This is an Accepted Manuscript of an article published by Taylor & Francis in International Studies in the Philosophy of Science on 05/22/2020, available online: https://www.tandfonline.com/doi/full/10.1080/02698595.2020.1767891.
Fasce, A. (2020). Are Pseudosciences Like Seagulls? A Discriminant Metacriterion Facilitates the Solution of the Demarcation Problem. International Studies in the Philosophy of Science. doi: 10.1080/02698595.2020.1767891.
In this article, I develop a philosophical framework, or “metacriterion”, for the demarcation of pseudoscience. Firstly, “gradualist demarcation” is discussed in depth, considering an approach to the demarcation problem that presupposes the existence of a spectrum between science and pseudoscience; six general problems are found by means of this analysis. Secondly, based on the subsequent discussion of these problems, a discriminant metacriterion composed of four philosophical requirements is proposed. Lastly, it is shown that this metacriterion is able to guide the development of a workable and well-founded demarcation criterion for pseudoscience.
Keywords: Pseudoscience, demarcation problem, demarcation criterion, family resemblance.
Interest in the demarcation problem is undergoing a boom after being shelved and even given up for dead. Nevertheless, despite current philosophical discussions, there are no substantial advances in the development of a functional demarcation criterion capable of achieving academic consensus. In my view, this is due to two reasons. On one hand, some scholars still engage in time-consuming, unproductive discussions on already discarded demarcation criteria, such as falsifiability. On the other hand, the, currently, most disseminated metacriterion — the general philosophical requirements for a demarcation criterion of pseudoscience to be deemed satisfactory —, which I call here “gradualist demarcation”, does not solve the shortcomings of prior approaches. Therefore, in this article I will develop an alternative, discriminant metacriterion that is able to deal with the traditional problems of the philosophical definition of pseudoscience.
Firstly, gradualist demarcation will be defined based on the analysis of two relevant proposals: the first, raised by Fred Gruenberger in 1962, and a more recent one, by Massimo Pigliucci in 2013. This analysis is pertinent given that it will show the conceptual evolution between both authors and, furthermore, it will bring out the most remarkable general problems of demarcation projects. Secondly, these general problems will be discussed in greater depth, taking account of the underlying conceptual issues that explain their persistence. Finally, a discriminant metacriterion will be presented: this metacriterion, comprising four requirements, is able to avoid, or at least to palliate, the aforementioned flaws, setting the theoretical basis for developing a well-grounded and workable demarcation criterion. Lastly, the current philosophical and scientific outcomes of this metacriterion are listed in order to show its fruitfulness.
Gradualist demarcation through two instances
Beyond the two examples to be analysed here, gradualist demarcation is defended by several scholars (e.g. Mahner, 2013; Dawes, 2018), so there are many demarcation projects that fit, in general terms, with this philosophical framework (e.g. Ruse, 1982; Beyerstein, 1995; Lilienfeld, Ammirati, & David, 2012). This approach assumes that the demarcation of pseudoscience is a matter of degree, a continuum that goes from the most reliable science to the most unreliable pseudoscience, with soft science, proto-science, and bad science within the spectrum. In this regard, it aims to offer a fuzzy answer to the demarcation problem: something could be pseudoscience to a certain extent, depending on how it fits with the universe of variables selected as a demarcation criterion. This strategy, according to its defenders, finds its main advantage in leaving aside necessary and sufficient conditions — as was demanded by, among others, Laudan (1983). Accordingly, gradualist conceptions consider that the denial of the vagueness of scientificity, and the consequent adoption of burdensome requirements such as necessary and sufficient conditions, would largely explain why philosophers of science have not been able to offer a consensual and operational demarcation criterion for pseudoscience.
Gruenberger (1964) was sceptical about the philosophical viability of the demarcation problem, arguing that “the best we can do is offer a checklist of some of the attributes of science and of the crackpots to help in making this decision” (Gruenberger, 1962, p. 2). Following this path, he chose thirteen variables, so the best of the sciences would meet all of them whereas pure foolishness would not meet any. Additionally, an in-between score makes diagnosis dependent on subjective assessment based on a context-dependant threshold. The complete list of characteristics comprises: public verifiability (12 points), predictability (12), controlled experiments (13), Occam’s razor (5), fruitfulness (10), authority (10), ability to communicate (8), humility (5), open-mindedness (5), the Fulton non sequitur (underrated genius; 5), paranoia (5), the dollar complex (excessive esteem of one’s ideas; 12), and statistics compulsion (5).
