Scientific Realism in the Wild: An Empirical Study of Seven Sciences and HPS James R. Beebe (University at Buffalo) and Finnur Dellsén (University of Iceland & Inland Norway University of Applied Sciences)* Abstract: We report the results of a study that investigated the views of researchers working in seven scientific disciplines (physics, chemistry, biology, economics, psychology, sociology, and anthropology) and in HPS (N = 1,798) in regard to four hypothesized dimensions of scientific realism. Among other things, we found (i) that natural scientists tended to express more strongly realist views than social scientists, (ii) that social scientists working in fields where quantitative methods predominate tended to express more strongly realist views than social scientists working in fields where qualitative methods are more common, (iii) that HPS scholars tended to express more anti-realist views than natural scientists, aligning themselves with social scientists working in fields where qualitative methods are more common, (iv) that van Fraassen's characterization of scientific realism failed to cluster with more standard characterizations, (v) that a van Fraassen-style anti-realism is significantly more popular among scientists and HPS scholars than more standard forms of anti-realism, and (vi) that while those who endorsed the No-Miracles Argument were more likely to endorse scientific realism, those who endorsed the Pessimistic Induction were no more or less likely to endorse anti-realism. Keywords: scientific realism, constructive empiricism, experimental philosophy, history and philosophy of science * We would like to thank audience members at the 2018 Buffalo Annual Experimental Philosophy Conference and the 2018 meeting of the Central States Philosophical Association, Huginn Thorsteinsson, and three anonymous reviewers from Philosophy of Science for helpful feedback on this project. All data and materials from this project are available at https://osf.io/cevsr. SCIENTIFIC REALISM IN THE WILD 2 I. Background Some of the most central and long-lasting controversies in philosophy of science have concerned various forms of 'scientific realism'. Historically, one important set of issues characterized in these terms concerns the metaphysical status of unobservable entities and the semantic status of theoretical terms. In the late nineteenth and early twentieth century, a strand of positivistic idealism denied that unobservable entities could exist independently of our minds (e.g., Pearson 1911). Similarly, in the heyday of logical positivism, it was debated whether scientific theories should be interpreted literally as being about mind-independent parts of the world. The logical positivists denied this, arguing instead that theories should be interpreted as collections of actual or possible observations associated with the theory (e.g., Schlick 1936). In analytic philosophy of science, these debates are now largely considered settled in favor of the realist view that unobservable entities do exist mind-independently and scientific theories are straightforwardly true or false depending on the state of the world (including its unobservable aspects).1 In the current paper, we refer to this as Metaphysical Realism (MR). Arguably, the central focus of recent debates under the heading of 'scientific realism' has concerned the epistemic status of a privileged set of scientific theories or theory-parts. In this debate, scientific realists are roughly those who claim that empirically successful scientific theories, including what these theories say about unobservable entities, are typically at least approximately true. This statement of a type of Epistemic Realism (ER) is often qualified in various ways, e.g. as extending only to certain parts of successful scientific theories, such as what these theories say about structures (Worrall 1989; 1994), causes (Hacking 1983; Cartwright 1983), or some combination of both (Chakravartty 2007; Egg 2016). In support of ER, many realists appeal 1 We are aware of only two recent exceptions to this, viz. Rowbottom 2011 and Frost-Arnold 2014. SCIENTIFIC REALISM IN THE WILD 3 to some version of the No-Miracles Argument (NMA) according to which the approximate truth of current best theories can be inferred on the basis of its being the best explanation of their empirical success (Smart 1963; Putnam 1975; Musgrave 1988; Psillos 1999). Against ER, many anti-realists appeal to some version of the Pessimistic Induction (PI) according to which current best theories will probably suffer the same fate as those numerous historical theories that were successful at the time but have now been discarded (Poincaré 1952/1905; Hesse 1975; Laudan 1981a; Stanford 2006). While ER is arguably the most debated dimension of scientific realism, there are other prominent debates in philosophy of science have also been characterized as concerning 'scientific realism'. In The Scientific Image (1980), van Fraassen rejected ER as a legitimate definition of 'scientific realism', arguing that such a definition should not refer to any specific set of scientific theories such as those that are currently 'best' or 'most successful' (1980: 7; see also 2017: 95-7). Instead, van Fraassen suggested that scientific realism amounts to the conjunction of two theses about acceptance and the aim of science, which we will refer to as van Fraassen Realism (vFR): Science aims to give us, in its theories, a literally true story of what the world is like; and acceptance of a scientific theory involves the belief that it is true (1980: 8). Van Fraassen's anti-realist alternative, constructive empiricism, denies both theses – roughly by replacing 'true' with 'empirically adequate':2 Science aims to give us theories which are empirically adequate; and acceptance of a theory involves as belief only that it is empirically adequate (1980: 12). It is important to understand what exactly van Fraassen meant by characterizing realism and antirealism in terms of 'acceptance' and what science 'aims to give us'. In speaking of the 'aim' of 2 Roughly, a theory is empirically adequate in van Fraassen's sense just in case it is correct in what it says about observable aspects of the world. SCIENTIFIC REALISM IN THE WILD 4 science, van Fraassen is referring to the basic criterion of success for scientific theories, i.e. the fundamental standards against which scientific theories should be judged (1980: 8; see also 2017: 98). The realist claims that scientific theories should be true, while the constructive empiricist insists that empirically adequate theories are just as good as far as science is concerned. By 'acceptance', van Fraassen is referring to whatever attitude scientists in fact have toward the theories they use in their scientific endeavors, e.g. in their explanations, predictions, and further theorizing (van Fraassen 1980: 12 & 151-2; see also Rosen 1994: 145). Thus the issue of realism is partly about whether using theories for these purposes involves believing that those theories are true or merely that they are empirically adequate. Importantly, van Fraassen is adamant that constructive empiricism makes no claim whatsoever about what individual scientists do or should believe; rather, his concern is only with what kind of belief (if any) is involved in the scientific enterprise as such (Forrest 1994; Rosen 1994; van Fraassen 1994). A final cluster of issues debated under the heading of 'scientific realism' concerns how science makes progress. In large part due to Bird's (2007) defense of an epistemic view of scientific progress, the nature of progress in science has again become prominent in philosophy of science after lying dormant for a few decades. In this debate, scientific realism is frequently characterized as the idea that scientific progress is or involves getting closer to the truth, including the underlying truth about unobservable entities. Thus what we will call Progressive Realism (PR) is the thesis that an episode in science is progressive when scientists have a more accurate account of the world, including its unobservable aspects, at the end of the episode than at its beginning. This thesis is denied by anti-realist accounts of scientific progress, such as Kuhn's (1970) and Laudan's (1977, 1981b) problem-solving accounts and Rowbottom's (forthcoming) SCIENTIFIC REALISM IN THE WILD 5 instrumentalist account, and affirmed in different ways by various self-professed realist