the scope of this initial incarnation of the JQD is restricted to Digital Media. The last few decades have seen the fastest increase in the production of human communication in history. JQD:DM will publish quantitative description of this increasingly massive sphere of online media.

A note on scope: The journal is aiming not just for digital trace data sets or structured datasets but evidence that speaks to some substantive question about communication processes and media. We will be working with a letter-of-intent model so that people can get feedback about the applicability of their piece for the journal before investing in writing up their material. Examples of relevant work include:

  • What segments of the population are using a particular platform?
  • What information sources do people use to learn about Covid-19?
  • Which political parties have the most engagement on social media?
  • What do different religious organizations communicate to their members about a particular topic?
  • Who is most likely to share videos about fake news?
  • What types of science articles have the most edits on Wikipedia?
  • What proportion of videos people share reference social justice topics?