When considering Gruenberger’s list, there is one thing, in particular, that should draw the attention of philosophers: theoretical arguments in order to justify it are explicitly relativised — “these are some significant items which we think are among the main attributes of the scientist” (Gruenberger, 1962, p. 4; italics are mine). Moreover, the scores of the variables are also arbitrary and he, again, shows no qualms over this: “the scores are personal, arbitrary, and biased. The reader is urged to fill in his own values, rather than to waste time quibbling over mine. I cannot defend any precise values (indeed, if I were to fill out the sheet again, I would probably have different values)” (Gruenberger, 1962, p. 12). Henceforth, I will call this issue “the problem of the weight of the variables”.
Similarly, his implementation of the criterion follows a corresponding logic: dowsing gets 28 points, extrasensory perception gets 38, and physics gets 97. But Gruenberger assessed the field of physics as a whole. This is problematic because physics is heterogeneous regarding Gruenberger’s criteria; for example, some of its branches, such as particle physics, have strong limitations in conducting controlled experiments. Also, philosophers can choose to demarcate several dimensions of science, such as logical-methodological issues (Wilson, 2000), propositions (Popper, 1963), fields of knowledge (Bunge, 1982; Thagard, 1988), theories (Kitcher 1982; Lugg, 1987), and research programmes (Lakatos, 1978a) — henceforth, I will refer to these dimensions using the overall concept of “units of demarcation”. Additionally, we don’t know on what basis he measured such vague concepts as humility, paranoia, parsimony, and the ability to communicate.
To make matters worse, a detailed analysis of the structure of the check-list reveals that a unit of demarcation that is authoritarian, fruitless, not able to predict, and does not conduct controlled experiments, could get as questionable a score as 55 points of scientificity, depending on how we define some terms of the criteria — in fact, Gruenberger considers that extrasensory perception (38 points) would belong to “the middle group, still open to debate” (Gruenberger, 1962, p. 12). The final shortcoming is of a practical nature: what to do if a unit of demarcation gets 55 or 38 points of scientificity? Do we fund research projects based on it? Do we include it in the education and judicial systems or, instead, we must discredit it? Gruenberger does not offer us any recommendation in this regard, which defines his criterion not only as easy to dodge but also as problematic for intersubjective decision-making.
Pigliucci (2013) has developed a much more contemporary approach to gradualist demarcation. Again, the conceptual basis from which he departs is the spectrum that would make up science, soft science, proto-science, and pseudoscience — a spectrum that would shape fuzzy conceptual clusters instead of clearly delineated concepts. Pigliucci rejects necessary and sufficient conditions for demarcation by considering them “old-fashioned” (Pigliucci, 2013, p. 19). Instead, he asserts that these assumptions “ought to give pause at least since Ludwig Wittgenstein’s talk of family resemblance concepts” (Pigliucci, 2013, p. 19). Accordingly, as in Toulmin (1972), Dupré (1993), and Irzik and Nola (2011), Pigliucci explicitly interprets the demarcation problem through the Wittgensteinian notion of family resemblance: “Demarcation should not be attempted on the basis of a small set of individually necessary and jointly sufficient conditions because ‘science’ and ‘pseudoscience’ are inherently Wittgensteinian family resemblance concepts” (Pigliucci, 2013, p. 25).
The analogy between the science/pseudoscience distinction and species is also of great relevance within Pigliucci’s conception of demarcation — although analogies are not, stricto sensu, arguments. He considers that the answer for the philosophical elucidation of pseudoscience should be similar to the one he offered in the past regarding the concept of “species”, which, following Hull (1965) and Templeton (1992), he characterizes as a Wittgensteinian cluster-type concept (Pigliucci, 2003). One classical example of this idea is the controversial characterization of herring gulls as a ring species (Irwin, Irwin, & Price, 2001): as these seagulls usually hybridize between nearby populations, it would be possible to observe a chain of hybridizations that ends up in two populations with greater problems in producing offspring. So, even though ring species show two clearly recognizable extremes (science and pseudoscience in Pigliucci’s analogy), they are unified by a continuum of hybrid individuals that are more or less of the same species. Hence, if in order to assess whether two individuals are or are not of the same species, we should consider a complex universe of variables — such as reproductive compatibility, evolutionary proximity, morphological similarity, and ecological behaviour —, the same should be done in order to distinguish between science and pseudoscience.
