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.

I have been fortunate to spend my professional life in the field of what we can now call “quantitative science studies” in the company of many inspiring mentors, colleagues, and collaborators. In 1970, the field, broadly conceived, consisted of a motley group of disaffected historians, sociologists, philosophers, and information scientists searching for new ways of understanding science as a technical, social, and cultural phenomenon. Some of us had been trained in the sciences, in my case chemistry, and for one reason or another had abandoned the lab bench and sought new ways to explore our interest in science. My avenue to quantitative science studies was through the history of science and an internal study of the old quantum theory (Small, 1971). At the time, Thomas Kuhn’s iconic book (Kuhn, 1962) was the central theoretical text and controversy swirled around it. My case study fit neatly into his model. One of my goals in this paper is to reframe that historical project with the benefit of 50 years of hindsight.

In my first job after grad school, I was given the assignment of coming up with an overview of the field of nuclear physics in the 1930s. As a former chemist, my first instinct was to turn to the physics literature, and I set about coding articles from the Physical Review and entries from an early abstracting publication called Science Abstracts. After laboring for many months coding data on index cards, I had a data set from which I could generate maps of nuclear physics using the co-occurrence of indexing headings, keywords, cited authors, and cited references. The paper I wrote describing this work was submitted to the leading history of science journal but was rejected on the grounds that I had reduced history to its statistical “bare bones.” Apparently, I had exceeded the limits of what the discipline considered acceptable.

In those days, historians of science did not generally undertake quantitative studies, with the exception, of course, of the pioneering work of Derek J. de Solla Price (Price, 1986). Initially I had a negative response to his ideas, but this gradually changed as my horizons broadened. I was impressed with the budding field of sociology of science under the leadership of Robert Merton, and notable exponents such as Harriet Zuckerman, Warren Hagstrom, Diana Crane, Nick Mullins, Lowell Hargens, Jonathan and Steven Cole, and others. Although Merton was known mainly for his so-called midrange theoretical writings on the norms and ethos of science (Merton, 1968), many of his students performed empirical studies, often focused on the reward system of science and the inequality of recognition (the Matthew effect). The younger generation of sociologists, on the other hand, gravitated to the study of social networks and informal communications—what Price had called invisible colleges. Networks were necessarily at small scale, but nevertheless were treated mathematically by researchers such as Nick Mullins, Harrison White, and Linton Freeman, for example, to predict social roles. Mullins thought that citations were the only way to get at the overall social network of science, because the standard survey techniques would only work for small groups.

Information scientists were also quantitative in their approach to scientific information, including my early collaborator Belver Griffith and his mentor Bill Garvey in their studies of scientific communication. Other information scientists were exploring ways of using citations to do retrieval and automatic classification such as my boss Gene Garfield, Mike Kessler, Sam Schiminovich, and Gerry Salton. After my work on cocitation, I teamed up with Belver to work on the mapping science problem.

Although decidedly nonquantitative, I also was exposed to philosophers of science, such as Thomas Kuhn, Imre Lakatos, Ernan McMullin, Stephen Toulmin, and Larry Laudan. The philosophers had retreated from the extreme mathematization of Carnap and the logical positivists, but philosophical approaches were not purely qualitative and retained a tendency toward symbolic representation. Only later did I become aware of Bayesian philosophy of science or the explanatory coherence of Paul Thagard, about which more later. One institutional effort at the time was to foster collaboration between historians and philosophers of science, which was manifest by naming departments “the history and philosophy of science” (Feigl, 1970). At the same time, sociology of science was moving to affiliate itself with history of science, and a tug of war ensued. I think it fair to say that sociology won and history and sociology became the more common moniker. Sociology eventually lost whatever ties it had with philosophy, which I think led to problems later on.

Around 1975 I learned of a new society called the Society for the Social Study of Science (4S), which I enthusiastically joined along with many of my new colleagues in the sociology of science. Their journal was called, appropriately, Science Studies, but was later renamed Social Studies of Science. I became editor of the 4S Newsletter along with the sociologist Jerry Gaston. At ISI, where I was working, we had an unexpected visit from a Frenchman named Bruno Latour, who was looking for citation data on some biochemistry papers published by the Salk Institute. Pages from the Science Citation Index were dutifully copied for him. Many of us heard his memorable lecture at the first 4S meeting at Cornell University in 1975, which marked the advent of the anthropology of science.

Thereafter, I became aware of a large contingent of European sociologists of science who espoused a new approach to science studies called social construction. Among them was David Edge, who strongly criticized our work on cocitation (Edge, 1977). A subgroup of the constructivists, the so-called strong program, asserted that the technical substance of science is derived from social factors external to science. I got to know many of these scholars, including Nigel Gilbert, Mike Mulkay, Steve Woolgar, and Karen Knorr-Cetina. While I did not agree with them, we had many lively exchanges.

