Abstract
There is an increasing demand in the field of science education for the incorporation of philosophical and sociological aspects that are related to the scientific enterprise in school curricula, to the extent that the incorporation of these aspects is now considered a necessity. Several of these aspects can be categorised within the framework of the nature of science, or NOS. We warn that a possible misinterpretation of the common view of NOS tenets can lead to epistemic relativism. We pay special attention to the empirical and objective nature of science because these important features, properly understood, can help eliminate subjective flaws and protect against relativism. Some of the epistemological concepts that are relevant to this discussion are disambiguated in an attempt to prevent the temptation to take views to an extreme, as has occurred in some cases. We expect this analysis to contribute to the extant literature by improving how science is presented in the classroom without oversimplifying scientific practice.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
This central point has been stressed in other places, for example Irzik and Nola (2011).
We take the title of one of his papers as evidence of McComas’ intention of characterising science by the list of myths (McComas 2002), which is ‘The principal elements of the nature of science: dispelling the myths”. In this study, a characterisation of science is made from an extended list of 15 myths. In the first paragraph of this article, McComas explicitly claims that the 15 major issues related to NOS do not represent all the important issues. While he remains silent on the issues left behind, later he claims that this list of 15 myths ‘... serve as starting points for evaluating current instructional foci while enhancing future curriculum design.’ (p.53).
Lederman et al. (2002) make a similar list of tenets to characterise science; this list has several points in common with McComas’ ten myths. In Lederman’s characterisation, it is stressed that scientific knowledge does not have a straightforward relationship with experiments; that there are differences between observations, inferences, theories and laws; that observations are driven by theory; that science is influenced by contingent socio-cultural factors; that there is no scientific method and that scientific knowledge is tentative. Some of these issues will be analysed in detail in this text.
In ‘The structure of scientific revolutions’, a new form of approaching the scientific enterprise is offered in terms of times of ‘normal science’ (times where things are more or less as the positivists characterise sciences) and times of ‘paradigm change’ (times where new ideas appear that are ‘incommensurable’ with old ideas, where theory choice is not driven by rationality). Kuhn rejects the idea that sciences approach the truth and stresses the importance of social and subjective aspects of the scientific activity.
According to Popper, nothing can ever be verified conclusively by experiments without appealing to inductive reasoning (an unjustified projection of current results to any equivalent future experiment); in contrast, we can dismiss or reject conclusively a given proposition, all that is required is one counterexample. According to Popper, this is exactly what science does; science and scientists do not try to verify theories (i.e. to prove that theories are right), rather, they try to falsify theories (i.e. to show that theories are false).
The ‘no miracle argument’ claims that science success would be a miracle if scientific theories were not true or approximately true, even worse, as science works in very different contexts, it would be a greatly repeated miracle, and because miracles do not exist (the argument claims), then mature and corroborated scientific theories must be true or approximately true.
Scientific realism claim that mature scientific theories have to be true or approximately true, and endorse metaphysical commitments (are realist about the world), epistemological commitments (this world can be studied by science) and semantic commitments (the theoretical elements in scientific theories must refer to genuine elements or properties of the world).
Constructive empiricism understands science not as an activity that aims to provide a true story about what the world is, but rather as providing empirically adequate models.
The pessimistic meta-inductive argument claims that scientific theories have changed dramatically. Old scientific theories once considered true are now rejected; therefore, the theoretical terms that are present in old discarded theories cannot genuinely refer to anything, being false the whole time. The list of theories that fall into such dramatic changes is so large that by inductive reasoning, we should expect our current theories to be false too; therefore, science does not deliver truth.
Epistemic structural realism, according to Worrall, takes the best of Laudan’s pessimistic meta-inductive argument and the scientific realist position to claim that science does deliver truth, but it is not the reference of the theoretical elements which is true of theories, but rather is the structure of theories which is true or approximately true.
