The difficulty of making claims to knowledge in social science

Abstract

This paper considers three common types of claim to research knowledge, and the relative difficulty of making each type of claim in an empirically and logically justified manner. Before this, the paper looks at some more general issues often raised when discussing knowledge in social science, such as the nature of truth and justified belief, the existence of “isms” and paradigms treated like fashion accessories that one can adopt or not at will, and the intrinsic limitations of how we get to know about the “stuff” we might want to make research claims about. The idea of this early section is to remove some potential obstacles, before arguing that none of these issues is relevant to the rest of the paper about the nature of claims in their most generic form, independent of things like specific methods of data collection.
 
 The first type of claim we identify is a fully descriptive one that only summarises the data observed. This is the easiest and safest kind of claim, but even these might suffer from non-random errors and inaccuracies. However, their biggest limitation is their lack of any wider utility. The second kind of claim is a generally descriptive one that makes statements about unobserved data on the basis of a fully descriptive claim. Here we meet Hume’s problem of induction. These claims have two parts, and the inductive part cannot seemingly be justified by logic, inferential statistics (whether Fisher or Neyman-Pearson style), Carnap’s inductive probabilities, or even necessarily by Popper’s falsification process. The third type is a causal claim, which we argue must also be a general claim. We develop a model, based on the work of Mill and Bradford-Hill, of what a plausible causal claim entails. But it still has all of the problems emerging from the first two types of claim, and adds a further problem created by our inability to assess causes directly. The paper concludes by suggesting how social science can proceed most safely in practice.

Judging the relative trustworthiness of research results: How to do it and why it matters

“…Perhaps most famously, Einstein (1920) produced theories of special and general relativity in physics which can demonstrate both the importance of observer standpoints, and how the phenomenon under investigation can be understood/resolved at a meta‐level for all standpoints. This is very different from something like relativism – which might assert that because there are many perspectives then all are inevitably equal, and anything can be or must be true (Gorard & Tan, 2022).…”

“…This review concerns the existing evidence on the use of financial intervention such as conditional cash transfers to help improve school attendance in developing countries, and comes from a larger project. In the report of that project, the weakest (0 rated) studies are simply ignored as they are judged not to contribute anything to knowledge (Gorard et al, 2022). The review studies are classified in terms of their quality (rows) and whether their results showed a benefit for attendance, no benefit or even harm, or a mixed set of results (columns).…”

Black box problem and African views of trust

“…My (descriptive) claim is that dominant views of trust grounded primarily in African scholarship can enhance our thinking about the specific problems black box clinical AI causes, at least from the African perspective. In this regard, the manuscript draws on an equally acceptable philosophical method to push the boundary of knowledge on the current discourse on the problems black box clinical AI creates within the clinical context (Gorard and Tan, 2022). This philosophical approach is not uncommon since this method has been adopted by different ethicists (Behrens, 2013;Cordeiro-Rodrigues and Ewuoso, 2022;Fayemi, 2018).…”