But all was not well with the juxtaposition of quantitative, and especially citation-based, work and constructivist sociology of science. The tensions became more acute at subsequent meetings of 4S and eventually erupted in overt hostility. The controversy divided the sociologists into two camps: the Mertonian normative camp versus the social constructivists. Constructivists accused the Mertonians of believing that science was objective and evidence based. They asserted that scientific papers were a kind of propaganda designed to promote the political or economic interests of their authors, rather than factual presentations constrained by Mertonian norms. Constructivists emphasized the nonnormative behavior of scientists, and the various forms of deceptions and biases engaged in by scientists. The so-called citationists found themselves mainly in the Mertonian camp.

As social construction took hold and became dominant in sociology, Mertonian normative and quantitative sociology went into decline. Many sociologists of the old school retired or became less active. My involvement with 4S dwindled, and I turned more to information science. While there may be newer, less extreme, versions of the social construction of science (Giere, 1988), it is not surprising that the approach has given rise to negative attitudes toward the practice and findings of science both on the part of sociologists and the society at large. This can be particularly damaging when it comes to existential issues such as climate change. Has the warming of the climate been socially constructed by climate scientists to further their interests? The core question is why should we take this science seriously?

It is interesting to speculate on why such a strong antiscience strain emerged in the sociology of science. Was it a rebellion against authority advocated by deconstructionists such as Derrida and Foucault? It is sometimes claimed that social construction represents a “critique” of science, while others say it debunks the true “interest laden” nature of scientific research. Any “truth” claims emerging from such an enterprise would of course be suspect. There is also a long-standing tradition of criticism of science as harmful to humanity on topics such as nuclear energy, chemical pollution, the abuse of human or animal subjects, or other ethical issues. Certainly, the general prejudices and faults of society, such as racial and sexual bias, affect scientists as members of the larger society. But this type of bias, while sometimes manifest in the actions of scientists, does not seem inherent in the nature of science. What the constructivists’ critique of science was mainly directed at, however, was the fundamental epistemic foundations of science itself, and the impossibility of gaining objective knowledge of the world.

What the constructivists did not anticipate, however, was that their antiscience critique would later find resonance with political and financial groups interested in science denial in areas such as climate change, smoking and cancer, childhood vaccination, ozone depletion, and biological evolution (Collins, 2014; Oreskes, 2019). Constructivist rhetoric also filtered into political discourse, where social media were exploited to create “truth” or “facts,” promote conspiracy theories, and label objective reports as “fake” (McIntyre, 2018).

One reason for the success of the social construction was that there was a philosophical and epistemological vacuum in the sociology of science. Sociologists of science had not paid attention to questions such as the justification or confirmation of scientific findings and there was a tendency to leave such questions to philosophers. Robert Merton, for example, while proposing a comprehensive set of social norms for science, did not offer any epistemic norms for the evaluation of scientific findings. An example of such a norm is one of the earliest precepts that guided Hellenistic philosophy, particularly astronomy, namely “saving the phenomena” or “saving the appearances.” Lloyd gives a fascinating account of this ancient precept as falling somewhere between 20th-century instrumentalism and philosophical realism (Lloyd, 1978). His interpretation is that the Greek astronomers believed that their models should accord with the observable evidence, as well as provide physical explanations.

Philosophers and scientists, for their part, were not interested in norms, and tended to dismiss or ignore the constructivist claims. Philosophy of science had traditionally been aimed at supplying a warrant for scientific knowledge, whether through logical deduction, induction, or abduction. Kuhn’s account of revolutions introduced an element of irrationality into theory choice when scientists jump to new paradigms, although he denied his theory led to irrationality. Hanson’s notion that observation was theory laden implied that sense evidence was not necessarily a neutral arbiter, but he also argued that some theories are more plausible than others (Hanson, 1972). Other philosophers at the time did not lack for theories of confirmation which included the role of evidence and observation, such as the hypothetico-deductive method, falsificationism (Popper, 1961), “inference to the best explanation” (Harman, 1965), or “Bayesian confirmation theory” (Salmon, 1967).

Another reason for the neglect of studies of confirmation is our fascination with the context of discovery, to use Reichenbach’s term, rather than the context of justification. Discovery is more exciting, delving into creativity and the unknown. Confirmation and corroboration, by contrast, seem repetitive and boring. Another excuse is to say that all theories will eventually be disproved anyway, so why bother looking at the process of confirmation? When a theory such as Einstein’s general relativity is strikingly confirmed by the observation of a black hole or gravity waves, or the standard model of particle physics is confirmed by finding a Higgs boson, we tend to treat these as old news or just things working out as we had expected them to. The philosopher Wesley Salmon, however, argues that the process of discovery and justification are intimately intertwined, involving alternating creative leaps and checks against available evidence (Salmon, 1970).

Clearly the only way to counter the constructivist antiscience bias is to give renewed attention to studies of confirmation in science: how evidence is used to confirm or disconfirm theories, and how the evidence is generated in the first place. To illustrate one approach to this problem I will turn the clock back to an episode in the history of science.

