A naïve Bayes approach to theory confirmation is used to compute the posterior probabilities for a series of four models of DNA considered by James Watson and Francis Crick in the early 1950s using multiple forms of evidence considered relevant at the time. Conditional probabilities for the evidence given each model are estimated from historical sources and manually assigned using a scale of five probabilities ranging from strongly consistent to strongly inconsistent . Alternative or competing theories are defined for each model based on preceding models in the series. Prior probabilities are also set based on the posterior probabilities of these earlier models. A dramatic increase in posterior probability is seen for the final double helix model compared to earlier models in the series, which is interpreted as a form of “Bayesian surprise” leading to the sense that a “discovery” was made. Implications for theory choice in the history of science are discussed.

The confirmation of scientific theories is approached by combining Bayesian probabilistic methods, in particular Bayesian causal networks, and the analysis of citing sentences for highly cited papers. It is assumed that causes and their effects can be identified by linguistic methods from the citing sentences and that the cause-and-effect pairs can be equated with theories and their evidence. Further, it is proposed that citation context sentiments for “evidence” and “uncertainty” can be used to supply the required conditional probabilities for Bayesian analysis where data is drawn from citing sentences for highly cited papers from various fields. Hence, the approach combines citation and linguistic methods in a probabilistic framework and, given the small sample of papers, should be considered a feasibility study. Special attention is given to the case of nociception in medicine, and analogies are drawn with various episodes from the history of science, such as the Watson and Crick discovery of the structure of DNA and other discoveries where a striking and improbable fit between theory and evidence leads to a sense of confirmation.

In the 1970s, quantitative science studies were being pursued by sociologists, historians, and information scientists. Philosophers were part of this discussion, but their role would diminish as sociology of science asserted itself. An antiscience bias within the sociology of science became evident in the late 1970s, which split the science studies community, notably causing the “citationists” to go their own way. The main point of contention was whether science was a rational, evidence-based activity. To reverse the antiscience trend, it will be necessary to revive philosophical models of science, such as Bayesian confirmation theory or explanatory coherence models, where theory-experiment agreement plays a decisive role. A case study from the history of science is used to illustrate these models, and bibliometric and text-based methods are proposed as a source of data to test these models.