Abstract
Scientific objectivity and reliability are matters of fundamental importance both to science and in the public sphere, where they tend to be regarded with scepticism due to reporting of faulty or biased information, particularly in certain domains. In science studies, these qualities have been questioned in the light of two main characteristics of scientific activity: the inadequacy of data to determine theories, which opens the door to the possible influence of epistemic and value-based assumptions and prejudices, and the social character of scientific work, which may magnify their effect. Science education has a role to play in promoting informed trust in science and in improving science by preparing students to act positively either in the research field as future scientists or in the public domain as informed citizens. In this article, we systematize questions, responses, and arguments relating to the objectivity and trustworthiness of science that have been propounded in the philosophy, history, and sociology of science and which may contribute to an epistemologically sound conception of the subject in science education. We examine the kinds of bias that can affect science, the mechanisms available for dealing with them, and the arguments against treating them as a basis for blanket scepticism. The positions and arguments examined in this analysis generally imply that science is trustworthy and valuable, but confidence in science should be qualified, depending on the circumstances of each research program and the community that produces it, and they suggest means for improving the function and social role of science.
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Notes
The statement view sees scientific theories as sets of (linguistic) statements (basically axioms and principles) that immediately describe the real world. The validity of those statements is then assessed in terms of the formal logical-empiricistic notions of truth and rationality (see Giere, 2001). The model-based view sees scientific theories more as sets of (theoretical) models that mediate the application of theory to the real world and axioms as providing the basis for the construction of these models. Models, being idealized and perspectival constructions, cannot in a strict sense be true; their evaluation is based instead on the more moderate and pragmatic concepts of similarity or fit, i.e. how similar the models are to, or how well they fit, the real systems (see Giere, 1999, 2001; Develaki 2017).
A characterization of non-science ensues from the different demarcation criteria proposed in the course of the elaboration of this topic (see e.g. de Filipe 2021). Very generally, we might say that non-science lacks basic elements that typically characterize science (e.g. a sound evidential basis) and that the supporting of non-scientific theories/explanations implies a denial of science, while pseudoscience does not deny science, but rather presents its theories as scientific. In Fernandez-Beanato, (2021) pseudoscience is defined ‘as a non-scientific doctrine or field that is presented by its theoreticians or practitioners as if it were scientific or a science or that is easily taken to be so by a given epistemic community’ (p. 4). On the other side, de Felipe, (2021) explains why a single demarcation criterion (e.g. the logical-empiricist verifiability of scientific claims proposed by the Vienna circle, or their falsifiability proposed by Popper, 1959) is not sufficient, that scientificity is a matter of degree, and thus that an alternative, more nuanced account of demarcation is needed. He also notes that philosophers have suggested in consequence, beyond empirical evidence, a multiplicity of demarcation criteria, the most important of these being the theoretical coherence of a theory (the coherence of its basic tenets and their compatibility with other scientific theories/knowledge), and its explanatory strength/scope. (In Sect. 4.2, we also refer to an important demarcation criterion.)
Induction in the empirical sciences means the inference of universal statements about a phenomenon or system from a limited number of specific observations and/or experiments (e.g. inferring the laws of gases from relevant observations with some gases). Deduction in the empirical sciences means the derivation of specific conclusions (predictions) about a phenomenon from a general principle/hypothesis/model, which are then compared to empirical data to check the validity of the initial general principle/hypothesis/model from which they were deduced: For example, the value of the lunar period, which is calculated (deduced) from the law or principle of universal gravitation, is compared to the value determined by observations with a telescope.
Goldman 1999 makes it clear that with his book ‘Knowledge in a social world’, he aims to offer a social theory, which takes full account of ‘the interpersonal and institutional contexts in which most knowledge endeavors are actually undertaken’ (p. vii), and by considering that ‘social practices can make both positive and negative contributions to knowledge’, to show ‘just which social practices, under what conditions, will promote knowledge rather than subvert it’ (p. viii). Although in the field of social epistemology there are a variety of approaches and priorities, they share a common emphasis on the social aspect of knowledge and the fact that the social character of knowledge does not necessarily imply bias and non-objective statements.
The peer review checks the correctness, quality, and importance of a piece of work before it is published. (It has been observed that peer reviews are often affected by biases relating for example to the ethnicity, language, institution/prestige of the author(s), and proposals for improving the process have been made (Lee et al., 2013; Oreskes, 2019).) Reproducibility, or replicability, is more focused on identifying faulty results/claims. Research findings are reproducible or replicable when they can be repeated with (roughly) the same results by others in other laboratories (see e.g. Longino, 1990; Alger, 2020; Ioannidis, 2005; Bruner & Holman 2019).