Each aforementioned author posits an alternative to the consensus view: Matthews argues for an extended list of features of science (or FOS); Clough suggests a change from a list of tenets to a set of questions guiding the reflections of students, and Irzik and Nola resolve to move from the consensus view of the nature of science to a family resemblance approach to the nature of science; this proposal was later extended in 2014 and was further complemented by Erduran and Dagher (2014). In our opinion, there are strong arguments and enough reasons behind the search for alternatives to the ‘consensus NOS’ to celebrate the efforts made by these authors in improving how philosophical and sociological dimensions of the scientific endeavour are treated in science education. This is not the place to analyse each of those proposals, nor are we in a position to promote or defend the curricula suggested by any of them; however, we encourage the reader interested in the topic to study the aforementioned approaches.
Matthews (2012) extends Lederman’s seven items of NOS with other features that are representative of science, such as experimentation, idealisation, the use of models and mathematization, among others. Not every feature in Matthews’ list is central and distinctive of science (that is not the intention of the list). However, the four features mentioned are unquestionably representative of the activity, and Mathews’ reflections capture much of our point.
Famously, Laudan (1983) claims that the problem of demarcation is a pseudo-problem; Hansson (2009) defends a demarcation criterion between science and pseudoscience, Resnik (2000) defends a pragmatic criterion of demarcation and for an example of concrete legal consequences with regard to the demarcation problem to assess the perils of epistemic relativism, see Pennock (2011).
Asimov (1989) quite convincingly argues how several educational instances lead naturally to a clear dichotomy between right and wrong without noting that some claims are closer to the truth than others. This bad habit prevents us from assessing important subtleties and blurs any notion of scale (how good an approximation is or the range of confidence in any assertion), leading to the idea that any claim is unavoidably wrong because it is not exactly right. This tendency leads to epistemic relativism; conversely, a clear notion of how and when a claim is valid and closer to the truth is a useful tool against relativism.
Perhaps a similar concern has motivated other authors to propose the teaching of NOS using questions instead of tenets (Clough 2007), thus avoiding a blind adherence to NOS notions that can lead to biased standpoints.
Needless to say, there are no perfect tools.
We are not claiming that the problem of induction has been solved by science; what is claimed is that science studies the regularities of the world and expects to find connections (causal or otherwise) between phenomena. For this to be achieved, regularities or causal connections are starting points, despite the fact that the justification of those topics constitutes a deep philosophical problem. In this sense, science uses intensively inductive reasoning without worrying about its justification, not because it is unproblematic but because that is a philosophical problem, not a scientific one. There are different potential philosophical arguments that attack the problem of induction, and reference to several approaches and the elaboration of a particular argument based on an Inference to the best explanation as a fundamental form of reasoning appears in chapter 12 of McCain (2016).
It might be argued that linear progress in science would be expected if, following the metaphor, nature always responds truthfully to our questions, because once the question is posed, the answer is final, and old scientific conclusions will never be revisited nor improved. We claim that nature always provides the same answers to the same questions, but that does not ensure that the message is always transparent to us. The search for correct interpretations of those answers and the elaboration of better (more specific) questions is a crucial part of the scientific endeavour. The reader of this essay should not take this paragraph as an attempt to provide a demarcation criterion for science; instead, we are simply stressing its empirical character.
An example of this is analysed below, in the next section, on the topic of string theory.
At the time this was written, the boson found at LHC was still being tested to determine whether it corresponds to the Higgs boson predicted by the theory.
One could argue that the standard model of particle physics, of which the Higgs mechanism is a fundamental piece, has been accepted as scientific knowledge both by the last two generations of physicists and by the larger scientific community, long before this empirical proof was obtained. Two related occurrences show the complex relation between expert consensus and scientific knowledge. First, before the LHC measurements, other parts of the standard model (e.g. the prediction of the Z or W bosons and the top quark) were found to agree with observations, which provided substantial support for the entire theoretical structure. Second, there are known problems with the standard model picture at energies higher than those currently achievable by our largest accelerators, presenting the possibility of observing non-SM physics in the LHC and possibly even invalidating the Higgs mechanism itself. Thus, the expert consensus with respect to the standard model and the Higgs boson was arguably an altogether common mixture of confidence and scepticism. The fact remains that independent of the number of sympathisers, a large international investment of both money and expert man-hours was deemed necessary to finally accept the existence of the Higgs particle and therefore, the correctness of the Higgs mechanism.