accounts (e.g. Bird 2007; Niiniluoto 2014, 2017; Dellsén 2016). Although each of the aforementioned strands of scientific realism has been discussed and debated almost ad nauseam, there has been nearly no empirical investigation into how scientists themselves view these issues. This is surprising since working scientists are arguably in a better epistemic position with regard to a number of issues in these debates. For example, working scientists would seem to be in the best position to evaluate the epistemic merits of theories they currently accept, since they have access to, and awareness of, a greater variety of evidence than others who might want to evaluate these theories (including philosophers of science). Similarly, scientists themselves are surely in a better position than philosophers to tell whether their acceptance of a particular theory involves believing that the theory is true (as per vFR). Although scientists are by no means infallible judges of these matters, their opinions should at least be taken seriously enough to have an impact on these debates. The central aim of our study was to investigate the attitudes of working scientists towards each strand of scientific realism: are scientists themselves scientific realists – and if so, in what sense? Our aim was also to investigate the relationships between affirmations of different strands of realism, in order to gauge the extent to which different realism debates really are distinct in the minds of scientists themselves. Furthermore, we sought to compare the attitudes of working scientists in different fields – physics, chemistry, biology, economics, psychology, sociology, and anthropology – as well as to compare the attitudes of working scientists with the attitudes of those who study aspects of science that are of central relevance to scientific realism, viz. scholars in history and philosophy of science (HPS). Finally, we wanted to understand what other philosophical views about science, such as about the theory-ladenness of observation and the SCIENTIFIC REALISM IN THE WILD 6 ability of science to explain everything eventually, were associated with different strands of scientific realism within each field. II. Materials, Procedure, and Participants In order to probe the judgments of scientists and HPS scholars about each of the dimensions of scientific realism described above, we constructed the Scientific Realism Questionnaire (SRQ), the elements of which appear in Table 1.3 Participants were asked to read each statement and indicate the extent to which they agreed or disagreed with it by selecting one of the following answer choices: Completely Disagree, Mostly Disagree, Slightly Disagree, Neither Agree nor Disagree, Slightly Agree, Mostly Agree, Completely Agree. 1. The objects and phenomena studied by science exist independently of how we conceive of or think about them. (MR) 2. Even if we cannot be certain which scientific theories are ultimately true, we can be certain that there is an ultimate truth out there waiting to be discovered. (MR) 3. Our most successful and rigorously tested scientific theories are at least approximately true. (ER) 4. Progress in science is a matter of getting closer and closer to the underlying truth about reality. (PR) 5. In order to go about their daily business as scientists, scientists do not need to believe that any of the theories they rely upon provides them with literally correct descriptions of the world.* (vFR) 6. Scientific theories should not be judged on the basis of how accurately they depict an underlying reality but rather on how useful they are in helping us systematize our experiences and develop new technologies.* (vFR) 7. The best explanation for the remarkable success of our best scientific theories is that they accurately depict an underlying reality. (NMA) 8. The fact that the history of science contains so many widely accepted theories that turned out to be fundamentally mistaken should make us skeptical about the scientific theories that are currently accepted. (PI) 9. Disagreements in science can always be resolved by obtaining better data. (Conv.) 10. Over time, scientists from competing schools of thought will eventually converge to a single perspective. (Conv.) 3 Our questionnaire included two additional statements about philosophical issues which we later came to view as orthogonal to other issues in our study. In the interest of space, we do not discuss these statements here. SCIENTIFIC REALISM IN THE WILD 7 11. There is no such thing as purely impartial scientific observation; all observations in science are shaped and informed by one's prior theoretical commitments. (TL) 12. Given enough time and resources, there is nothing science cannot explain. (Sci.) 13. Having different schools of thought within a scientific discipline is good for the health of that discipline. (Dis.) 14. There is a significant amount of persistent disagreement in my field [in science].4 (Dis.) Table 1. Questionnaire items. Most statements on the SRQ were formulated as statements of (some version of) scientific realism or some view that realists would probably be more inclined to endorse. We endeavored to make the statements simple and easy for scientists to understand, and thus avoided semi-technical terms such as 'unobservable', 'empirical adequacy', 'acceptance', 'aim of science', and their cognates.5 For purposes of analysis, a response of 'Completely Disagree' to one of these statements was scored as 1, 'Mostly Disagree' as 2, and so on. Responses to statements marked with an '*' were reverse-scored (i.e., 'Strongly Agree' was scored as 1, etc.). Each of the items in the SRQ makes a statement about science as a whole rather than about any particular scientific discipline. It should be noted that NMA and PI are standardly formulated as arguments about science as a whole rather than about particular sciences or scientific theories. Nevertheless, in a follow-up study, we will develop a questionnaire that asks participants separately about natural and social science. The first six statements on the SRQ concerned the central dimensions of scientific realism. Statements (1) and (2) are formulations of Metaphysical Realism (MR). Item (3) is a statement of 4 Scientists received a version of statement (14) that read 'There is a significant amount of persistent disagreement in my field.' HPS scholars received a version that read 'There is a significant amount of persistent disagreement in science.' 5 We found it most challenging to formulate vFR without the use of semi-technical terms like 'acceptance' and 'aim of science'. Guided by van Fraassen's own later writings (e.g., 1985, 1994, 2001, 2007), and some of the associated literature (e.g., Rosen 1994; Teller 2001; Dicken 2010; Dellsén 2017), we settled on characterizing acceptance of a theory in terms of scientists "going about their daily business" with respect to the theory, and the aim of science in terms of what scientific theories should "be judged on the basis of". SCIENTIFIC REALISM IN THE WILD 8 Epistemic Realism (ER). Statement (4) is an expression of Progressive Realism (PR). And statements (5) and (6) reject scientific realism as characterized by van Fraassen (vFR). The remaining eight statements on the SRQ concerned additional features of scientific practice the responses to which we thought might shed light on responses to the first six. Statements (7) and (8) represent two of the most prominent arguments for and against Epistemic Realism in the philosophical literature-viz., NMA and PI. We hypothesized that participants' responses to these statements would correlate (positively and negatively, respectively) with their responses to (3). Statements (9) and (10) express the expectation that scientific disagreements are never ultimately fundamental, can always in principle be overcome, and can be replaced by convergence of opinion when sufficient data are available (Conv.). While these statements are not expressions of MR, ER, or PR, we expected them to positively correlate with participants' responses to (1), (2), (3), and (4). We also hypothesized that a strong endorsement of the theory-ladenness (TL) of observation (statement 11), positive views about expert disagreement (statement 13), and perceptions of disagreement within science (statement 14) would negatively predict participants' realism scores, and that agreement with the view that science can explain everything (statement 12) would positively predict them. We recruited 1,798 scholars working in seven scientific disciplines (physics, chemistry, biology, economics, psychology, sociology, and anthropology) and in HPS,6 and asked them to respond to the fourteen statements on the SRQ. Participants were recruited between November, 6 Among HPS scholars, 78% indicated that they were primarily philosophers of science, 31% of whom indicated they worked in something like an education department rather than a philosophy department. 