Pigliucci does not extract complete results from his ideas that can be considered a comprehensive demarcation criterion. Instead, he selects two initial variables ― theoretical understanding and empirical knowledge ― in order to show the way forward to future authors willing to finish his demarcation project. Despite this, he considers these variables as already able to offer substantially nuanced demarcation of science, proto-science, soft science, and pseudoscience (Figure 1).
Pigliucci’s criterion used on a sample of units of demarcation.
Note: from Pigliucci (2013, p. 23).
An in-depth analysis of Figure 1 reveals several philosophical shortcomings:
1) The “relevance criterion” used by Pigliucci in order to justify his variables selection is, in his words, “science attempts to give an empirically based theoretical understanding of the world” (Pigliucci, 2013, p. 22). On why he specifically selects these two features instead of others ― i.e., why he does not select variables based on some other relevance criterion such as “science attempts to develop effective technology based on prediction and manipulation” ―, Pigliucci argues that “we have to start from somewhere” (Pigliucci, 2013, p. 22). Again, we find the same issue of a lack of theoretical foundations to justify variables selection, as with Gruenberger’s criterion. Hereafter, I will call this issue, referring to which characteristics are relevant and which are not for demarcation purposes, as the “problem of the relevance criterion”.
2) On what basis has Pigliucci drawn the borderlines of the clusters? In a cluster analysis carried out in a satisfying way ― that is, in which clear clusters are observed despite the existence of a spectrum ―, we must cut off the clusters somewhere. In this case, Pigliucci places the cluster of pseudoscience in the lower left corner, that of science in the upper right, that of soft science in the upper left, and that of proto-science in the lower right. Nevertheless, considering that these borderlines are the crucial outputs of demarcation, Pigliucci offers little argumentation aimed at justifying them. Accordingly, gradualist demarcation does not prevent the proliferation of relativised borderlines for pseudoscience ― although defining economics or string theory as pseudosciences, or astrology as soft science, are momentous decisions. The scope of pseudoscience should not be left to a borderline that can be moved from one place to another without theoretical resistance, as it would entail a lack of normative power.
The “normative power” of a given demarcation criterion is defined in terms of justifications, standards, and norms directly derived from its acceptance. Normative force emerges from its capacity to offer an intersubjective definition of pseudoscience, acceptance of which compels individuals to acknowledge and put into practice specific rational-based behavioural prescriptions, thus, setting constraints during philosophical analysis and pragmatic decision-making. Hence, within a normative framework the demarcation criterion of pseudoscience should fit with the general definition of “public reason” (Torcello, 2020), and contradictory outcomes of the same criterion should be regarded as inconsistent. However, when a demarcation criterion lacks normative power, contradictory conceptions and decisions can be consistently and justifiably derived from it – i.e. mutually contradictory propositions could be legitimately derived from the same criterion because that criterion allows, or is based on, “subjective” assessment. Consequently, the borderline between science and pseudoscience would be greatly influenced by contingent beliefs and values, either idiosyncratic or shared by a certain group, society, or culture. From here on, I will refer to this issue as the “problem of normative power”.
3) What does Pigliucci mean by “theoretical understanding”, and why string theory has the most? He briefly defines the concept as “internal coherence and logic” (Pigliucci, 2013, p. 22), in that, although “astrologers certainly can produce ‘theoretical’ foundations for their claims, these quickly turn out to be both internally incoherent and, more damning, entirely detached from, or in contradiction with, very established notions from a variety of other sciences (particularly physics and astronomy, but also biology)” (Pigliucci, 2013, p. 24). Accordingly, one could criticize a particular pseudoscience because, even if internally coherent, it contradicts robust scientific theories ― a classic but controversial demarcation strategy (Toulmin, 1984). Nevertheless, if internal and external coherence are to be regarded as independent variables when assessing the quality of theoretical understanding, they should be independently included in the demarcation criterion of pseudoscience.
