Jacob Stegenga

Much of the literature on values in science is framed around the idea of a shift in the status of the so-called value-free ideal (VFI) for science: having been widely accepted in the past it has come to be thoroughly rejected. In turn, with the rejection of the VFI nearly status quo, a widely asserted view is that there is now a new problem for philosophy of science to address, namely, to distinguish appropriate value-influence on science from inappropriate value-influence. While we agree that understanding the many intricate ways that values can influence science is an important task for philosophy, we argue that this framing—that there is a single phenomenon to be understood, or a single problem to be solved—is misleading, and liable to distort inquiry. This framing creates a chimera.

A central aim of medical research is causal inference. Does this drug have harmful side effects? Is this medical intervention effective? Does this chemical cause cancer? To provide evidence that bears on these important questions, many sorts of measurements are made in a variety of types of studies. These measurements generate a plethora of data, and these data must be quantitatively summarized so they are rendered relevant to causal hypotheses. That is, to render measurements made in medical research into evidence for a causal hypothesis, those measurements must be transformed into summary quantifications, called "outcome measures." This chapter has two aims. First, we argue for the superiority of one form of outcome measure, called absolute measures. Second, we argue against a widely held myth in epidemiology. The myth is that in observational methods, such as case-control studies, only the relative outcome measure called the odds ratio can be calculated, and we argue that there is no justification for this myth.

I defend a radical interpretation of biological populations—what I call population pluralism—which holds that there are many ways that a particular grouping of individuals can be related such that the grouping satisfies the conditions necessary for those individuals to evolve together. More constraining accounts of biological populations face empirical counter-examples and conceptual difficulties. One of the most intuitive and frequently employed conditions, causal connectivity—itself beset with numerous difficulties—is best construed by considering the relevant causal relations as ‘thick’ causal concepts. I argue that the fine-grained causal relations that could constitute membership in a biological population are huge in number and many are manifested by degree, and thus we can construe population membership as being defined by massively multidimensional constructs, the differences between which are largely arbitrary. I end by showing that positions in two recent debates in theoretical biology depend on a view of biological populations at odds with the pluralism defended here. 1 Introduction2 Biological Population, Broad and Narrow3 Difficulties with Narrow Biological Population Conditions3.1 Against the genealogical condition3.2 Against the conspecificity condition3.3 Against the proximity condition3.4 Against the typology condition4 Causal Connectivity5 Massively Multidimensional Population Constructs6 Population Uniqueness and Natural Selection6.1 Statisticalism and its discontents6.2 Price at what price?7 Conclusion

How reliable are causal inferences in complex empirical scenarios? For example, a physician prescribes a drug to a patient, and then the patient undergoes various changes to their symptoms. They then increase their confidence that it is the drug that causes such changes. Are such inferences reliable guides to the causal relation in question, particularly when the physician can gain a large volume of such clinical experience by treating many patients? The evidence-based medicine movement says no, while some physicians and philosophers support such appeals to first-person experience. We develop a formal model and simulate causal inference based on clinical experience. We conclude that in very particular clinical scenarios such inferences can be reliable, while in many other routine clinical scenarios such inferences are not reliable.

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions.

We take up the challenge of developing an international network with capacity to survey the world’s scientists on an ongoing basis, providing rich datasets regarding the opinions of scientists and scientific sub-communities, both at a time and also over time. The novel methodology employed sees local coordinators, at each institution in the network, sending survey invitation emails internally to scientists at their home institution. The emails link to a ‘10 second survey’, where the participant is presented with a single statement to consider, and a standard five-point Likert scale. In June 2023, a group of 30 philosophers and social scientists invited 20,085 scientists across 30 institutions in 12 countries to participate, gathering 6,807 responses to the statement Science has put it beyond reasonable doubt that COVID-19 is caused by a virus. The study demonstrates that it is possible to establish a global network to quickly ascertain scientific opinion on a large international scale, with high response rate, low opt-out rate, and in a way that allows for significant (perhaps indefinite) repeatability. Measuring scientific opinion in this new way would be a valuable complement to currently available approaches, potentially informing policy decisions and public understanding across diverse fields.

A stereotype is a belief or claim that a group of people has a particular feature. Stereotypes are expressed by sentences that have the form of generic statements, like “Canadians are nice.” Recent work on generics lends new life to understanding generics as statements involving probabilities. I argue that generics (and thus sentences expressing stereotypes) can take one of several forms involving conditional probabilities, and these probabilities have what I call a naturalness requirement. This is the natural probability theory of stereotypes. Each of the two components of the theory entails a family of fallacies that contributes to the spurious reinforcement of stereotypes: inferential slippage within and between the different generic forms, and inferential slippage from facts about frequencies of group traits to beliefs about natural propensities or dispositions of groups. Empirical research suggests that we often commit these fallacies. Moreover, this theory can referee a vitriolic debate between some psychologists, who hold that stereotypes are always false and stereotyping is always wrong, and other psychologists, who hold that stereotypes are often accurate and stereotyping is often reasonable.

If scientists violate principles and practices of routine science to quickly develop interventions against catastrophic threats, they are engaged in what I call fast science. The magnitude, imminence, and plausibility of a threat justify engaging in and acting on fast science. Yet, that justification is incomplete. I defend two principles to assess fast science, which say: fast science should satisfy as much as possible the reliability-enhancing features of routine science, and the fast science developing an intervention against a threat should not depend on the same problematic assumptions as the fast science which estimates the magnitude, imminence, and plausibility of the threat.