Although bivalent logic allows only two defined truth values for each proposition (either true or false), there is a sense in which it is clear that some propositions are further from the truth than others. Asimov (1989) exemplified this clearly and intuitively, but for a more rigorous philosophical account of verisimilitude or truthlikeness and critical discussions on the topic see Miller (1972), Popper (1976) and Hilpinen (1976).
The problem of what is retained in theory change, that is, the problem of recognising those theoretical elements that are true or approximately true of our theories, allowing sophisticated realist positions, is part of an ongoing philosophical problem and is related to what is called ‘selective realism’. According to some authors, the mathematical structure of the world is genuinely captured by our best theories, and we should be realist about it (Worrall 1989), while for others, we should be realists about only those mechanisms and laws that are causally responsible for (or indispensable to) relevant phenomena (Psillos 2005).
Some philosophers of science have distinguished between context of discovery and context of justification; the former is whatever promotes the discovery: hard work, dreams, use of psychotropic drugs, hallucinations, mystic revelations, unexpected results in the lab or collaboration with colleagues. The latter is related to whatever validates that discovery, such as mathematical or logical consistency, evidential support or valuable predictions. It might be important to note within the framework of our discussion that many philosophers (such as Reichenbach and Popper) believe that only the context of justification is relevant for scientific knowledge and that this context is susceptible to logical analysis. For an overview of the distinction and its importance, see section 5 of Schickore (2014).
There are many critics of Carnap’s project. See, for example, Quine (1951), page 37.
This point is similar to the one previously exposed at the beginning of the “The Empirical Basis of Science” section, when discussing McComas’ criticism against the notion of science as an experimentally based activity. McComas equates ‘experiments’ to ‘manipulation of controlled cases’, whereas in scientific contexts ‘observations’ are usually considered as ‘experimental evidence’, whether or not such evidence is gathered after careful manipulation in a lab.
In the same spirit, Allchin (2011) denounced the importance of the distinction between ‘hypothesis’ and ‘prediction’ for a group of researchers who presented their papers at a conference that was also attended by Allchin. Allchin notes that ‘The presenter implicitly implied that this distinction was not only essential to teach NOS, but that mastering it constituted a key triumph for students [...] No one articulated how this might lead either to better scientific practice or to deeper understanding of scientific claims’.
Lederman et al. might be referring to the impossibility of crucial experiments (in the line of Duhem-Quine’s objection to Popper’s falsifiability criterion) by ‘direct testing’ and not to the ‘naked reporting of the senses’. However, we doubt that this is the case because Lederman et al. use the lack of ‘direct testing’ in connection with the unobservable, not in connection to theoretical choice.
Lederman’s sentence “...Scientific theories are often based on a set of assumptions or axioms and posit the existence of non-observable entities. Thus, theories cannot be directly tested…” seems to claim that there is no crucial experiment because there are unobservable entities, not because the negative results of any experiment prevent us from deciding if an auxiliary hypothesis, a part of a theory, or a whole theory should be changed. If that argument were true, then there would be two different reasons to deny the existence of crucial experiments; however, the impossibility of empirical evidence does not follow from the unobservable character of theoretical entities.
It should be noted that the lack of direct testing (in the Duhem-Quine sense) is a sound argument to address the eventual difficulties between empirical results and the theories or theoretical constructions that are available for their interpretation. Nevertheless, Duhem and Quine’s argument neither questions the empirical nature of science nor claims that theoretical constructions lack empirical evidence; it may warn us about the uniqueness of our conclusions concerning empirical results; again, however, this is part of a discussion that lies outside of science’s empirical nature.
Schommers (1994) develops this idea in a book chapter. Although the book arrives at several conclusions all too quickly, the arguments about the topic at hand are sufficiently covered and supported.
Each colour corresponds to a determined frequency of oscillation of the electromagnetic field in a range that extends from 430 to 790 THz. This description was explicitly avoided in the text because it can be argued that it rests on unobservable theoretical terms (electromagnetic field). Therefore, it only mentioned the empirical conclusion, which has been corroborated by countless independent experiences, that colour is determined by the oscillation of ‘something’.