7% of HPS scholars indicated they were historians of science. The remaining 15% of this participant group indicated that they worked in the history and philosophy of science or science and technology studies, thus leaving it unclear whether they were primarily philosophers, historians, or something else. Only 23% of those in the latter category indicated they worked in STS. SCIENTIFIC REALISM IN THE WILD 9 2016, and July, 2017, via professional listservs and direct email invitations (email addresses were collected by hand from university departmental websites). The SRQ was hosted online at vovici.com. Test items were presented in counterbalanced order. Participants were asked to indicate the scientific discipline in which they worked and to provide basic demographic information about their age, gender, ethnicity, native language, and highest degree earned. Participant demographics are represented in Table 2. n Ave. age Female Highest degree Native English Bachelors Masters Ph.D. Phy 317 51 17% 3% 6% 92% 74% Chem 182 45 26% 3% 24% 74% 65% Bio 219 40 56% 20% 20% 60% 84% Econ 249 51 24% 2% 5% 94% 81% Psy 189 41 36% 4% 12% 84% 68% Socio 198 51 46% 1% 11% 88% 84% Anthro 264 46 67% 5% 25% 71% 83% HPS 170 48 23% 3% 20% 78% 62% Table 2. Participant demographics. III. Results: Realism Scores The mean responses from each discipline to each item on the SRQ appear in Table 3. Standard deviations appear in parentheses. SCIENTIFIC REALISM IN THE WILD 10 Item Phy Chem Bio Econ Psy Soc Anthro HPS 1 (MR) 6.0 (1.5) 5.6 (1.7) 5.4 (1.7) 5.4 (1.6) 4.7 (1.9) 4.5 (1.9) 4.2 (2.0) 5.0 (1.9) 2 (MR) 5.0 (1.9) 5.3 (1.9) 5.3 (1.7) 5.3 (1.6) 4.9 (1.7) 4.6 (1.9) 3.6 (1.9) 4.1 (2.0) 3 (ER) 6.0 (1.3) 5.7 (1.3) 5.7 (1.2) 5.7 (1.0) 5.2 (1.3) 5.4 (1.2) 5.1 (1.4) 5.0 (1.5) 4 (PR) 5.5 (1.6) 5.7 (1.4) 5.8 (1.2) 5.6 (1.3) 5.3 (1.4) 5.1 (1.5) 4.0 (1.9) 4.2 (1.9) 5 (vFR) 3.0 (1.9) 3.2 (1.8) 3.6 (1.9) 2.6 (1.7) 3.0 (1.7) 3.1 (1.8) 3.1 (1.7) 2.8 (1.7) 6 (vFR) 3.9 (2.0) 4.0 (1.8) 4.4 (1.7) 3.7 (1.7) 3.8 (1.6) 4.0 (1.8) 3.7 (1.8) 3.7 (1.9) 7 (NMA) 5.7 (1.4) 5.7 (1.4) 5.7 (1.1) 5.5 (1.2) 4.9 (1.5) 5.2 (1.3) 4.6 (1.8) 4.6 (1.7) 8 (PI) 3.9 (2.0) 4.4 (1.9) 4.5 (1.8) 4.6 (1.7) 4.9 (1.7) 4.5 (1.8) 5.0 (1.7) 4.6 (1.8) 9 (Conv.) 4.6 (1.9) 4.7 (1.8) 4.3 (1.7) 3.8 (1.8) 4.0 (1.8) 3.7 (1.8) 3.3 (1.8) 3.1 (1.7) 10 (Conv.) 5.0 (1.5) 4.7 (1.6) 4.5 (1.6) 3.9 (1.6) 3.9 (1.7) 3.2 (1.5) 3.1 (1.6) 3.8 (1.7) 11 (TL) 4.6 (1.9) 5.0 (1.7) 5.2 (1.5) 4.9 (1.6) 5.4 (1.6) 5.4 (1.5) 6.0 (1.3) 5.5 (1.5) 12 (Sci.) 3.2 (2.0) 3.9 (2.1) 3.9 (2.2) 3.0 (1.7) 3.4 (1.9) 2.8 (1.8) 2.8 (1.8) 2.8 (1.9) 13 (Dis.) 6.0 (1.0) 6.0 (1.0) 6.1 (0.9) 5.6 (1.2) 6.1 (0.9) 6.0 (1.1) 6.3 (0.8) 5.9 (1.0) 14 (Dis.) 4.3 (1.7) 4.2 (1.7) 4.8 (1.6) 5.6 (1.4) 5.5 (1.3) 5.6 (1.4) 5.7 (1.2) 5.0 (1.4) Table 3. Mean responses (and standard deviations) to each statement on the SRQ, organized by participant discipline. We will begin by discussing statistical analyses of participants' responses to statements (1) through (6) because these represent measures of what are taken to be the central dimensions of scientific realism. Since participant responses to statements (5) and (6) were reverse-scored, a higher mean response to statements (1) through (6) indicates a more strongly realist response. A series of Kruskal-Wallis tests revealed significant between-discipline differences (all p's < .001) in participants' responses to all statements.7 A number of theoretically significant facts are immediately apparent from the first six rows of data in Table 3. The most strongly realist responses are found among natural scientists, and the 7 We used nonparametric Kruskal-Wallis tests rather than a MANOVA and a series of one-way ANOVAs because Box's test for homogeneity of covariance matrices was significant (p < .001), and Shapiro-Wilk tests for normality were significant (all p's < .001) for all questionnaire statements. However, Levene's tests for homogeneity of variance were significant (p's < .05) for all questionnaire statements except (9) (p = .55). All statistical tests reported in this paper are two-tailed. SCIENTIFIC REALISM IN THE WILD 11 least realist responses tend to be found among anthropologists and HPS scholars. Responses are also more strongly realist among natural scientists than among social scientists. Some statements- e.g., (3), "Our most successful and rigorously tested scientific theories are at least approximately true"-garnered rather strongly realist responses across the board, while other statements-e.g., (5), "In order to go about their daily business as scientists, scientists do not need to believe that any of the theories they rely upon provides them with literally correct descriptions of the world"- garnered antirealist responses across the board.8 We decided in advance of collecting and analyzing data that the various disciplines in our study would be arranged in all tables and figures according to traditional conceptions of what are the 'hardest' and 'softest' sciences. We do not endorse these traditional characterizations, but it is noteworthy that on almost all analyses we report, there is a significant downward trend in the degree of realism expressed as one moves from 'hardest' to 'softest' science. Kendall's tau correlations between participants' responses to the first six statements on the SRQ are represented in Table 4. (1) (MR) (2) (MR) (3) (ER) (4) (PR) (5) (vFR) (6) (vFR) (1) (MR) 1 (2) (MR) .38*** 1 (3) (ER) .25*** .27*** 1 (4) (PR) .33*** .48*** .37*** 1 (5) (vFR) .03 .09*** .04 .11*** 1 (6) (vFR) .13*** .17*** .12*** .24*** .26*** 1 Table 4. Kendall's tau correlation matrix for the first six statements on the SRQ. In all tables, correlations marked with an '*' are significant at the .05 level, those marked with an '**' are significant at the .01 level, and those marked with '***' are significant at the .001 level. 8 Overall means: (3) = 5.5, (5) = 3.1. SCIENTIFIC REALISM IN THE WILD 12 As expected, participant responses to the two statements of MR positively correlated with each other fairly well. The two statements of vFR correlated moderately well together but not as strongly as we anticipated. Statement (4), "Progress in science is a matter of getting closer and closer to the underlying truth about reality", correlated fairly strongly with statements of MR and ER. In order to examine broader patterns in our data and the ways in which participants' responses to the first six items on the SRQ clustered together, we conducted an exploratory factor analysis.9 The analysis resulted in the two factors represented in the columns of Table 5. SRQ Item Factor 1 Factor 2 (1) (MR) .57 (2) (MR) .73 (3) (ER) .51 (4) (PR) .75 (5) (vFR) .53 (6) (vFR) .59 Table 5. Pattern matrix for an exploratory factor analysis of data from statements (1) through (6) of the SRQ. Factor loadings below .3 are suppressed. The factor loadings represented in Table 5 (i.e., the values in the two rightmost columns) correspond (roughly) to the correlations between participants' responses to each statement and the underlying factor in question. Factor 1 explained 31.7% of the variance in the data, while Factor 2 explained 8.3%. The two factors correlated to a moderate degree (r = .35). Thus, our factor analysis reveals that the first four statements on the SRQ seem to be getting at the same underlying 9 We performed a principal axis factoring analysis with oblique (direct oblimin) rotation. A Kaiser-Meyer-Olkin Measure of sampling adequacy, which analyzes a dataset for how suitable it is for factor analysis, yielded a result of .73, which according to Hutcheson and Sofroniou (1999) is 'middling.' Bartlett's test of sphericity was significant (df = 15, 2 = 1964.19, p < .001). Kaiser's (1960) eigenvalue greater than 1.0 rule, Cattell's (1966) scree test, and a parallel analysis (O'Connor 2000) all indicated that two factors should be extracted. SCIENTIFIC REALISM IN THE WILD 13 phenomenon-at least when examined at a certain level of granularity-but that this phenomenon is distinctively different