The following is a capsule history of this episode, which is marked by the failure of a theory (Heilbron & Kuhn, 1969). In 1913 Niels Bohr published his ground-breaking three-part paper on the constitution of atoms and molecules (Bohr, 1913). The first part dealt primarily with the hydrogen atom, which was believed to have one orbiting electron, but also touched on the atoms of other elements. His model of the atom was a mix of assumptions derived from classical mechanics, quantum theory, electromagnetism, and Rutherford’s recent discovery of the atomic nucleus from experiments on the scattering of alpha particles. Figure 1 is a schematic representation of this model, showing the component assumptions as incoming links and the model’s predictions as outgoing links.

Figure 1.

Causal model of Bohr’s 1913 model of the hydrogen atom.

Figure 1.

Causal model of Bohr’s 1913 model of the hydrogen atom.

Close modal

As the figure suggests, the theory was successful in predicting the spectral lines of hydrogen as well as physical properties such as the ionization potential (the energy required to ionize the atom). Ionization potential measurements were still uncertain, but Bohr believed that agreement would improve when the measurements became more accurate. The dashed link in Figure 1 connecting Rutherford’s nuclear atom and ionization potential measurements indicates that the interpretation of cathode ray tube experiments was dependent on Rutherford’s concept of the atom, with its orbiting electron.

Encouraged by his success with hydrogen, Bohr applied the model to the helium atom, which contained two orbiting electrons (Small, 1971). Here he needed an additional assumption about the orbital geometry of the second electron, and he opted for a single circular ring with two electrons on either side. In the case of helium, however, success was less certain: The ortho and para spectral lines were not predicted, although the model yielded an ionization potential having the right order of magnitude given existing measurements. Again, Bohr was optimistic that the model would be vindicated by more accurate measurements.

About 10 years later, however, more accurate measurements became available that were clearly in disagreement with the model. In Figure 2 for the helium atom, the dashed lines to spectral lines, ionization, and orbital geometry, indicate that these connections were weak.

Figure 2.

Causal network of Bohr’s 1924 model of the helium atom.

Figure 2.

Causal network of Bohr’s 1924 model of the helium atom.

Close modal

The response of physicists working on helium was to modify the assumptions used in the model. The first assumption to be modified was the orbital geometry of the orbiting electrons, for example, by placing them at an angle to one another or on inner and outer orbits. When such geometric modifications failed, physicists turned to other perceived weak points, such as the ad hoc quantum condition, or the electromagnetic force between the electrons and the nucleus. One researcher even abandoned Rutherford’s nuclear atom altogether, reverting to Thomson’s plum pudding model, but to no avail. The final bastion was to give up on the laws of classical mechanics. At that point physicists abandoned the helium problem, in favor of simpler physical systems such as the single particle oscillator. It was Werner Heisenberg who finally cracked the problem with his matrix mechanics in 1925, followed by Erwin Schrodinger’s wave equation in 1926 (Jammer, 1966). As Heisenberg described it, the two electrons in helium were behaving nonclassically by continuously swapping their identities. In the late 1920s, a number of physicists, using the matrix mechanics or the wave equation, were able to predict the helium ionization potential and the spectral lines to great accuracy.

One way of understanding what happened between 1913 and 1924 is to plot the changing certainty or probability of each assumption in Bohr’s model (see Figure 3) over the decade. In particular, the certainty of orbital geometry, classical mechanics, and the quantum condition declined, while the certainty of the measurements of the ionization potential increased.

Figure 3.

Estimated certainties (probabilities) of Bohr’s atomic theory assumptions 1913–1924.

Figure 3.

Estimated certainties (probabilities) of Bohr’s atomic theory assumptions 1913–1924.

Close modal

In this historical case the probabilities have been approximated based on qualitative historical evidence, that is, statements of participants, and the sequence with which hypotheses were modified over time. In fact, this case study is a good example of what philosophers call the Duhem-Quine problem (Howson & Urbach, 2006): That is, when a prediction fails, how do you know which hypothesis is at fault? We can postulate, following Duhem, that the order in which assumptions were modified was a function of their perceived uncertainty, or improbability (Duhem, 1954). As one hypothesis was modified, others of lesser certainty were then open to modification too, leading to a proliferation of possible variant models and a loss of research direction. The most certain of the theoretical assumptions was classical mechanics, and it was the last one to be modified. In the end, to find a new mechanical basis, the helium problem had to be abandoned in favor of a simpler atomic system, Heisenberg’s oscillator. Interestingly, the two empirical findings, spectral lines and ionization potential, were the most resilient. They survived the failure of the old theory and were used to test the new theory based on quantum and wave mechanics. Thus, the empirical findings were intrinsically more certain than the theoretical assumptions, perhaps because they were less encumbered by theory or were embedded in wider conceptual networks.

This suggests that to assess the certainty or probability of a theoretical or experimental assumption, we need to look at how embedded they are in existing knowledge networks. A comparison of the networks for hydrogen and helium (Figures 1 and 2) shows that the helium model is less embedded than the hydrogen model due to the weaker links in the helium figure to spectra, ionization potential, and orbital geometry. Although the hydrogen model had a higher degree of embeddedness than the helium model, the latter was dependent on and logically entailed the former, so that neither model survived the quantum mechanical revolution of the mid-1920s.