See Olsson (2014) for a general description of the Coherence Theories of Epistemic Justification or Poston (2014) for a defence of a variant called explanatory coherentism. Conversely, McCain (2014) promotes a mentalist-internalist theory of epistemic justification (explanationist evidentialism) that is closer to foundationalism and is compatible with the idea of ‘indirect testing’.
References to Plato may seem outdated, and the requirement of ‘truth’ perhaps too stringent; however, modulo refinements, the traditional theory of knowledge, is still the best theory available to address propositional knowledge within analytical philosophy, and the ‘truth’ requirement is relatively unproblematic. The most important and problematic condition is the ‘justification’ requirement (What counts as justification? How is it validated? How is it achieved?). The first eight chapters of McCain’s (2016) book are devoted to analysing the traditional theory of knowledge in a comprehensive way, including its problems, and proposing a particular and coherent perspective based on evidence as the basis of justification, which fuses with scientific knowledge in the rest of the book.
This standpoint agrees with and theoretically justifies the ‘degrees’ of truth defended by Asimov (1989) and its role in a mature comprehension of the status of scientific knowledge.
Faced with many philosophical subtleties and aware that scientific claims with enough evidence to be considered true are often erroneously catalogued as ‘scientific knowledge’, McCain entertains the idea of eradicating the notion of knowledge from science. However, he provides pragmatic reasons for continuing to use ‘scientific knowledge’ as the label for those highly corroborated claims, as opposed to weaker forms of justified belief, primarily to avoid epistemic relativism. We agree completely with his view on this topic. See section 8.5 of McCain 2016 (p 127–128).
A similar claim is made by Franklin (1989).
The notion of objectivity in science is much improved with respect to this example, involving the collaborative effort of many scientists and experimental devices.
Matthews (2012) proposes to change teaching NOS to include a discussion of the features of science (FOS) in the classroom. Clough (2007) encourages us to abandon the exposition of NOS tenets and to replace it with an inquiry-based methodology and Irzik and Nola (2011) propose replacing NOS as minimal consensus and promoting an image of science based in family resemblance.
References
AAAS. (1993). Benchmarks for science literacy. New York: Oxford University Press.
Abd-El-Khalick, F. (2012). Examining the sources for our understandings about science: enduring conflations and critical issues in research on nature of science in science education. International Journal of Science Education, 34(3), 353–374. https://doi.org/10.1080/09500693.2011.629013.
Allchin, D. (2011). Evaluating knowledge of the nature of (whole) science. Science Education, 95(3), 518–542. https://doi.org/10.1002/sce.20432.
Alters, B. J. (1997). Whose nature of science? Journal of Research in Science Teaching, 34(1), 39–55.
Asimov, I. (1989). The relativity of wrong. Skeptical Inquirer, 14(1), 35–44.
Ayer, A. J. (2012). Language, truth and logic. New York: Dover.
Bean, S. J. (2011). Emerging and continuing trends in vaccine opposition website content. Vaccine, 29(10), 1874–1880. https://doi.org/10.1016/j.vaccine.2011.01.003.
Brickhouse, N. W. (1990). Teachers’ beliefs about the nature of science and their relationship to classroom practice. Journal of Teacher Education, 41(3), 53–62. https://doi.org/10.1177/002248719004100307.
Carnap, R., George, R. A. (1969). The logical structure of the world: And, Pseudoproblems in philosophy. Open Court Publishing.
Clough, M. P. (2007). Teaching the nature of science to secondary and post-secondary students: questions rather than tenets. The Pantaneto Forum, 25, 31–40.
Diethelm, P., & McKee, M. (2009). Denialism: what is it and how should scientists respond? The European Journal of Public Health, 19(1), 2–4. https://doi.org/10.1093/eurpub/ckn139.
Douglas, H. (2004). The irreducible complexity of objectivity. Synthese, 138(3), 453–473. https://doi.org/10.1023/B:SYNT.0000016451.18182.91.
Eflin, J. T., Glennan, S., & Reisch, G. (1999). The nature of science: a perspective from the philosophy of science. Journal of Research in Science Teaching, 36(1), 107–116. https://doi.org/10.1002/(SICI)1098-2736(199901)36:1<107::AID-TEA7>3.0.CO;2-3.
Erduran, S., & Dagher, Z. R. (2014). Reconceptualizing nature of science for science education. Springer.