from the one being measured by statements (5) and (6). On the basis of the foregoing correlational and factor analyses and the philosophical theorizing upon which the SRQ was based, we calculated separate scores for each of the dimensions of scientific realism that we studied for each scientist who participated in our study. MR scores were obtained by averaging participants' responses to statement (1), "The objects and phenomena studied by science exist independently of how we conceive of or think about them", and statement (2), "The objects and phenomena studied by science exist independently of how we conceive of or think about them". Participants' ER and PR scores were simply their responses to statement (3), "Our most successful and rigorously tested scientific theories are at least approximately true", and statement (4), "Progress in science is a matter of getting closer and closer to the underlying truth about reality", respectively. VFR scores were calculated by averaging responses to statement (5), "In order to go about their daily business as scientists, scientists do not need to believe that any of the theories they rely upon provides them with literally correct descriptions of the world", and statement (6), "Scientific theories should not be judged on the basis of how accurately they depict an underlying reality but rather on how useful they are in helping us systematize our experiences and develop new technologies". Because participants' responses to statements (1) through (4) clustered together (i.e., loaded onto a single factor), and because these statements represent the core dimensions of scientific realism on a standard way of thinking about the matter, we also calculated a 'Standard Scientific Realism score' (SSR) for each participant by averaging together participants' responses to these statements. Mean MR, ER, PR, vFR, and SSR scores and 95% confidence intervals for each discipline appear in Figures 1 through 5 below. A SCIENTIFIC REALISM IN THE WILD 14 series of Kruskal-Wallis tests revealed significant between-discipline differences on each score.10 One sample Wilcoxon signed rank tests were conducted on each score for each discipline to determine whether the observed distribution differed significantly from a hypothetical distribution centered around the neutral midpoint of 4. In all figures, distributions that differed from the midpoint to a small extent are marked with a '†', distributions that differed to a medium extent are marked with '††', and distributions that differed to a large extent ) are marked with '†††.' Distributions whose measure of effect size fell below the conventional threshold for a small effect received no mark.11 10 MR: H(7) = 214.23, p < .001. ER: H(7) = 139.62, p < .001. PR: H(7) = 226.04, p < .001. VFR: H(7) = 47.28, p < .001. SSR: H(7) = 260.20, p < .001. 11 To measure these effect sizes, r values were calculated by dividing standardized test statistics by the square root of the sample size. For each one-sample test, the sample size used in the calculation was the sum of the actual sample size and an equally large hypothetical sample. Although best known as measures of correlation strength, r values can also be used as measures of effect size in other contexts. SCIENTIFIC REALISM IN THE WILD 15 Figure 1. Mean MR scores, sorted by discipline. SCIENTIFIC REALISM IN THE WILD 16 Figure 2. Mean ER scores, sorted by discipline. SCIENTIFIC REALISM IN THE WILD 17 Figure 3. Mean PR scores, sorted by discipline. SCIENTIFIC REALISM IN THE WILD 18 Figure 4. Mean vFR scores, sorted by discipline. SCIENTIFIC REALISM IN THE WILD 19 Figure 5. Mean SSR scores, sorted by discipline. With the exception of Figure 4, we see common patterns in each of these figures. There are higher realism scores among natural scientists than social scientists; the views of HPS scholars strongly diverge from those of natural scientists; and the views of HPS agree most closely with those of anthropologists (and sometimes sociologists). In most figures, means mostly fell significantly above the neutral midpoint. Only in Figure 4 did means generally fall below the midpoint. A series of post hoc REGWQ (Ryan-Einot-Gabriel-Welsch Q) range tests was conducted in order to classify the mean responses of the eight disciplines represented in our study into distinct SCIENTIFIC REALISM IN THE WILD 20 homogeneous subsets. REGWQ tests place mean responses into distinct subsets if they differ significantly from each other (at an  level of .05, controlling for the familywise error rate) and into the same subset otherwise. The results of these tests are represented in Table 6. Homogeneous Subsets MR Score ER Score PR Score vFR Score SSR Score Subset 1 Phy Chem Bio Econ Phy Chem Bio Econ Bio Chem Econ Phy Bio Chem Socio Phy Chem Bio Econ Subset 2 Psy Socio HPS Chem Bio Econ Socio Chem Econ Phy Psy Chem Socio Phy Psy Anthro HPS Psy Socio Subset 3 Anthro Socio Psy Anthro HPS Phy Psy Socio Phy Psy Anthro HPS Econ HPS Subset 4 HPS Anthro Anthro Table 6. Homogeneous subsets on each score, with disciplines listed in descending order of mean score. In the second column of Table 6, we can see that the natural sciences and economics cluster into a subset at the top of the table with the highest MR scores, that psychology, sociology, and HPS cluster into a second subset with the next highest set of scores, and that anthropology appears by itself at the bottom with the lowest SSR score. With the exception of vFR scores, something like this pattern is repeated in each of the other columns.12 12 Because physics includes some rather speculative theories and because of the heavy use of idealizations in economics, we had hypothesized that scientists from these two disciplines would score lower than other scientists on SCIENTIFIC REALISM IN THE WILD 21 As we noted above, we hypothesized that the realism scores of natural scientists would be higher than those of social scientists and that the realism scores of social scientists working in disciplines that rely primarily upon quantitative methods would be higher than those of scientists in disciplines where qualitative methods are common. In order to test this hypothesis more clearly, we sorted scientists' SSR scores into three groups. Physics, chemistry, and biology were combined into natural science; economics and psychology were combined into social science where quantitative methods predominate; and sociology and anthropology were combined into social science where qualitative methods are common. The means scores of these three groups, together with those of HPS scholars, are represented in Figure 6. the vFR dimension. Our data confirmed this expectation. However, we did not anticipate the strongly realist responses of sociologists and the strongly antirealist responses of psychologists to the vFR statements. SCIENTIFIC REALISM IN THE WILD 22 Figure 6. Mean SSR scores, sorted by broad disciplinary category. A Kruskal-Wallis test revealed a significant between-group differences on this score.13 Post hoc Mann-Whitney tests indicated significant pairwise differences between each pair of participants group (p's < .001), except for HPS and the qualitative social sciences (p = .52).14 That is, (i) natural scientists had significantly higher SSR scores than social scientists or HPS scholars, (ii) social scientists in disciplines where quantitative methods predominate had significantly higher 13 H(3) = 219.63, p < .001. 