Havstad (2022) argues that the argument from inductive risk for the claim that non-epistemic values have a legitimate role to play in the internal stages of science is deductively valid. She also defends its premises and thus soundness. This is, as far as we are aware, the best reconstruction of the argument from inductive risk in the existing literature. However, there is a small flaw in this reconstruction of the argument from inductive risk which appears to render the argument invalid. This flaw is superficial, and a small amendment to it rescues the claim of validity.

Drug regulation is fraught with inductive risk. Regulators must make a prediction about whether or not an experimental pharmaceutical will be effective and relatively safe when used by typical patients, and such predictions are based on a complex, indeterminate, and incomplete evidential basis. Such inductive risk has important practical consequences. If regulators reject an experimental drug when it in fact has a favourable benefit/harm profile, then a valuable intervention is denied to the public and a company’s material interests are needlessly thwarted. Conversely, if regulators approve an experimental drug when it in fact has an unfavourable benefit/harm profile, then resources are wasted, people are needlessly harmed, and other potentially more effective treatments are underutilized. Given that such regulatory decisions have these practical consequences, non-epistemic values about the relative importance of these consequences impact the way such regulatory decisions are made (similar to the analysis of laboratory studies on the toxic effects of dioxins presented in Douglas [2000]). To balance the competing demands of the pertinent non-epistemic values, regulators must perform what I call an “inductive risk calculus.” At least in the American context this inductive risk calculus is not well-managed.

The value-free ideal in science has been criticised as both unattainable and undesirable. We argue that it can be defended as a practical principle guiding scientific research even if the unattainability and undesirability of a value-free end-state are granted. If a goal is unattainable, then one can separate the desirability of accomplishing the goal from the desirability of pursuing it. We articulate a novel value-free ideal, which holds that scientists should act as if science should be value-free, and we argue that even if a purely value-free science is undesirable, this value-free ideal is desirable to pursue.

The value-free ideal for science holds that values should not influence the core features of scientific reasoning. We defend the difference-to-inference model of value-permeation, which holds that value-permeation in science is problematic when values make a difference to the inferences made about a hypothesis. This view of value-permeation is superior to existing views, and it suggests a corresponding maxim—namely, that scientists should strive to eliminate differences to inference. This maxim is the basis of a novel value-free ideal for science.

Philosophers have committed sins while studying science, it is said – philosophy of science focused on physics to the detriment of biology, reconstructed idealizations of scientific episodes rather than attending to historical details, and focused on theories and concepts to the detriment of experiments. Recent generations of philosophers of science have tried to atone for these sins, and by the 1980s the exculpation was in full swing. Marcel Weber’s Philosophy of Experimental Biology is a zenith mea culpa for philosophy of science: it carefully describes several historical examples from twentieth century biology to address both ‘old’ philosophical topics, like reductionism, inference, and realism, and ‘new’ topics, like discovery, models, and norms. Biology, experiments, history – at last, philosophy of science, free of sin.

The standard view about sex differences in sexual desire is that males are lusty and loose, while females are cool and coy. This is widely believed and is a core premise of some scientific programs like evolutionary psychology. But is it true? A mountain of evidence seems to support the standard view. Yet, this evidence is shot through with methodological and philosophical problems. Developments in the study of sexual desire suggest that some of these problems can be resolved, and when they are, the standard view looks, at best, to be an exaggeration.

It is a plausible speculation that conventional choices in outcome measures might influence the results of meta-analyses. We test that speculation by simulating data from trials on antidepressants. We vary real drug effectiveness while modulating conventional values for outcome measures. We had previously shown that one conventional choice used in meta-analyses of antidepressants falls in a narrow range of values that maximize estimates of effectiveness. Our present analysis investigates why this phenomenon occurs. Moreover, our results suggest the superiority of absolute outcome measures over relative measures. This research program can be extended to test numerous other aspects of clinical research.

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence.

Medicalisation is a social phenomenon in which conditions that were once under legal, religious, personal or other jurisdictions are brought into the domain of medical authority. Low sexual desire in females has been medicalised, pathologised as a disease, and intervened upon with a range of pharmaceuticals. There are two polarised positions on the medicalisation of low female sexual desire: I call these the mainstream view and the critical view. I assess the central arguments for both positions. Dividing the two positions are opposing models of the aetiology of low female sexual desire. I conclude by suggesting that the balance of arguments supports a modest defence of the critical view regarding the medicalisation of low female sexual desire.

We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory. We defend a nuanced position on p-hacking: p-hacking is sometimes, but not always, epistemically pernicious. Our argument requires a novel understanding of Bayesianism, since a standard criticism of Bayesian confirmation theory is that it cannot represent the influence of biased methods. We then turn to pre-analysis plans, a methodological device used to mitigate p-hacking. Some say that pre-analysis plans are epistemically meritorious while others deny this, and in practice pre-analysis plans are often violated. We resolve this debate with a modest defence of pre-analysis plans. Further, we argue that pre-analysis plans can be epistemically relevant even if the plan is not strictly followed—and suggest that allowing for flexible pre-analysis plans may be the best available policy option.