Additionally, what does Pigliucci mean by “understanding”? He considers, as the best example, a hypothesis that is not supported by empirical evidence — in fact, it is controversial if some of the basic assumptions of string theory are testable (Smolin, 2007; Woit, 2007). Pigliucci does not mention classical characteristics of scientific explanations, such as causal links, models, and mechanisms, whose inclusion would substantially increase the number of independent variables. Furthermore, the concept of understanding (de Regt, 2017) is not elucidated enough to grasp the differences between real understanding and overconfidence bias (e.g. Kruger & Dunning, 1999; Rozenblit & Keil, 2002). Something similar can be said on the other variable, “empirical knowledge”, also briefly defined as “empirical support” (Pigliucci, 2013, p. 22). I will refer to these issues as “the problem of defining variables” and “the problem of measuring variables”.
4) What type of units of demarcation are within the scope of this criterion? Pigliucci refers to his sample of units as “disciplines and notions” (Pigliucci, 2013, p. 23), but the selection is not convincing. Firstly, the demarcation of economics, physics and psychology as a whole is philosophically problematic. For example, the theoretical understanding provided by personality psychology, which works almost entirely on the basis of constructs (Fried, 2017), could be considered as lower than that of psychobiology, which includes biological mechanisms that underlie behaviours — this criticism also applies to other authors who demarcate entire fields of knowledge, such as Bunge (1982) and Thagard (1988). Secondly, Pigliucci’s sample includes a unit usually considered as a branch of psychology, namely evolutionary psychology, that resides in a different cluster, as well as an uncommon concept such as “scientific history”, whose demarcation seems to be begging the question — besides, scientific history is classified as proto-science. Henceforth, I will call this issue “the problem of the scope” — that is, the problem regarding what can be assessed by the demarcation criterion of pseudoscience and what is beyond its reach.
All the aforementioned problems can be found in gradualist demarcation projects, albeit they are not exclusive of this approach. In sum, these problems are:
(a) The problem of the scope.
(b) The problem of defining the variables.
(c) The problem of the relevance criterion.
(d) The problem of the weight of the variables.
(e) The problem of measuring the variables.
(f) The problem of normative power.
Three misconceptions of gradualist demarcation
In this section, I will discuss three misconceptions that lie at the root of the aforementioned problems: science as an improved version of pseudoscience, Wittgensteinian family resemblance as a philosophical background for demarcation, and gradual demarcation as an approach that facilitates pragmatic decision-making.
Science is not an improved version of pseudoscience
The basic problem underlying gradualist demarcation is a misunderstanding of the objectives of the demarcation problem as a philosophical endeavour, as it consists of delimiting the borderlines of science as a set of fields and research programs (Hansson, 2017), thus separating science from what is not. Nevertheless, gradualist demarcationists often overstate the scope of the demarcation problem by including the recognition of limitations and strengths among legitimate fields of knowledge — which is not to demarcate but to establish quality parameters.
In this regard, Boudry (2013) has made an interesting distinction between unfeasible and uninteresting “territorial demarcation” between science and other epistemic activities, and “normative demarcation” between science and pseudoscience, eminently worthy of philosophical attention. So, the existing differences between science, proto-science, soft science, philosophy, humanities and the like do not belong in the demarcation of pseudoscience. Contrastingly, gradualist projects hold, as an implicit assumption, that science and pseudoscience are of the same class – in that they are each a set of entities defined by a shared set of variables. That is to say, that they are clusters defined according to the same variables but with different quality levels. So, the demarcation problem would be in the general recognition of such quality levels, instead of the philosophical justification of why science and pseudoscience belong to different classes.
Furthermore, the arguments used in order to justify a gradualist approach to demarcation often assume that science and pseudoscience would be separated by a direct fuzzy borderline. Nevertheless, this latter assumption does not justify either Gruenberger’s scepticism about the demarcation problem or Pigliucci’s Figure 1, in which science and pseudoscience have no direct frontier ― as border disputes are not transitive relations, the prototypical territories of proto-science and soft science generate a sharp distinction between the extremes of the spectrum. In other words, even though both extremes could have ambiguous limits with these in-between clusters, pseudoscience can be rightfully considered as not an overlapping class when compared to science. This non sequitur derived from their basic assumptions has been overlooked by gradualist projects; science and pseudoscience can be deemed different classes even if accepting a spectrum between them. In fact, the same can be said about many other cultural concepts. For example, there is no fuzzy borderline between science and pyramid schemes, phishing websites, and embezzlement ― taking into account that pseudoscience can be conceptualized as an intellectual misconduct (Blancke, Boudry, & Pigliucci, 2017; Fasce, 2017).