14 For all Mann-Whitney tests reported, a Bonferroni correction was used to control for an inflated type I error rate. SCIENTIFIC REALISM IN THE WILD 23 SSR scores than HPS scholars and social scientists where qualitative methods are common, and (iii) social scientists in more qualitative disciplines did not differ significantly from HPS scholars. IV. Results: Additional Factors We now turn our attention to analyses of data from the remaining eight statements on the SRQ. A Kendall's tau correlation matrix for these statements, showing their correlations among themselves and with the first six statements, appears in Table 7. SCIENTIFIC REALISM IN THE WILD 24 (1) (MR) (2) (MR) (3) (ER) (4) (PR) (5) (vFR) (6) (vFR) (7) (NMA) (8) (PI) (9) (Conv.) (10) (Conv.) (11) (TL) (12) (Sci.) (13) (Dis.) (14) (Dis.) (7) (NMA) .31*** .38*** .48*** .47*** .12*** .24*** 1 (8) (PI) -.10*** -.09*** -.16*** -.12*** -.15*** -.08*** -.17*** 1 (9) (Conv.) .22*** .23*** .32*** .35*** .11*** .09*** .29*** -.14*** 1 (10) (Conv.) .26*** .27*** .27*** .38*** .06*** .10*** .29*** -.12*** .33*** 1 (11) (TL) -.20*** -.18*** -.13*** -.20*** -.10*** -.12*** -.18*** .19*** -.22*** -.17*** 1 (12) (Sci.) .12*** .22*** .14*** .25*** .09*** .08*** .16*** -.10*** .31*** .21*** -.11*** 1 (13) (Dis.) -.05** -.05* -.01 -.03 -.06** -.07*** -.06** .10*** -.07*** -.07*** .18*** .01 1 (14) (Dis.) -.08*** -.07*** -.09*** -.09*** -.09*** -.05* -.08*** .16*** -.13*** -.20*** .18*** -.06*** .18*** 1 Table 7. Kendall's tau correlation matrix for the remaining data from the SRQ. SCIENTIFIC REALISM IN THE WILD 25 Participant responses to statement (7), "The best explanation for the remarkable success of our best scientific theories is that they accurately depict an underlying reality" (which expresses NMA), correlated fairly strongly with responses to statements about each of the standard dimensions of scientific realism-i.e., statements (1) through (4). Surprisingly, statement (8), "The fact that the history of science contains so many widely accepted theories that turned out to be fundamentally mistaken should make us skeptical about the scientific theories that are currently accepted" (which represents PI), did not strongly correlate with participant responses to any other statement. Responses to statement (9), "Disagreements in science can always be resolved by obtaining better data", and statement (10), "Over time, scientists from competing schools of thought will eventually converge to a single perspective", correlated moderately well with each other, with responses to the four statements about the standard dimensions of scientific realism, and with NMA, i.e. (7). As expected, responses to statement (11), "There is no such thing as purely impartial scientific observation; all observations in science are shaped and informed by one's prior theoretical commitments", negatively correlated with responses to statements that expressed realist sentiments. They also positively correlated with statement (13), "Having different schools of thought within a scientific discipline is good for the health of that discipline", and statement (14), "There is a significant amount of persistent disagreement in my field [in science]" – although none of these correlations were as large as expected. Also as expected, responses to statement (12), "Given enough time and resources, there is nothing science cannot explain", correlated most strongly with responses to statements of PR and the replacement of disagreement with convergence SCIENTIFIC REALISM IN THE WILD 26 of opinion in light of new evidence. Statements (13) and (14) negatively correlated with expressions of scientific realism, but only to a small or negligible degree. In order to examine how the fourteen statements on the SRQ clustered together, we performed a second exploratory factor analysis, this time using all of the data from the SRQ.15 The analysis resulted in the four factors represented in Table 8. SRQ Item SSR+ TD vFR OFS (1) (MR) .55 (2) (MR) .65 (3) (ER) .51 (4) (PR) .75 (5) (vFR) .61 (6) (vFR) .48 (7) (NMA) .71 (8) (PI) (9) (Conv.) .57 (10) (Conv.) .35 (11) (TL) .41 (12) (Sci.) .47 (13) (Dis.) .39 (14) (Dis.) .52 Variance Explained 24.8% 4.8% 3.8% 2.7% Table 8. Pattern matrix for an exploratory factor analysis of data from statements (1) through (14) of the SRQ. 15 As before, we performed a principal axis factoring analysis with oblique (direct oblimin) rotation. A KMO measure of sampling adequacy was .85 ('meritorious,' according to Hutcheson and Sofroniou 1999). Bartlett's test of sphericity was significant (df = 91, 2 = 5117.78, p < .001). Kaiser's (1960) eigenvalue greater than 1.0 rule and Cattell's (1966) scree test each indicated that four factors should be extracted, but a parallel analysis (O'Connor 2000) indicated that five should be. However, in light of the Kaiser and Cattell recommendations, the fact that the parallel analysis was performed on individual test items, and the fact that there were a small number of test items, we believe that five represents an overextraction. Communalities, a scree plot, and results from the parallel analysis are available at https://osf.io/cevsr. SCIENTIFIC REALISM IN THE WILD 27 Participants' responses to the four standard dimensions of scientific realism-statements (1) through (4)-clustered together with their responses to a statement of NMA (7) and (albeit to a lesser extent) to a statement that competing schools of thought should eventually converge to a single perspective (10). Since this factor includes the previous SSR statements, we refer to it as 'SSR+'. Scientists' mean SSR+ scores are represented in Figure 7 and displayed according to the same broad disciplinary categorization used in Figure 6. A Kruskal-Wallis test revealed significant between-group differences on this score.16 Post hoc Mann-Whitney tests indicated significant pairwise differences between each pair of participants group (p's < .001), except for HPS and the qualitative social sciences (p = .26). 16 H(3) = 275.52, p < .001. SCIENTIFIC REALISM IN THE WILD 28 Figure 7. Mean SSR+ scores, sorted by broad disciplinary category. Statements (11), (13), and (14) formed a second cluster characterized by an embrace of theoretical diversity and the pervasiveness and penetrability of such diversity. The mean Theoretical Diversity (TD) scores of scientists in our study are represented in Figure 8. A KruskalWallis test revealed a significant between-group differences on this score.17 Post hoc MannWhitney tests indicated significant pairwise differences between each pair of participants group (p's < .001), except for HPS and the quantitative social sciences (p = .56). 17 H(3) = 176.17, p < .001. SCIENTIFIC REALISM IN THE WILD 29 Figure 8. Mean TD scores, sorted by broad disciplinary category. Our statement of PI (8) failed to load to an appreciable extent onto any of the four resulting factors-i.e., responses to this statement failed to cluster with responses to any other statement to an appreciable extent. Since factor loadings below .3 were suppressed, the row for this statement in Table 8 is blank. The statements that concerned van Fraassen Realism (vFR), (5) and (6), again failed to cluster with any other statements in the analysis. Statements (9) and (12) clustered together around a factor that seems to involve a kind of optimism about science's eventual ability to explain the world and resolve disagreements. Disciplinary differences on Scientific Optimism (SO) are represented in Figure 9. A KruskalSCIENTIFIC REALISM IN THE WILD 30 Wallis test revealed significant between-group differences on this score.18 Post hoc Mann-Whitney tests indicated significant pairwise differences between each pair of participants group (p's < .001), except for HPS and the qualitative social sciences (p = .24). Figure 9. Mean SO scores, sorted by broad disciplinary category. Correlations between the four factors described above are depicted in Table 9. As might be expected, higher SO scores predicted higher SSR+ scores. Higher TD scores predicted lower SSR+ scores, lower vFR scores, and lower SO scores. 18 H(3) = 125.51, p < .001. SCIENTIFIC REALISM IN THE WILD 31 SSR+ TD VFR SO SSR+ 1 TD -.35 1 VFR .27 -.24 1 SO .45 -.22 .17 1 Table 9. Factor correlation matrix. VI. Discussion We turn now to a discussion of the significance of these results for debates about scientific realism and our understanding of science more broadly. One of the most significant of our findings concerns differences in philosophical opinions between HPS scholars and scientists in the quantitative sciences studied here, which include natural scientists (physicists, chemists, and biologists) and some social scientists (economists and psychologists). In particular, HPS scholars differed significantly from natural scientists on our measures of Metaphysical Realism (MR), Epistemic Realism (ER), Progressive Realism (PR), and in our overall measures of Standard Scientific Realism (SSR and SSR+). Furthermore, HPS scholars also differed significantly from quantitative scientists with regard to Scientific Optimism (SO). By contrast, HPS were significantly in step with the generally more anti-realist qualitative scientists in our study (sociologists and anthropologists) with regard to all of these measures. Since the quantitative sciences are the sciences that HPS scholars have studied most extensively – especially in debates about scientific realism – this almost certainly reflects a real difference in opinion between HPS scholars