Obviously, a complete picture of Bohr’s atomic theory would be extremely complex, because each of the theoretical or experimental assumptions was embedded in a wider network of relationships. For example, the “nuclear atom” was embedded in the alpha particle scattering experiments of Rutherford; the quantum condition was embedded in a quantum theory network involving earlier work by Planck on blackbody radiation and Einstein’s photoelectric effect; the spectral lines were embedded in 19th-century spectroscopy of Balmer, Paschen, and others; the ionization potential from cathode ray tube discharge experiments by Franck; electromagnetism in the work of Faraday and Maxwell; and classical mechanics in a tradition going back to Isaac Newton. The only assumption without its own historical network is orbital geometry, which was the first assumption to be questioned and modified.

Our example from the history of science shows the role that confirmation and disconfirmation can play in directing the development of science through periods of conceptual change. A set of theoretical assumptions predicted experimental results in one case, but these same assumptions failed to explain experiments in another. Following a period of trying to patch up the theory by modifying its assumptions, a new approach was proposed which radically altered our view of the atom. It appears the process can be depicted as a network of connections between theoretical hypothesis and experimental findings, which are in turn imbedded to varying degrees in diverse areas of physics at the time. This suggests that a network model of some kind might be a good way to model the processes of confirmation and disconfirmation.

The two primary methods available to model such confirmatory networks are Bayesian causal networks (Pearl & Mackenzie, 2018; Sprenger & Hartmann, 2019), which, for example, have been implemented in the bnlearn package in R (Nagarajan, Scutari, & Lebre, 2013), and Thagard’s theory of explanatory coherence (TEC) implemented in his ECHO software (Thagard, 1992). Both models are based on a connectionist view of scientific ideas and operate by passing or exchanging confirming or disconfirming information to adjacent nodes following defined paths in the network in the manner of a spreading activation process. In Bayesian causal networks the links are directional, while in TEC they are undirected and can indicate agreement or contradiction. Both models require that the networks be defined beforehand, including all hypotheses and evidence to be explained, and that all parameters be set. TEC runs until the weights on each node converge to stable values between −1 and +1, while for the Bayesian network the updated probabilities (between 0 and 1) are obtained by running queries. The outputs of the Bayesian model are updated probabilities, for example, the probability of the theory given the evidence, and in TEC, the outputs are updated weights for the hypotheses and evidence.

In Bayesian networks, confirming or disconfirming information is encoded in conditional probabilities, and in prior probabilities for hypotheses. These “priors” then increase or decrease as new evidence is brought to bear; technically the priors lead to posterior probabilities, which then become the new priors. The posterior probability is the probability of the theory given the evidence. The Bayesian theory further asserts that, over time, as more evidence comes in, the probability will converge to a stable value. In TEC, the confirming or disconfirming information is encoded in the structure and type of links and in the starting weights assigned to hypothesis or evidence nodes. TEC can be rerun when new evidence comes in.

Rudolf Carnap was one of the first philosophers to advocate a probabilistic approach to confirmation, although some philosophers, for example Popper, have objected to ascribing degrees of belief to hypotheses arguing that only countable events can have probabilities (Popper, 1961, Chapter 10). Others argue that scientists do not behave in a Bayesian manner (Giere, 1988). Leydesdorff has given an information theoretic interpretation of Bayesian conditionalization without specifying how it might be applied (Leydesdorff, 1992). Yet a Bayesian probabilistic approach seems well suited to showing how evidence changes the probability of a theory over time (Salmon, 1967). For example, when a theory we are not confident in has an unexpected empirical confirmation, the prior probability of the evidence P(E) is small, and the posterior probability increases following Bayes’ theorem:
$PTE=PT*PET/PE,$
(1)
where P(T|E) is the posterior probability of the theory given the evidence, P(E|T) is the probability of the evidence given the theory, and P(T) is the prior probability of the theory. The firmness with which the theory necessitates or entails the evidence, P(E|T), also increases the posterior probability of the theory, as does a higher prior probability of the theory P(T). Conversely, if we are confident in a theory and a disconfirming piece of evidence unexpectedly appears, the posterior probability decreases due to a decrease in P(E|T). Finally, the posterior probability increases if there is a low probability of the evidence given the theory is incorrect, denoted by P(E|∼T), or decreases if, for example, a rival theory gives a better fit and P(E|∼T) is high, where the symbol ∼ indicates negation. This last point is made clear by an alternative form of Bayes’ theorem, namely Howson and Urbach’s third form of Bayes’ theorem:
$PTE=PT*PET/PT*PET+P∼T*PE∼T,$
(2)
where P(∼T) is the prior probability the theory is false and is equal to [1 − P(T)] (Howson & Urbach, 2006, p. 21). This form is derived from the standard form of Bayes’ theorem by applying the theorem of “total probability.”