Goldman comments on examples of primate research. From a study with savannah baboon troops, primatologists concluded ‘that the males’ behaviour was the crucial determinant of social cohesion [of the troop], and inferred that this is the paradigm of all primate social life’, an inference obviously prompted by ‘culture-imbued assumptions about aggressive males’’. However, in a later study of all the individuals in a troop, and not just its larger and more prominent members, they observed cases of other types of primates where both male and female ‘maintain social cohesion, and cooperation rather than aggression is the rule’. Similarly, the once standard biologists’ view of sexual preferences and behaviours (‘that males are prone toward promiscuity but females are coy and choosy—a strikingly Victorian perspective’) was initially rooted in studies of a single species, the Drosophila, the results of which were then extrapolated for all of nature (p. 238).
In her first study of research into human evolution and the role of (gonadal) hormones in sex differences (relating to behaviour, temperament, and cognition), Longino examines how gender ideologies may insert background assumptions to make data evidence for these assumptions. For example, in relation to human descent, in the (androcratic) ‘man-the-hunter’ assumptions, data (here the use of tools) were evidence of hunting by males, and, in the (gynecratic) ‘woman-the-gatherer’ assumptions, the same data were evidence of food collecting and preparation by females or for their protection by gathering. In her second study, comparing two research programs on the function of the brain in human behaviour (one from neuroendocrinology and the other from neurophysiology), she examines how contextual values can motivate the ‘acceptance of global, frameworklike assumptions that determine the character of research in an entire field’ (p. 104). In the first program, the interest focused on the (causal) role of (fetal) hormones on behaviour (carrying out tests with animals on the effect of the hormones), while in the second, the focus was on constructing a model of the role of the brain (its structure and development) for explaining higher cognitive behaviours/performances.
Oreskes examines the case of the Limited Energy Theory, popular in the late nineteenth century, which held that women should not participate in higher education because energy expended on study would adversely affect their fertility. Dr. Mary Putnam Jacobi criticized the theory in the same period, pointing out its empirical inadequacy and methodological errors, including scant evidence and biased sample, and adding that the popularity of this work ‘could be attributed to many interests besides those of scientific truth’ (cited in Oreskes, 2019, 79). Oreskes also studied the case of eugenics, a social movement, developed in the early part of the twentieth century in the USA and Western Europe with the intention of improving the ‘human race’ through directed changes in the composition of its genetic material (i.e. to promote the fertility of the ‘best’, the ‘fit’, and decrease the birth rate among the ‘unfit’). Its presumed scientific basis was the belief, then current, in heredity in biology (i.e. that genes controlled a whole series of physical characteristics and diseases, even behaviours and mental illnesses) coupled with Darwin’s theory of evolution by natural selection. This movement involved a variety of participants, diverse ‘values and motivations’, and social practices from tax incentives and family allowances to sterilization laws. Oreskes stresses that there was no community consensus on eugenicist claims and describes the objections raised by many scientists and geneticists who pointed out the thin empirical basis for eugenicist claims, the ignoring of evidence that did not support them, and the ignorance of a basic research direction that supported both genetic and environmental aspects, instead of genetic determinism, are shaping especially complex behaviours and character traits.
Bruner and Holman (2019) describe the situation that arises from publication bias in the case of a drug that has no therapeutical effect. The standard research method involves two groups—the treatment group which is exposed to the drug and the control group which is not—so as to permit assessment of the treatment effect. A simple statistical test can determine whether the treatment effect is statistically significant—i.e., whether the null hypothesis that the effect is the same for both groups can be rejected. Assuming that scientists only publish significant (and positive) results which show that the drug works (is efficient), then ‘the community as a whole will form incorrect beliefs about the actual effect of the drug’ (p. 95).
Giere et al. (2006) explain that the initial triple helix model was rejected because it did not correctly predict the amount of water that the DNA molecules were observed to retain in Franklin’s experiments with DNA samples (negative evidence). The double helix model that was proposed in its stead was selected not only because it gave a correct prediction for the quantity of water but chiefly because it correctly predicted the X-ray image of DNA (positive evidence), which could not be predicted with any other possible alternative model, while the prediction for the correct quantity of water could have been extracted from alternative models (e.g., from appropriately adjusted triple helix models).