Franklin, A., Anderson, M., Brock, D., Coleman, S., Downing, J., Gruvander, A., et al. (1989). Can a theory-laden observation test the theory? The British Journal for the Philosophy of Science, 40(2), 229–231.
Gil Pérez, D., Macedo, B., Martínez-Torregrosa, J., Sifredo, C., Valdes, P., Vilches, A. (2005). ¿Cómso promover el interés por la cultura científica?, Santiago de Chile: UNESCO para América Latina y el Caribe.
Goertzel, T. (2010). Conspiracy theories in science. EMBO Reports, 11(7), 493–499. https://doi.org/10.1038/embor.2010.84.
Hansson, S. O. (2009). Cutting the Gordian knot of demarcation. International Studies in the Philosophy of Science, 23(3), 237–243. https://doi.org/10.1080/02698590903196007.
Hilpinen, R. (1976). Approximate truth and truthlikeness. In: Formal methods in the methodology of empirical sciences (pp. 19–42). Springer Netherlands.
Irzik, G., & Nola, R. (2011). A family resemblance approach to the nature of science for science education. Science and Education, 20(7–8), 591–607. https://doi.org/10.1007/s11191-010-9293-4.
Kosso, P. (1989). Science and objectivity. The Journal of Philosophy, 86(5), 245–257. https://doi.org/10.2307/2027109.
Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed.). Chicago: University of Chicago Press.
Ladyman, J. (2002). Understanding philosophy of science. London: Routledge.
Ladyman, J. (2016). Structural realism. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy (winter 2016 edition). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/win2016/entries/structural-realism/ January 2017
Laudan, L. (1981). A confutation of convergent realism. Philosophy of Science, 48, 19–49. https://doi.org/10.1086/288975.
Laudan, L. (1983). The demise of the demarcation problem. In Physics, philosophy and psychoanalysis (pp. 111–127). Dordrecht: Springer Verlag.
Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching, 39(6), 497–521. https://doi.org/10.1002/tea.10034.
Lederman, N. G. (2007). Nature of science: past, present, and future. Handbook of research on science education, 2, 831–879.
Lewandowsky, S., Gignac, G. E., & Oberauer, K. (2013). The role of conspiracist ideation and worldviews in predicting rejection of science. PLoS One, 8(10), e75637. https://doi.org/10.1371/journal.pone.0075637.
Matthews, M. R. (1999). Response: social constructivism and mathematics education: some comments. Philosophy of Education Archive, 330–341.
Matthews, M. R. (2012). Changing the focus: from nature of science (NOS) to features of science (FOS). In Advances in nature of science research (pp. 3–26). Dordrecht: Springer Verlag.
McCain, K. (2014). Evidentialism and epistemic justification (Vol. 59). New York: Routledge.
McCain, K. (2016). The nature of scientific knowledge: an explanatory approach. Switerland: Springer Verlag.
McComas, W. F. (1996). Ten myths of science: reexamining what we think we know about the nature of science. School Science and Mathematics, 96(1), 10–16. https://doi.org/10.1111/j.1949-8594.1996.tb10205.x.
McComas, W. F. (2002). The principal elements of the nature of science: dispelling the myths. In W. F. McComas, Nature of science in science education (pp. 53–70). Dordrecht: Springer.
McComas, W. F., Olson, J. K. (1998). The nature of science in international science education standards documents. In W. F. McComas, Nature of science in science education (pp. 41–52). Nature of science in science education (pp. 53–70). Dordrecht: Springer: Springer.
McComas, W. F., Clough, M. P., Almazroa, H. (2002). The role and character of the nature of science in science education. In W. F. McComas, Nature of science in science education (pp. 3–39). Nature of science in science education (pp. 53–70). Dordrecht: Springer: Springer.
Miller, D. (1972). The truth-likeness of truthlikeness. Analysis, 33(2), 50–55.
National Curriculum Council. (1988). Science in the national curriculum. York: NCC.
National Research Council. (2012). A framework for K-12 science education: practices, crosscutting concepts, and Core ideas. In H. Quinn, H. Schweingruber, & T. Keller (Eds.), Committee on conceptual framework for the new k-12 science education standards. Washington, DC: National Academies Press.