It is informative to plot this alternative form of Bayes’ theorem as a surface in three-dimensional space (Figure 4).

Figure 4.

Three-dimensional surface plot of an alternative form of Bayes’ theorem.

Figure 4.

Three-dimensional surface plot of an alternative form of Bayes’ theorem.

Close modal

The figure shows three surfaces for three different values of the prior probability of the theory P(T): 0.1, 0.5, and 0.9. The vertical axis corresponds to P(T|E), the probability of the theory given the evidence or posterior probability, while the other two axes plot P(E|T) and P(E|∼T), the probabilities of the evidence given the theory is true and false respectively. Note that for each surface we can trace a diagonal from P(E|T) = P(E|∼T) = 0 to P(E|T) = P(E|∼T) = 1, which defines a straight line where the prior equals the posterior probability, P(T) = P(T|E), indicating that neither confirmation nor disconfirmation occurs. However, if P(E|T) is greater than P(E|∼T), the surface slants upwards from the diagonal and the posterior is greater than the prior probability, indicating confirmation. Conversely, if P(E|∼T) is greater than P(E|T), the surface slants downwards from the diagonal and the posterior is less than the prior probability indicating disconfirmation. This sets up a competition between how well the theory entails or predicts the evidence versus how well another theory might predict it1.

A more recent innovation is the Bayesian causal network, which allow the probabilistic modeling of complex networks of relationships based on a directed acyclic graph or DAG. The child and parent nodes in the network iteratively exchange probabilistic information by a process called belief propagation (Pearl, 2018, p. 112). The simplest such network is T -> E, where the theory T is considered as causing or leading to the evidence E. This can be modeled as P(T) * P(E|T) where P(T) is the prior probability of the theory and P(E|T) is the conditional probability of the evidence given the theory. In this simple case the solution can be computed exactly using the alternative form of Bayes’ theorem (Eq. 2) as illustrated in Figure 4.

Bayesian causal networks simplify the “joint probability distribution,” the product of the probabilities of all the independent variables, by requiring conditional probabilities be supplied only for the “children” of each “parent node” in the DAG by applying the so-called chain rule (Pearl, 2000, 14). For example, in the hydrogen atom case with seven true/false variables for the states of the theoretical assumptions and one experimental finding, without the chain rule it would be necessary to specify 27 or 128 conditional probabilities. The use of the chain rule cuts this number in half to 26. The joint probability distribution then can be written as:
$PH1*PH2*PH3*PH4*PH5*PTH1H2H3H4H5*PET,$
(3)
where the probabilities of each node follow the labeling in Figure 2,2. Prior probabilities of the hypotheses H1 through H5 were assigned values in accord with their approximate certainties circa 1913 (see Figure 3). Historical evidence was, however, not available to specify the conditional probabilities of the type P(T|H1, H2, H3, H4, H5) , so a simple rule was used based on the proportion of “true” hypotheses out of the five that the theory was dependent on. For example, P(T|H1, H2, ∼H3, ∼H4, ∼H5) was set to 2/5 = 0.4. This assumes that all hypotheses contribute positively to the theory without creating internal inconsistencies. Finally, we set the P(E|T) and P(E|∼T) to 0.6 and 0.2, respectively, due to the good fit of the theory to the spectral lines of hydrogen, and the lack of a rival theory. Based on these settings, we obtain a posterior probability of the theory, given the evidence, of 0.87 using the bnlearn package in R (Nagarajan et al., 2013). This provides a confirmation when compared to the average of prior probabilities, which was 0.7.

Turning to the helium atom (Figure 2), three changes were made to the input parameters. First, the prior probability of the orbital geometry of the two-electron atom was reduced to 0.1 due to uncertainty regarding this hypothesis circa 1924. Secondly, P(E|T) was decreased to 0.2 due to the poor fit with the experimental data, and P(E|∼T) was increased to 0.8 on the assumption that an alternative theory would perform better. These changes reduce the posterior probability to 0.27, which disconfirms the helium model when compared to an average prior of 0.6.

Paul Thagard’s TEC model was designed to determine the acceptability of scientific theories and has been the subject of extensive commentary by philosophers, psychologists, and others (Thagard, 1989). His monograph from 1992 presents numerous case studies from the history of science which clearly show how his algorithm can be applied to identify the most coherent theory of those available in a given historical context (Thagard, 1992). Unlike Bayesian causal networks, the TEC model was designed specifically for assessing theories given their experimental confirmation or disconfirmation and is much simpler to apply, requiring fewer parameters provided you accept his standard system settings. The model is based on a neural network analogy, where links can be either excitatory in the case of supporting evidence or inhibitory in the case of contradictory evidence. Although TEC does not have the dynamic nature of the Bayesian approach, where priors are sequentially updated as new evidence comes in, TEC models can be run at different points in time, as well as including competing theories in the same network. Rival theories can actively compete with contradictory assumptions weakening or strengthening each other depending on their relation to experiment. The weights of the nodes, representing the degrees of confirmation of the various hypotheses and evidence, change as the spreading activation converges to an equilibrium. The final weights for each node vary from −1 to +1 and are analogous to the posterior probabilities of Bayesian theory. The model has built-in rules, such as a penalty for multiple supporting hypotheses following the Occam’s razor idea that the fewer hypotheses the better, and a rule to enhance confirmation by finding analogous explanations and strengthening them. In this sense, Thagard’s TEC approach takes into consideration a wider range of modes of confirmation than the Bayesian approach, although this can obscure how the algorithm works in some cases.