Nature of science concerns the nature of scientific methods and knowledge and the socio-cultural dimension of science. Basic aims of scientific literacy are the acquisition of a body of scientific knowledge, a set of scientific skills and ways of thinking, and an understanding of the nature of science (NOS), plus the ability to use them in addressing science-related problems of the age.
With regard to the ethical responsibility of the scientist to improve the state of science and its social role, Kitcher (2001), for example, suggests that the scientist who becomes aware that the project he/she is part of is socially harmful has the obligation: to tell the truth, e.g. that the promises of a cure are illusory; to provoke open discussion for public understanding of the subject; ‘to think creatively about forms of education in his research field’; or even to ‘abandon, very publicly, his/her funding, and channel her efforts towards other forms of research’ (p. 197). Oreskes (2019) points out how important it is for academic scientists to pay attention to ‘who is funding their science and to what ends, to insist in all circumstances of full disclosure of that funding, and to reject any grants or contracts that involve non-disclosure or non-publication agreements’ (p. 242).
Certain lists of ideas/items about the nature of science (NOS), containing ideas such as that scientific knowledge is tentative, empirically based, theory-laden, and socio-culturally embedded (see, e.g. McComas 2008; Lederman 2006; Hodson 2014) have been created for teaching the NOS. The lists have been criticized on the grounds, among other things, that their NOS ideas present an unchanging and too generalized picture of science, ignoring the discipline-specific nature and context dependency of scientific work (see in this regard, e.g. Gilbert & Justi 2016; Wong & Hodson 2009; Abd-El-Chalick 2012). One proposed alternative to NOS lists is the family resemblance approach (Irzik & Nola 2011), which presents science as a discrete system whose basic features are organized in two dimensions, that of ‘science as cognitive-epistemic system of thought and practice’ and that of ‘science as social-institution system’ (the professional, ethical, and social characteristics and standards of science).
Science education has examined argument and argumentation from different epistemological and educational perspectives (see Erduran & Jimenez-Aleixandre 2007; Erduran et al. 2004; Sampson & Clark 2008; Adúriz-Bravo 2011; Develaki 2017, 2019, 2020). ‘Argumentation is a critically important discourse process in science and it should be taught and learned in the science classroom as part of scientific inquiry and literacy’ (Erduran et al. 2015, p. 1). Model-based teaching, teaching with and about models, has for a number of years now constituted an important and challenging approach in science education. The research on model-based teaching suggests that modelling activities can enhance the acquiring of knowledge, abilities, and epistemologies that reflect real science (Schwarz & White 2005; Gilbert & Justi 2016) and describes modes of model-based inquiry teaching and learning (see, e.g. Halloun 2004, 2007; Clement & Rea-Ramirez 2008; Oh & Oh 2011; Adúriz-Bravo & Izquierdo-Aymerich, 2009; Gilbert & Justi 2016; Develaki, 2007, 2016, 2020).
References
AAAS (American Association for the Advancement of Science) (1993). Benchmarks for science literacy. New York: Oxford University Press
Abd-El-Khalick. (2012). Examining the sources for our understanding 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
Adúriz-Bravo, A. (2011). Fostering model-based school scientific argumentation among prospective science teachers. US-China Education Review, 8(5), 718–723.
Adúriz-Bravo, Α, & Izquierdo-Aymerich, Μ. (2009). A research-informed instructional unit to teach the nature of science to pre-service science teachers. Science & Education, 18, 1177–1192.
Alger, B. E. (2020). Is reproducibility a crisis for science? In M. Matthews (Ed), HPS&ST Newsletter, Opinion Page, Feb. 2020. (https://www.hpsst.com/uploads/6/2/9/3/62931075/2020feb.pdf)
Barnes, B. (1977). Interests and the growth of knowledge. Routledge.
Boyd, R. N. (1983). On the current status of the issue of scientific realism. Erkenntnis, 19(45), 45–90.
Boyd, R. N. (1992). Constructivism, realism, and the philosophical method. In J. Earman (Ed.), Interference, explanation, and other frustrations, Essays in the Philosophy of Science (pp. 131–199). University of California Press.
Bruner, J. P., & Holman, B. (2019). Self-correction in science: Meta-analysis, bias and social structure. Studies in History and Philosophy of Science, 78, 93–97.
Bybee, R. (1997). Achieving scientific literacy: From purposes to practices. Portsmouth
Clement, J. J., & Rea-Ramirez, M. A. (Eds.). (2008). Model based learning and instruction in science. Springer.
