How do philosophers of science gain knowledge about science? Conducting case studies has become one dominant answer to this question. Many episodes in the history of science come with their own unique features, justifying the use of such a detail-oriented method. The method runs into trouble though, when applied to larger structures, like the relative prominence of competing methods, models, or subjects, which might get lost or suffer distortions through the biases introduced by the focus on small parts of the literature.

In this contribution, we introduce a novel, computational way of assessing the degree to which individual case studies can be expected to remain justified even when they are drawn from a large body of literature. We do so by providing a technique of measurement for the structural similarity between overlapping sub-samples drawn from comprehensive corpora of scientific literature. To address the question of the generalizability of case studies, we take many small sub-samples from a large known corpus. Using machine-learning methods, we identify networks in these samples, which relate to different features of interest, like thematic connectedness or similarity in methodological foundations. We then measure, how well the networks that can be extracted from small samples reconstruct the macro-structures that arise when the whole corpus is considered. The results allow us to propose a general upper bound for the degree to which we can expect case studies on small samples to faithfully represent a whole field. We can further present answers to the question whether scientific case studies ought to focus on elite samples of highly cited literature, or whether they can be expected to increase their epistemic value by consciously incorporating marginalized literature. Apart from these general points, the proposed method can also be used as a way to suggest improvements to the scope of individual case studies through interactive exploration of computationally refurbished material.

Research that is not explicitly bound to a distinct discipline has not yet gained much acknowledgment with regard to research impact assessment and mapping of the respective research field. In this article, we provide a suggestion for new impact metrics taking the example of migration research as a phenomenon-oriented research field. Therewith, research merit is made comparable and is calculated irrespective of discipline. We show how the field of migration studies evolved, apply our new metrics and give insight into impact factors, numbers of citations of articles, and authors, as well as journals. Further, we present a field-related collaboration network that indicates a rather disconnected community. However, collaborations between researchers are on the rise. In our conclusion, we argue that there is a need for further assessment of research impact within other phenomenon-oriented research fields.