TEC treats the agreement of theory with experiment as a special case, having a constant pre-set value of one. Treating experimental evidence as a special case can sometimes be problematic because it is difficult to differentiate theory from experiment due to the theory-laden nature of experiment. For example, in the atomic example, should Rutherford’s nuclear atom be treated as experimental or theoretical? Clearly it is both. It is perhaps best to treat the initial likelihoods of experimental evidence or theoretical assumptions in the manner of prior probabilities, and the software does provide the ability to set prior weights on evidence and hypotheses to values less than one.

Thagard has implemented TEC in Common Lisp in a program called ECHO. It is easy to run once the environment is set up and you accept the default settings in the program such as excitatory and inhibitory weights, decay rate, and so on. For example, to run the hydrogen atom model all that is necessary is to specify the hypotheses involved in the theory and the experimental evidence to be explained since there is no competing theory in 1913 and no contradictory hypotheses are involved:
$explain’H1H2H3H4H5’E1,$
(4)
where H1 through H5 are the hypotheses in Figure 1 and E1 stands for the ionization potential. This single evidence unit gives a final weight for the hypotheses of 0.15 and a weight of 0.52 for the evidence after 140 iterations. To explain both the line spectrum and ionization potential of hydrogen at the same time we specify
$explain’H1H2H3H4H5’E1$
(5)
$explain’H1H2H3H4H5’E2)$
(6)
where E2 is the hydrogen spectrum evidence. Using both experimental results gives a much higher final weight for the hypotheses of 0.41 and a weight of 0.56 for the evidence units after 142 iterations. Because of the multiple hypotheses involved we can assume that the program has reduced the degree confirmation following the Occam’s razor rule described above. However, in using two experimental confirmations the program will presumably invoke the “analogy” condition and enhance confirmation.
In the case of the helium model, where experimental agreement was poor for both the ionization potential and spectral lines, we used reduced weights of 0.2 for both the hypotheses and the evidence as follows:
$explain’H1H2H3H4H5’E1.2$
(7)
$explain’H1H2H3H4H5’E2.2$
(8)
$data’E1.2E2.2$
(9)
The final weights after 76 iterations were 0.01 for the hypotheses and 0.16 for the evidence units, a very weak confirmation. Note that in ECHO it is not possible for a hypothesis to directly contradict an evidence unit, and thus it is necessary to use low initial weights to represent this situation.

It is not yet possible to state a clear preference for the TEC or Bayesian network approach, or whether it might be possible to create an alternative model combining the simplicity of TEC with the rigor of the probabilistic framework. Cabrera (2017) provides a comparison of the different approaches. Thagard has expressed reservations concerning the ability of a Bayesian approach to model historical cases such as Lavoisier’s theory of combustion. He states: “We would have to take each hypothesis separately and calculate its probability given the evidence, but it is totally obscure how this could be done… What, for example, is the conditional probability of burned objects gaining in weight given the hypothesis that oxygen is combined with them?” (Thagard, 1992, p. 92). It is true that the Bayesian approach involves specifying numerous probabilities that are difficult to tie to historical evidence. On the other hand, it does not seem difficult to construct a causal network model of Lavoisier’s oxygen theory of combustion along the lines outlined here for Bohr’s atomic models, taking each hypothesis as a weighted binary choice. Hypotheses such as “oxygen is a component of air,” “oxygen has weight,” and “oxygen combines with metals to form calxes” are connected by directed links to a theory node which in turn leads to evidence nodes such as “calxes show a weight gain after burning” and “burning uses up oxygen from the air.” Again, the challenge would be to specify all the conditional probabilities based on some historical rationale.

Mention should also be made of a difficulty with the Bayesian approach, the “problem of old evidence,” especially as applied to the hydrogen atom and other historical cases (Howson & Urbach, 2006; Sprenger & Hartmann, 2019). Both the spectral lines of hydrogen and the ionization potential were known prior to Bohr’s formulation of the theory, which strictly speaking cannot result in an increase in the posterior probability. Some theorists have argued that this problem can be overcome by considering such evidence as not being “entailed” by the new theory until after the theory is formulated. However, the prohibition on old evidence is counterintuitive because our confidence in a theory increases if it can explain any evidence, whether old or new.

Finally, a comment should be made on a new theory that claims that all we need for scientific rationality is a consensus of diverse individuals (Oreskes, 2019). While consensus is correlated with low hedging and high certainty (Small, 2019), which is measurable using citation contexts, consensus is not adequate as a theory of rationality or confirmation in science. If it were, we would have to say that any set of beliefs, if agreed upon by a large enough group, is justified, including a paradigm such as the old quantum theory regardless of its fit with experiment or the better fit offered by an alternative theory.