Collins, H. (1983). An empirical relativist programme in the sociology of scientific knowledge. Science Observed: Perspectives on the Social Study of Science. Sage, pp. 115–140.
de Felipe, I. Ο. (2021). The Universality of Science and Traditional Chinese. Medicine. Science & Education, https://doi.org/10.1007/s11191-021-00249-4
Develaki, M. (2007). The model-based view of scientific theories and the structuring of school science programmes. Science & Education, 16(7), 725–749
Develaki, M. (2012). Integrating scientific methods and knowledge into the teaching of Newton’s theory of gravitation: an instructional sequence for teachers’ and students’ nature of science education. Science & Education, 21, 853–879.
Develaki, M. (2016). Key aspects of scientific modeling exemplified by school science models: some units for teaching contextualized scientific methodology. Interchange, 47(3), 297–327.
Develaki, M. (2017). Using computer simulations for promoting model-based reasoning. Epistemological and educational dimensions. Science & Education, 26, 1001–1027.
Develaki, M. (2019). Methodology and epistemology of computer simulations and implications for science education. Journal of Science Education and Technology, 28(4), 353–370.
Develaki, M. (2020). Comparing crosscutting practices in STEM disciplines. Modeling and reasoning in mathematics, Science, and Engineering. Science & Education, 29, 949–979.
Devitt, M. (1991). Realism and truth (2nd ed.). Blackwell
Duhem, P. (1991). The aim and structure of physical theory (2nd ed,). Princeton University Press
Erduran, S., Ozdem, Y., & Park. (2015). Research trends on argumentation in science education: A journal content analysis from 1998-2014. International Journal of STEM Education 2, 5
Erduran, S., & Jimenez-Aleixandre, M. P. (Eds.). (2007). Argumentation in science education: Perspectives from classroom-based research. Springer.
Erduran, S., Simon, S., & Osborne, J. (2004). TAPing into argumentation: Developments in the application of Toulmin’s argument pattern for studying science discourse. Science Education, 88(6), 915–933.
Estany, A., & Casacuberta, D. (2012). Contributions of socially distributed cognition to social epistemology. EIDOS, 16, 40–68.
Fernandez-Beanato, D. (2021). Feng Shui and the demarcation project. Science & Education (2021). https://doi.org/10.1007/s11191-021-00240-z
Gasparatou, R. (2017). Scientism and scientific thinking. Science & Education, 26, 799–812.
Giere, R. N. (1999). Science without laws. University of Chicago Press
Giere, R. Ν. (2001). A new framework for teaching scientific reasoning. Argumentation, 15(1), 21–33.
Giere, R. N., Bickle, J. & Mauldi, R.F. (2006). Understanding Scientific Reasoning. Thomson Wadsworth
Giere, R. (2007). Distributed cognition without distributed knowing. Social Epistemology, 212(3), 313–320.
Gilbert, M. (2000). Collective belief and scientific change. In M. Gilbert (Ed.), New Essays in Plural Subject Theory (pp. 37–49). Rowman & Littlefield.
Gilbert, J. K., & Justi, R. (2016). Modelling-based teaching in science education. Springer.
Goldman, A. I. (1999). Knowledge in a social world. Oxford University Press.
Goldman, A. I. (2001). Experts: Which ones should you trust? Philosophy and Phenomenological Research, LXIII, 1, 85–110.
Halloun, I. A. (2007). Mediated modeling in science education. Science & Education, 16, 653–697.
Halloun, I. A. (2004). Modeling theory in science education. Kluwer Academic Publishers.
Hanson, N. R. (1958). Patterns of discovery: An inquiry into the conceptual foundations of science. Cambridge University Press.
Harding, S. (1986). The science question in feminism. Cornell University Press.
Hodson, D. (1992). In search of a meaningful relationship: An exploration of some issues relating to integration in science and science education. International Journal of Science Education, 14, 541–562.
Hodson, D. (2014). Nature of science in the science curriculum: Origin, development, implications and shifting emphases. In M. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 911–970). Springer.
Hull, D. (1988). Science as a process: An evolutionary account of the social and conceptual development of science. University of Chicago Press.
Hume, D. (1902). An enquiry concerning human understanding. 2nd ed., L. A. Selby-Biggie, (Ed.). Oxford University Press
Ioannidis, J. P. A. (2005). Why most published research findings Are False. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124
Ioannidis, J. P. A., Fanelli, D., Dunne, D. D., & Goodman, S. N. (2015). Meta-research: Evaluation and improvement of research methods and practices. PLoS Biology, 13(10), e1002264. https://doi.org/10.1371/journal.pbio.1002264:1-7
Irzik, G., & Nola, R. (2011). A family resemblance approach to the nature of science for science education. Science & education, 20(7–8), 591–607.