and their subject matters, viz. scientists themselves. What could explain such a discrepancy? An HPS-friendly explanation is that HPS scholars have, whereas quantitative scientists have not, learned about the significant historical and philosophical SCIENTIFIC REALISM IN THE WILD 32 challenges facing the various types of scientific realism involved in our study. Relatedly, it could be suggested that quantitative scientists have been indoctrinated by an overly realist and optimistic view of science, whereas HPS scholars have not been subject to such propaganda. In favor of this explanation, one could appeal to Kuhn's (1970: 136-143) observation that textbooks in natural science tend to present a selective and distorted view of the history of science as a series of great achievements accumulated over time. Since such textbooks are arguably more central to the training of quantitative scientists than qualitative scientists, this might also explain why the qualitative scientists in our study (sociologists and anthropologists) align themselves more closely with HPS scholars than our quantitative scientists.19 A more HPS-critical type of explanation of our results lays the blame on the picture painted of science within HPS. On this explanation, it would be claimed that HPS scholars often present an impoverished or simplistic view of science in which skeptical and anti-realist arguments will seem stronger than they are. We note three ways in which this challenge could be substantiated. First, it could be argued that HPS scholars are trained and encouraged to emphasize the role of historically contingent factors in the development of science, such as the influence of social and political values. Conversely, HPS scholars will tend to reject as uninteresting or 'whiggish' research into scientific developments where scientists simply adopted theories on the basis of strong empirical evidence. As a consequence, it would be argued, HPS scholars are presented with a picture of science that is skewed towards anti-realism.20 Second, much historical work that is meant to challenge scientific realism (ER in particular) proceeds from studying one or a few instances of scientific theories that were, in retrospect, false or insufficiently supported by the 19 We are grateful to an anonymous reviewer for their suggestion of appealing to textbooks in elaborating this explanation. 20 We are grateful to another anonymous reviewer for suggesting this explanation. SCIENTIFIC REALISM IN THE WILD 33 available evidence. However, quantitative scientists may be skeptical of placing much stock on what is essentially anecdotal evidence, and may furthermore have reasons to think that the cases in question are not representative of the currently accepted theories in their fields. Third, working scientists may well have access to a variety of unstated or 'tacit' evidence that isn't – and perhaps could not be – reported in scientific articles and documents, but that nevertheless justifiably increases their levels of confidence in currently accepted theories.21 Scientific conclusions that may appear from the outside to be blind leaps of faith, or overconfidence in a particular theory, may in fact be grounded in a tacit but nevertheless justified understanding of the scientific subject matter in question. Another significant finding concerns the differences in realist tendencies between scientific disciplines. In a recent study, Robinson et al. (2018) investigated differences in the philosophical opinions of 264 scientists classified into three different branches of science, 'Life Sciences', 'Physical Sciences and Mathematics' and 'Social and Behavioral Sciences'. For our purposes, their most relevant results concern two philosophical statements that Robinson et al. characterize as concerning 'Reality', viz. "Scientific claims need not represent objective reality to be useful" (Reality 2) and "The subject of my research is a human construct" (Reality 4). Robinson et al. found statistically significant differences between scientists in Life Sciences and Social and Behavioral Sciences on Reality 2. They also found statistically significant differences on Reality 4 between Life Science and Physical Sciences and Mathematics on the one hand, and Social and Behavioral Sciences on the other hand. In comparison to Robinson et al., our study is much larger and more powerful (1,798 participants as compared with 264); employed a more fine-grained categorization of scientific 21 This type of tacit evidence would presumably be especially common in heavily experimental sciences, which would seem to include the sciences we have characterized as 'quantitative' (with the possible exception of economics). SCIENTIFIC REALISM IN THE WILD 34 fields (seven 'disciplines' rather than three 'branches'); and probed more specific and precisely stated opinions regarding scientific realism.22 However, our study partially confirmed Robinson et al.'s results in so far as "The subject of my research is a human construct" (Reality 4) corresponds to (the denial of) our statement (1): "The objects and phenomena studied by science exist independently of how we conceive of or think about them." In our study, psychologists, sociologists and anthropologists scored significantly lower than biologists (which most clearly represent the Life Sciences in our study, all p's < .05). Indeed, the former fields also scored much lower than chemists and physicists (p's < .001), which would presumably be classified as 'Physical Sciences' by Robinson et al. Interestingly, however, Robinson et al. did not observe a statistically significant difference between their 'Physical Sciences and Mathematics' and 'Social and Behavioral Sciences' on Reality 4. From the point of view of our study, this is an anomaly that may be explained by the inclusion of mathematics in the former branch. After all, it is plausible that a non-trivial number of mathematicians see the subject of their research – mathematical objects – as a 'human construct' (in line with intuitionism about mathematics; see Iemhoff 2013).23 More significantly, our study dramatically extends Robinson et al.'s results about differences between scientific fields to a number of issues regarding scientific realism. In the broadest terms, our results show that quantitative sciences such as physics and economics appear to be much more inclined towards standard forms of scientific realism than qualitative sciences 22 We also believe that the statements we asked our participants to evaluate provide for a better reflection of the issues that lie at the heart of the various scientific realism debates in philosophy of science. In particular, we find it doubtful that any sensible scientific realist would claim that scientific theories must represent objective reality in order to be useful (Reality 2), since it is widely acknowledged – even among realists – that idealizations play an indispensable role in science (e.g., Elliot-Graves and Weisberg 2014). 23 We find it harder to locate a statement corresponding to Reality 2 (the other statement about which Robinson et al. obtained statistically significant results concerning between-branch differences) within our study – in part because we do not think Reality 2 quite corresponds to anything seriously debated in the realism literature (see previous footnote). Nevertheless, for our statement (5), which bears some similarity to Robinson et al.'s Reality 2, we observed significant differences between the responses from biology and each of the social and behavioral sciences (economics, psychology, sociology, anthropology, all p's < .01). SCIENTIFIC REALISM IN THE WILD 35 such as sociology and anthropology. These differences were especially pronounced with regard to metaphysical statements about objective truth and mind-independent reality (MR) and whether scientific progress involves more accurate representation of underlying reality (PR). This may in part be explained by the fact that the subject matters of sociology and anthropology are arguably more subjective and malleable by human intervention than the subject matters of the natural sciences. However, this conjecture seems inadequate to explain why economics aligns itself so closely with the natural sciences, given that the subject matter of economics is similar in the aforementioned respects. A better explanation may thus appeal to the fact that economics tends to employ a similar methodology as the natural sciences