NCSESA, NRC, BSE, and DBSSE. (1995). National science education standards. Washington, DC: National Academies Press.
NGSSLead States. (2013). Next generation science standards: for states, by states. Washington, DC: National Academies Press.
Nozick, R. (1998). Invariance and objectivity. Proc Address Am Philosoph Assoc, 72(2), 21–48.
Olsson, E. (2014). Coherentist theories of epistemic justification. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (2014th ed.). Stanford: Stanford University.
Osborne, J., Collins, S., Ratcliffe, M., Millar, R., & Duschl, R. (2003). What ideas-about-science should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40(7), 692–720. https://doi.org/10.1002/tea.10105.
Pennock, R. T. (2011). Can’t philosophers tell the difference between science and religion?: demarcation revisited. Synthese, 178(2), 177–206. https://doi.org/10.1007/s11229-009-9547-3.
Popper, K. (1976). A note on verisimilitude. The British Journal for the Philosophy of Science, 27(2), 147–159.
Poston, T. (2014). Reason and explanation: a defense of explanatory coherentism. Houndmills: PalgraveMacmillan.
Pozo, J. I., & Crespo, M. (1998). Aprender y enseñar ciencia: del conocimiento cotidiano al conocimiento científico. Morata: Colección Pedagogía / Ediciones Morata series.
Psillos, S. (2000). The present state of the scientific realism debate. British Journal for the Philosophy of Science, 51(4), 705–728. https://doi.org/10.1093/bjps/51.4.705.
Psillos, S. (2005). Scientific realism: how science tracks truth. London: Routledge.
Quine, W. V. (1951). Two dogmas of empiricism. The Philosophical Review, 60(1), 20–43. https://doi.org/10.2307/2181906.
Resnik, D. B. (2000). A pragmatic approach to the demarcation problem. Studies in History and Philosophy of Science Part A, 31(2), 249–267. https://doi.org/10.1016/S0039-3681(00)00004-2.
Rudolph, J. L. (2000). Reconsidering the ‘nature of science’ as a curriculum component. Journal of Curriculum Studies, 32(3), 403–419. https://doi.org/10.1080/002202700182628.
Rutherford, F., & Ahlgren, A. (1991). Science for all Americans. New York: Oxford University Press.
Schickore, J. (2014). Scientific discovery. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (2014th ed.). Stanford: Stanford University.
Schommers, W. (1994). Space and time, matter and mind: the relationship between reality and space-time (Vol. 1). Singapore: World Scientific.
Smolin, L. (2007). The trouble with physics: the rise of string theory, the fall of a science, and what comes next. Boston: Houghton Mifflin.
Sokal, A. D., Bricmont, J. (1998). Intellectual impostures: postmodern philosophers’ abuse of science (pp. xiii-xiii). London: profile books.
Sumranwanich, W., & Yuenyong, C. (2014). Graduate students’ concepts of nature of science (NOS) and attitudes toward teaching NOS. Procedia-Social and Behavioral Sciences, 116, 2443–2452.
van Fraassen, B. C. (1980). The scientific image. Oxford: Oxford University Press.
Weber, E. U., & Stern, P. C. (2011). Public understanding of climate change in the United States. American Psychologist, 66(4), 315–328. https://doi.org/10.1037/a0023253.
Woit, P. (2007). Not even wrong: the failure of string theory and the continuing challenge to unify the laws of physics. London: Jonathan Cape.
Worrall, J. (1989). Structural realism: the best of both worlds? Dialectica, 43(1–2), 99–124. https://doi.org/10.1111/j.1746-8361.1989.tb00933.x.
Acknowledgments
This work was supported by ‘Fondo Nacional de Desarrollo Científico y Tecnológico’ FONDECYT under Grant No. 1151257, No. 1150661 and No. 1151169 and Dirección General de Investigación y Posgrado of Pontificia Universidad Católica de Valparaíso.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Romero-Maltrana, D., Benitez, F., Vera, F. et al. The ‘Nature of Science’ and the Perils of Epistemic Relativism. Res Sci Educ 49, 1735–1757 (2019). https://doi.org/10.1007/s11165-017-9673-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11165-017-9673-8