Jukola, S. (2016). The commercialization of research and the quest for the objectivity of science. Foundations of Science, 21(1), 89–103.
Kitcher, P. (2001). Science, truth, and democracy. Oxford University Press.
Krosnick J. A. (2019). ‘Comments on the present and future of science, inspired by Naomi Oreskes. In Oreskes’, N. (ed), Why Trust Science ? (pp. 202–2011).
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press (2nd ed. 1970)
Kuhn, T. S. (1970). Logic of discovery or psychology of research. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 1–24). Cambridge University Press.
Kuhn, T. S. (1977). The essential tension: Selected studies in scientific tradition and change. University of Chicago Press.
Lakatos & A. Musgrave (Eds.) (1970). Criticism and the growth of knowledge. Cambridge University Press
Laudan, L. (1981). A confutation of Convergent Realism. Philosophy of Science, 48(1), 19–49.
Laudan, L. (1990). Demystifying underdetermination, In C. Wade Savage, ed., Minessota Studies in the Philosophy of Science, vol. xiv. University of Minnesota Press
Laudan, L., & Leplin, J. (1991). Empirical equivalence and underdetermination. The Journal of Philosophy, 88(9), 449–472.
Lederman, N. G. (2006). Syntax of nature of science within inquiry and science instruction. In L. B. Flick & N. G. Lederman (Eds.), Scientific inquiry and nature of science (pp. 301–317). Springer.
Lee, C. J., Cassidy, R., Sugimoto, G. Z., & Blaise, C. (2013). Bias in peer review. Journal of the American Society for Information Science and Technology, 64(1), 2–17.
Longino, H. E. (1990). Science as social knowledge. Princeton University Press.
Longino, H. E. (2002). The fate of knowledge. Princeton University Press.
Matthews, M. R. (ed) (2019). Feng shui: Teaching about science and pseudoscience. Springer
McComas, W. F. (2008). Seeking historical examples to illustrate key aspects of the nature of science. Science & Education, 17(2–3), 249–263.
NGSS Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press.
Niniluoto, I. (2015). Social Aspects of scientific knowledge. Synthese, 197, 447–468.
NRC (National Research Council). (1996). National science education standards. National Academy Press.
NRC (National Research Council). (2012). A framework for K-12 science education. Practices, crosscutting concepts, and core ideas. National Academy Press
Oh, P. S., & Oh, S. J. (2011). What teachers of science need to know about models. International Journal of Science Education, 33, 1109–1130.
Oreskes, N. (2019). Why trust science? Princeton University Press.
Popper, K. R. (1959). The logic of scientific discovery. Hutchinson.
Quine, Willard. V. O. (1951). Two dogmas of empiricism. Philosophical Review 60(1), 20-43
Sampson, V., & Clark, D. B. (2008). Assessment of the ways students generate arguments in science education: Current perspectives and recommendations for future directions. Science Education, 92(3), 447–472.
SCED (Science & Education) (2020). Nature of STEM. Science & Education 29 (4) (SI)
Schwarz, C. V., & White, B. Y. (2005). Meta-modeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.
Shapin, S. (1982). The history of science and its sociological reconstruction. History of Science, 20, 157–211.
Solomon, M. (1992). Scientific rationality and human reasoning. Philosophy of Science, 59, 439–455.
Suppe, F. (1977). The structure of scientific theories (2nd ed.). University of Illinois Press.
Tatsioni, A., Bonitsis, N. G., & Ioannidis, J. P. (2007). Persistence of contradicted claims in the literature. JAMA, 298(21), 2517–2526. https://doi.org/10.1001/jama.298.21.2517
Wong, S. L., & Hodson, D. (2009). From the horse’s mouth: What scientists say about scientific investigation and scientific knowledge. Science Education, 93, 109–130.
Zeidler, D. L., Herman, B. C., & Sadler, T. D. (2019). New directions in socioscientific issues research. Disciplinary and Interdisciplinary Science Education Research, 1, 11.
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Develaki, M. Trustworthiness of Science in Debate: Challenges, Responses, and Implications. Sci & Educ 31, 1181–1208 (2022). https://doi.org/10.1007/s11191-021-00300-4
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DOI: https://doi.org/10.1007/s11191-021-00300-4