in our study, with a heavy emphasis on quantitative methods. For example, it may be that dedicating oneself to quantitative methods instills a realist mindset about one's theories and their relation to evidence. Alternatively, those who come to appreciate using quantitative methods may be somehow predisposed towards realism. Again, these issues would need to be investigated further. So far in this section we have focused on what we label 'Standard Scientific Realism' (statements 1-4), which cluster with statements of NMA and scientific convergence to a single perspective (statements 7 and 10). As we have noted, responses to these statements did not cluster with vFR, defined as a two-tiered thesis about the belief involved in acceptance of a theory and the basic criterion for success in science. This suggests that not only does van Fraassen's approach differ from the ways other philosophers of science have defined scientific realism but also that vFR may not connect in substantial ways with the broad set of issues involved in discussions of scientific realism. On the one hand, this is not surprising given that van Fraassen's intention was to restructure the scientific debate in light of what he saw as an inadequate conception of the underlying issue. On the other hand, it may be worrying for van Fraassen that our participants SCIENTIFIC REALISM IN THE WILD 36 apparently did not see van Fraassen as addressing the same philosophical issue as other contributors to the scientific realism debates. Presumably, the goal of van Fraassen's restructuring of the debate will have been to capture a logically distinct but closely associated set of issues; if so, our data suggest that the restructuring was not entirely successful. With that said, another interesting result of our study is that the two statements of different aspects of vFR did cluster together. This suggests that van Fraassen's two claims about the belief involved in acceptance and science's criterion of success do indeed 'come as a package' in the minds of scientists, as opposed to being two entirely separate issues that can be treated independently of one another. Discussing these claims independently of each other, as philosophers of science often do in their treatment of van Fraassen's constructive empiricism (e.g., Dicken 2010) might thus be somewhat misleading. Yet another interesting result is how popular van Fraassen's type of scientific anti-realism appears to be in all of the scientific disciplines in our study, both absolutely and relative to other forms of scientific realism (MR, ER, and PR). In analytic philosophy of science, where nearly all the debate about van Fraassen's conception of realism and anti-realism has taken place, very few have aligned themselves with van Fraassen's constructive empiricism or any other position that counts as anti-realist on van Fraassen's definition.24 However, our results suggest that this is by far the most popular version of anti-realism among scientists, and this holds for each of the scientific disciplines studied here (compare Figure 4 to Figures 1-3 and 5). It is noteworthy in this regard that many well-known scientists have publicly expressed positions that are very similar to 24 Some exceptions in which van Fraassen's constructive empiricism is defended (with various important qualifications) include Bueno 1999, Muller 2004, Cartwright 2007, and Dellsén 2017; see also Wray (2007, 2008, 2010) for defenses of van Fraassen's arguments against realism. Most other contributions to the literature criticize constructive empiricism in one way or another – see, e.g., Churchland 1985, Musgrave 1985; Horwich 1991; Psillos 1996; Ladyman 2000, Alspector-Kelly 2001, Dicken and Lipton 2006. SCIENTIFIC REALISM IN THE WILD 37 van Fraassen's anti-realism, e.g. Hawking and Mlodinow's (2010) 'model-dependent realism'. Such anecdotal evidence for the popularity of a van Fraassen-style anti-realism about science can now be backed up with more systematic support. The relative popularity of van Fraassen-style anti-realism among scientists is also significant in light of the exchange between van Fraassen (1994) and Rosen (1994) about the nature of constructive empiricism. In brief, their exchange concerned the relationship between the scientific realism debate, as construed by van Fraassen, and the intentions of individual scientists. Rosen claimed that the debate could not simply be about scientists' own understanding of the aim of science, since that should be settled by a sociological investigation in which scientists are simply asked for their personal opinions on the matter. In van Fraassen's response, he acknowledged that scientific realism and constructive empiricism are not straightforward empirical claims that could be directly tested by a sociological investigation of this sort, due primarily to the fact that who are counted as scientists in the first place will be determined in part by one's view of what it is to do science, which is in turn determined by science's criterion of success. Any sociological investigation into the extent to which scientists are realists or anti-realists would thus be complicated by an "hermeneutic circle" in which the extension of 'science' is partly determined by the aim of science, and vice versa (van Fraassen 1994: 188-192). It is not clear to us exactly where van Fraassen lands on the issue of whether vFR could be investigated empirically by surveying scientists.25 However, our own view is that the opinions held by scientists themselves are at least highly relevant to evaluating vFR (and van Fraassen's antirealist alternative, constructive empiricism), since it surely counts against a philosophical account 25 Van Fraassen certainly seems to suggest that an empirical investigation would be relevant in some way or another, at least as an indication of whether vFR, or his own constructive empiricism, could be offered as plausible account of the aim of science on a standard or common definition of 'science'. However, this interpretative question about how van Fraassen should be understood lies outside the scope of this paper. SCIENTIFIC REALISM IN THE WILD 38 if it must attribute to scientists substantial and widespread misconceptions about science's own criterion of success and what it is in fact involved in accepting a scientific theory.26 Our impression is that many philosophers of science have assumed that this type of empirical study would count in favor of vFR and against constructive empiricism (see, e.g., Rosen 1994: 145-146). Our study suggests that the opposite is true. Indeed, every single discipline's mean vFR score fell on or below the midpoint. This indicates that most scientists are anti-realists of some kind on van Fraassen's conception of the debate. There are many other interesting correlations in our data, but we restrict ourselves to highlighting two further results. First, it is noteworthy how closely agreement with PR – the idea that progress is matter of getting closer to the underlying truth – correlates with other forms of realism, MR and ER, and the associated NMA. Second, we were surprised to find no strong correlation in our data between standard forms of scientific realism, SSR, and what is usually taken as the main argument against scientific realism, viz. PI (statement 8). It is worth noting that the same is not true for the most popular type of argument in favor of scientific realism, viz. NMA (statement 7), which correlates very strongly with ER and PR (see table 7) and clusters with SSR (see table 8). This suggests that, in contrast to most philosophers of science, scientists who endorse PI do not view it as posing a serious challenge to ER and PR. Perhaps, then, the realist response to PI that is most likely to be found convincing among working scientists will not consist in rebutting this anti-realist argument, but rather in articulating a form of scientific realism that is unambiguously compatible with the PI.27 26 To be sure, this is not the only consideration relevant to evaluating vFR and its anti-realist alternatives – they would also need to explain the standards for scientific theory acceptance, scientists' practice of providing explanations for observable phenomena, and so forth – but we fail to see any good reason to ignore this type of consideration entirely. However, with the exception of some rather inconclusive evidence provided by Jones (2003), this is exactly what has occurred in the debate about vFR and its alternatives in the philosophy of science literature. 