Also relevant to this discussion is E. O. Wilson’s concept of consilience (Wilson, 1998) following the 19th-century philosopher William Whewell. In the case of the Bohr atom, we saw that the theoretical assumptions and empirical confirmations connected work across several subfields of physics: atomic theory, spectroscopy, cathode ray tube work, particle scattering, Newtonian mechanics, electromagnetic theory, and quantum theory. Some of these connections were unexpected or low probability in the Bayesian sense. For example, that Planck’s constant could be related to spectroscopic data and cathode ray discharge experiments in the context of an atomic model was striking. The joining of diverse well-established knowledge areas in a network should by itself increase our confidence in the theory (Niiniluoto, 2016). The fact that Bohr’s hydrogen model was superseded in the 1920s did not prevent Einstein from stating as recently as 1951 that Bohr’s explanation of the spectral lines of hydrogen “… appeared to me like a miracle—and appears to me as a miracle even today. This is the highest form of musicality in the sphere of thought.” (ter Haar, 1967, p. 42)

There are many similar case studies in the history of science where a Bayesian model fits the historical narrative (Weinert, 2010), but to generate a wider sample of cases of confirmation or disconfirmation, bibliometric methods seem the most promising. Ideally, the methods should generate bibliometric networks for specific topics that can be tracked over time so that changes in the degree of confirmation can be followed. The nodes in the networks should correspond, to some extent, with theoretical assumptions or empirical findings, and at least some connections between the nodes should be causal in nature, approximating a DAG. The existing methods of document clustering and mapping are obvious candidates for such a wider sampling, including the various citation-based clustering methods (Klavans & Boyack, 2017).

Furthermore, the method of citation context analysis can be applied for the identification of causal relationships signaled by words such as cause, predict, explain, suggest, and demonstrate; that is, epistemic verbs that appear frequently in scientific texts and especially citation contexts (Bertin & Atanassova, 2014). Thus, if maps are generated by citation data, and the full texts of papers citing the clustered papers are used to provide citation contexts, then it might be possible to find causal relationships imbedded in the network. Another way in which citation contexts can assist is in estimating prior probabilities, that is, the degrees of certainty regarding the cited papers representing theories or empirical results. This can be implemented by counting hedging terms, such as may, might, and could in the citation contexts.

Earlier studies have suggested that such an approach is feasible. In a study of a cocitation cluster on the topic of leukemia viruses where the contexts of cocitations were analyzed, about one-third of the links were found to be “causal” (Small, 1986). For example, a cocitation context for one of the links on the map stated: “In the presence of murine leukemia virus, these MCF viruses can accelerate the development of tumors.” In other words, a causal relationship was postulated. Furthermore, the linked documents in the network provided the evidential basis for the causal assertion.

Similarly, another case study of a cocitation cluster on water pollution uncovered causal links among papers showing that the use and subsequent disposal of steroid hormones by humans could impair the fertility of fish downstream from wastewater treatment plants (Small & Klavans, 2011). Again, about one-third of the links were causal in nature, representing about 10 distinct causal assertions, such as “These chemicals are probably responsible for elevated incidence of intersex characteristics in freshwater fish.” As in the previous example, the evidential basis for the assertion was contained in the linked documents, such as water quality measurements and statistics on fish populations.

Cross-sectional estimates of certainty may also be of interest. For example, a state of high uncertainty (low prior probability) might be associated with a “causal” link indicating that a disconfirming event has occurred or that the link is controversial. Using a sample of the top 1,000 highly cited papers from PubMed Central, both the hedging rate and rate of causal words were computed using citing sentences (Small, 2018). Papers were selected having at least a 10% rate for both hedging and causality. The 36 papers selected cover a range of controversial topics, the most prominent of these being inflammation as a cause of disease. As one citing author states: “Current knowledge on inflammation-induced carcinogenesis is limited but the widespread implications of this phenomenon on human health warrant further studies.” Other topics were the possible toxic effects of nanoparticles and the missing heritability in genetic studies.

Using the same data set, 27 papers were selected having high certainty (≤3% hedging rate) and high causality (≥10%). About 40% of these were statistical studies on the prevalence of various diseases in the population, based on epidemiological evidence. This suggests that strong evidence and high certainty are correlated. In this high certainty sample, it was necessary to restrict papers to nonmethods because method papers have very low hedging rates. This is consistent with the higher certainty of empirical findings which we saw in the atomic theory case.

However, ideally, we would like to study how the degree of confirmation changes over time. We can do this with highly cited papers in the following way. Assume we have the citing papers for each of the highly cited papers and the citing sentences where they are referenced. Taking the publication years of the citing papers, we find the median citing year for each highly cited paper and divide the citing sentences into “early” and “late” sets, computing the hedging rate for the two sets by searching for hedging words. The hedging rate is the percentage of sentences containing one or more of the hedging words. By comparing the “early” and “late” hedging rates, we can determine which highly cited papers have increasing or decreasing hedging rates. Similarly, we compute the rate of causal words for the two sets. A high causality rate combined with a decrease or increase in the hedging rate suggests that confirmation or disconfirmation has occurred.

Such an analysis was performed using the 1,000 highly cited papers from PubMed Central noted above. We can illustrate the results by considering one of these papers published in Nature in 1993, entitled “A synaptic model of memory: long-term potentiation in the hippocampus.” This paper went from an “early” hedging rate of 12.1% to 5.6% in the “late” period and had a causal word rate of 8.9%. One citing author stated: “The involvement of NMDA receptors to the plasticity of synaptic inputs in the CNS has been well demonstrated.” While we have not constructed a causal network for this research area, a rough Bayesian analysis can be carried out assuming that the hedging rate is inversely related to the prior probability of the paper’s findings. Thus taking the “early” prior probability to be 100% – 12%, or 0.88, and the “late” posterior probability to be 100% – 5.6%, or 0.94, we can apply the alternative form of Bayes’ theorem (Eq. 2) to compute that posterior probability by setting P(E|T) and P(E|∼T) at 0.6 and 0.3 respectively. Clearly, the next step is to find a bibliometric justification for these settings, and this appears to involve quantifying of how well this theory of memory or, its rival theories, explain the evidence. A crude proxy for this metric might be simply the proportion of “causal” citations to the paper in the “late” period. Bibliometric methods could also be used to identify rival theories and assess their hedging and causal rates. Such methods hold promise for the identifying instances of confirmation, particularly in citation-based clusters.

This paper suggests a new direction for quantitative science studies: Why should we believe the findings of science? I have argued that it will be important for quantitative science studies to address confirmation in science and the role of evidence in that process. The neglect of such studies in the past has fostered the belief that in science “anything goes” and we can invent our own scientific “facts” and impose our “reality” on others. While this has been a perennial topic in the philosophy of science, it has been of little concern to sociology, history of science, or quantitative science studies. This is not about reestablishing some lost authority of science or asserting its special status as the ultimate arbiter of knowledge, but it is about correcting an imbalance in the image of science as an interest-driven and politically motivated activity untethered to evidence.

The field of quantitative science studies has the tools to begin to examine the dynamics of confirmation in scientific texts. With citation contexts we can assess the collective opinions of the scientific community, using hedging to measure uncertainty, and epistemic words to locate causal assertions tied to evidence. One place to begin is to identify causal networks embedded in our maps of science, tracking them over time to assess the impact of evidence on the certainty or uncertainty of theoretical assumptions. In approaching confirmation as a bibliometric problem, we are implicitly moving from the personalist focus of the philosophy of science to a community or social focus.

Possible theoretical approaches for this effort are Bayesian causal networks and Thagard’s explanatory coherence. Whether individual scientists really behave to maximize the probabilistic “consistency” or “coherence” of their beliefs is not important. What is more important is whether this can be discerned at the level of a specialty or a community. If what we find is “mob psychology” as Lakatos feared Kuhn’s theory would lead to (Lakatos, 1970, p. 178), and not evidence-based rationality, we would have a sound basis for a real critique of science.

The difficulties and limitations of course are substantial. The theoretical models will not be easy to apply, requiring translating bibliometric and textual data to model variables. A one-to-one translation of a bibliometric network into a causal network will not, in general, be possible. The time lags between the appearance of new evidence and its manifestation in the changing appraisals of the citing community will be difficult to coordinate. On the plus side, quantitative science studies have the opportunity to address an issue of great importance to the scientific community generally, namely the integrity of science, and at the same time rebuild bridges with the philosophy of science that should expand the types of problems we can address. It is of interest that a recent book on Bayesian philosophy of science has suggested that bibliometric methods might be applicable to the topic (Sprenger & Hartmann, 2019).

A relevant metaphor for the scientific endeavor comes from Lee Smolin (Smolin, 2019, p. 277). To paraphrase, you are walking through a landscape of high peaks of certain knowledge and deep valleys of uncertainty: “So I have a decision to make: I either keep on the present path, which will end up on the top of that low hill just past the next village, or head down into the swamps to stumble along unknown paths in search of undiscovered mountains.”

The author has no competing interests.

No funding has been received for this research.

I would like to thank Paul Thagard for providing his ECHO software and Mike Patek for technical assistance implementing it. Also, I am grateful to Marco Scutari for comments on the Bayesian network for Bohr’s theory.

1

In the Bayesian scheme, incorrect or uncertain evidence, denoted as P(∼E), can be accounted for because P(E|T) = 1 − P(∼E|T), where P(∼E|T) means the theory predicts incorrect evidence, which decreases P(E|T).

2

The dependency of the ionization potential on Rutherford’s nuclear atom has not been included, but could be added with a term P(E|H4) without introducing a cycle in the causal graph.

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## Author notes

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