27 To be sure, this has been suggested in different ways by Lyons 2005, Roush 2010, and Niiniluoto 2017. SCIENTIFIC REALISM IN THE WILD 39 We hope that the findings we report here regarding the judgments of scientists and HPS scholars about different dimensions of scientific realism will lead to fruitful discussions about the nature of scientific realism and the relationship between scientific practices and philosophical theorizing about those practices. References Alspector-Kelly, Marc. 2001. Should the Empiricist Be a Constructive Empiricist? Philosophy of Science 68: 413-431. Bird, Alexander. 2007. What is Scientific Progress? Noûs 41: 64-89. Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford: Oxford University Press. Cattell, Raymond B. 1966. The Scree Test For The Number Of Factors. Multivariate Behavioral Research 1: 245–76. Chakravartty, Anjan. 2007. A Metaphysics for Scientific Realism: Knowing the Unobservable. Cambridge: Cambridge University Press. Churchland, Paul M. 1985. The Ontological Status of Observables: In Praise of the Superempirical Virtues. In P. M. Churchland and C. A. Hooker (eds.) Images of Science: Essays on Realism and Empiricism with a Reply from Bas C. van Fraassen. Chicago: University of Chicago Press, pp. 35-47. Dellsén, Finnur. 2016. Scientific Progress: Knowledge versus Understanding. Studies in History and Philosophy of Science 56: 72-83. Dellsén, Finnur. 2017. Reconstructed Empiricism. Acta Analytica 32: 95-113. SCIENTIFIC REALISM IN THE WILD 40 Dicken, Paul. 2010. Constructive Empiricism: Epistemology and the Philosophy of Science. New York: Palgrave Macmillan. Dicken, Paul, and Peter Lipton. 2006. What Can Bas Believe? Musgrave and van Fraassen on Observability. Analysis 66: 226-233. Egg, Matthias. 2016. Expanding Our Grasp: Causal Knowledge and the Problem of Unconceived Alternatives. British Journal for the Philosophy of Science 67: 115-141. Elliot-Graves, Alkistis, and Michael Weisberg. 2014. Idealization. Philosophy Compass 9: 176185. Frost-Arnold, Greg. 2014. Can the pessimistic induction be saved from semantic anti-realism about scientific theory? British Journal for the Philosophy of Science 65: 521-548. Forrest, Peter. 1994. Why Most of Us Should Be Scientific Realists: A Reply to Van Fraassen. The Monist 77: 47-70. Hacking, Ian. 1983. Representing and Intervening: Introductory Topics in the Philosophy of Natural Science. Cambridge: Cambridge University Press. Hawking, Stephen and Leonard Mlodinow. 2010. The Grand Design. New York: Bantam Books. Hesse, Mary. 1976. Truth and the Growth of Scientific Knowledge. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, vol. 2, pp. 261-280. Horwich, Paul. 1991. On the Nature and Norms of Theoretical Commitment. Philosophy of Science 58: 1-14. Hutcheson, Graeme, and Sofroniou, Nick. 1999. The Multivariate Social Scientist. London: Sage. Iemhoff, Rosalie 2013. Intuitionism in the Philosophy of Mathematics. In E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Winter 2016 Edition). URL = <https://plato.stanford.edu/archives/win2016/entries/intuitionism/>. Jones, Ward. 2003. Is Scientific Theory-Commitment Doxastic or Practical? Synthese 137: 325344. SCIENTIFIC REALISM IN THE WILD 41 Kaiser, Henry F. 1960. The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement 20: 141-51. Kuhn, Thomas S. 1970. The Structure of Scientific Revolutions (2nd ed.). Chicago, IL: University of Chicago Press. Ladyman, James. 2000. What's Really Wrong with Constructive Empiricism? Van Fraassen and the Metaphysics of Modality. The British Journal for the Philosophy of Science 51: 837856. Laudan, Larry. 1977. Progress and its Problems: Toward a Theory of Scientific Growth. Berkeley: University of California Press. Laudan, Larry. 1981a. A confutation of convergent realism. Philosophy of Science 48: 19-49. Laudan, Larry. 1981b. A problem-solving approach to scientific progress. In I. Hacking (ed.), Scientific Revolutions. Oxford: Oxford University Press, pp. 144-155. Lyons, Timothy D. 2005. Toward a purely axiological scientific realism. Erkenntnis, 63: 167-204. Muller, F. A. .2004. Can a Constructive Empiricist Adopt the Concept of Observability? Philosophy of Science 71: 80-97. Musgrave, Alan. 1985. Realism Versus Constructive Empiricism. In P. M. Churchland and C. A. Hooker (eds.) Images of Science: Essays on Realism and Empiricism with a Reply from Bas C. van Fraassen. Chicago: University of Chicago Press, pp. 197-221. Musgrave, Alan 1988. The Ultimate Argument for Scientific Realism. In R. Nola (ed.), Relativism and Realism in Science. Dordrecht and Boston: Kluwer Academic Publishers, pp. 229-252. Niiniluoto, Ilkka. 2014. Scientific progress as increasing verisimilitude. Studies in History and Philosophy of Science 46: 73-77. Niiniluoto, Ilkka. 2017. Optimistic realism about scientific progress. Synthese 194: 3291-3309. SCIENTIFIC REALISM IN THE WILD 42 O'Connor, Brian P. 2000. SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods, Instrumentation, and Computers 32: 396-402. Pearson, Karl. 1911. The Grammar of Science, 3rd ed. New York: Macmillan. Poincaré, Henri 1952/1905. Science and Hypothesis. New York: Dover. Republication of the first English translation, published by Walter Scott Publishing, London. Psillos, Stathis. 1996. On van Fraassen's Critique of Abductive Reasoning. The Philosophical Quarterly 46: 31-47. Psillos, Stathis. 1999. Scientific Realism: How Science Tracks Truth. London: Routledge. Putnam, Hilary. 1975. "What is Mathematical Truth?" In Mathematics, Matter and Method: Philosophical Papers, Vol. I. Cambridge: Cambridge University Press, pp. 60-78. Robinson, Brian, Chad Gonnerman, and Michael O'Rourke. Forthcoming. Experimental Philosophy of Science and Philosophical Differences across the Sciences. Philosophy of Science. Rosen, Gideon. 1994. What is Constructive Empiricism? Philosophical Studies 74: 143-178. Roush, Sherrilyn. 2010. Optimism about the Pessimistic Induction. In P.D. Magnus and Jacob Busch (eds.), New Waves in Philosophy of Science. Basingstoke: Palgrave Macmillan, pp. 29-58. Rowbottom, Darrell P. 2011. The instrumentalist's new clothes. Philosophy of Science 78: 12001211. Rowbottom, Darrell P. Forthcoming. The Instrument of Science. London: Routledge. Schlick, Moritz. 1936. Meaning and Verification. The Philosophical Review 45: 339-369. Smart, J.J.C. 1963. Philosophy and Scientific Realism. London: Routledge & Kegan Paul. SCIENTIFIC REALISM IN THE WILD 43 Stanford, P. Kyle 2006. Exceeding Our Grasp: Science, History, and the Problem of Unconceived Alternatives. Oxford: Oxford University Press. van Fraassen, Bas C. 1980. The Scientific Image. Oxford: Clarendon Press. van Fraassen, Bas C. 1985. Empiricism in the Philosophy of Science. In P. M. Churchland and C. A. Hooker (eds.) Images of Science: Essays on Realism and Empiricism with a Reply from Bas C. van Fraassen. Chicago: University of Chicago Press, pp. 245-308. van Fraassen, Bas C. 2001. Constructive empiricism now. Philosophical Studies 106: 151-170. van Fraassen, Bas C. 2007. From a View of Science to a New Empiricism. In B. Monton (ed.), Images of Empiricism: Essays on Science and Stances, with a Reply from Bas C. van Fraassen. Oxford: Oxford University Press, pp. 337-384. van Fraassen, Bas C. 2017. Misdirection and Misconception in the Scientific Realism Debates. In E. Agazzi (ed.), Varieties of Scientific Realism. Dordrecht: Springer, pp. 95-108. Wray, K. Brad (2007). A selectionist explanation for the success and failures of science. Erkenntnis 67: 81-89. Wray, K. Brad (2008). The Argument from Underconsideration as Grounds for Anti-realism: A Defense. International Studies in the Philosophy of Science 22: 317-326. Wray, K. Brad (2010). Selection and Predictive Success. Erkenntnis 72: 365-377. Worrall, John. 1989. Structural realism: The best of both worlds. Dialectica 43: 99-124. Worrall, John. 1994. How to remain (reasonably) optimistic: Scientific realism and the "Luminiferous Ether". In R. M. Burian, D. Hull. M. Forbes (eds.) PSA 1994: Proceedings of the 1994 Biennial Meeting of the Philosophy of Science Association, volume 1, pp. 334342. Worrall, John. 2011. "The No Miracles Intuition and the No Miracles Argument." In D. Dieks, W. Gonzalez, S. Hartmann, T. Uebel, and M. Weber (eds.), Explanation, Prediction, and Confirmation. The Philosophy of Science in a European Perspective, vol 2. Dordrecht: Springer, pp. 11-20.