Resemblant characteristics such as the inability to predict or to offer proper explanations should not be interpreted in the same way among scientists and pseudoscientists. Experts in pancreatic cancer are almost always incapable of curing it and experts in fibromyalgia do not understand the etiology of the disease, but these limitations do not make them partially pseudoscientific ― oncologists and rheumatologists, like many economists, psychologists, and the SETI team, use the most reliable methods and base their ideas on the current corpus of scientific evidence. This leads to the question: if, for example, pseudoscientists make medical or psychological statements without controlling confirmation bias at all or appealing to tradition, whereas scientists are governed by completely different standards, how can a pseudoscientist improve these radically negligent behaviours without changing their nature? For instance: a complete lack of evidence or a priori untestable content cannot be improved in order to achieve scientific standards. A given hypothesis is or is not within the spectrum of evidence and refers or does not refer to potentially testable phenomena, yet metaphysical content cannot be improved to become natural phenomena and remain as metaphysical content. So, for demarcation purposes it would be more reasonable to compare between classes and not between individuals classified within a spectrum. This situation could explain why there are no historical cases of pseudosciences that evolved to become science, not even the oldest ones such as phrenology and homeopathy.
In addition, if gradualist demarcation does not consider that pseudoscientists would need to change the nature of their actions in order to achieve scientific standards, but merely improve their former behaviour, what solution do they offer for the problem of the scope? As science mimicry has been traditionally regarded as the cornerstone of pseudoscience (Hansson, 2009; Blancke, Boudry, & Pigliucci, 2017), the most promising option would be to consider only units that are publicly presented as scientific within the scope of the demarcation criterion of pseudoscience. Nevertheless, all mono-criterial approaches, and only two of the twenty-one demarcation criteria analysed in Fasce (2018), consider this to be one of the variables that define pseudoscience. In fact, science fiction or Greek mythology could be classified as pseudoscience by Pigliucci’s criterion because its scope is not restrictive enough — it does not require something to be publicly presented as scientific in order for it to be considered as pseudoscientific. Thus, an undesirable practical implication of fuzzy definitions by family resemblance is the confusion between pseudoscience and other types of unwarranted beliefs, such as paranormal and conspiracy theories (for a discussion on this psychometric issue see Fasce & Picó, 2019a).
Wittgensteinian family resemblance outlaws normative elucidation
The use of the Wittgensteinian conception of family resemblance (Wittgenstein, 1958; Pompa, 1967) as a solution to the demarcation problem is substantially more problematic than often suggested. Wittgenstein theorized that entities that are named using concepts of natural language are linked together by a “complicated network of similarities overlapping and criss-crossing: sometimes overall similarities, sometimes similarities of detail” (Wittgenstein, 1958, 32). A classic example — one that Wittgenstein usually uses in his arguments — is that of games (e.g. Wittgenstein, 1958, p. 31). We do not have a clear and outright definition of what a game is and what is not: in some games there is no winner, some are played alone and others by teams, some are played with balls, some are played with cards, and sometimes we use the term for activities that are not even related to fun — for example, when someone is “playing with your feelings”. For this reason, Wittgenstein did not consider it appropriate to carry out closed, sharp definitions of concepts, characterising them as a form of impoverished artificial language (Givón, 1986). Instead, concepts would be an intricate network of language-games whose meanings are contextual tools to perform regulated pragmatic interactions (Wittgenstein, 1958, pp. 26-27).
There is a classic problem about this conception, known as “wide-open texture” (Richman, 1962; Andersen, 2000). It is not easy to discard explicit and exhaustive definitions of concepts based on sufficient and necessary characteristics, because somehow everything is similar to anything. For example, unlike athletics, pickpocketing is not a sporting discipline; nevertheless, carrying out this distinction forces us to go beyond family resemblance. It can be said that pickpocketing: demands complex techniques which the pickpocket must practice to remain skilled in; is practiced as a means of earning a living; and can even be considered to have a winner and a loser. Moreover, this is also a problem for positive delimitations of kinds. For example, cousins are defined as the offspring of parents’ siblings, so this specific kinship ― not physical resemblance ― is the characteristic with normative power in order to decide if two persons are cousins or not. Hence, to use that kind of distinction we are compelled to prioritize certain relationships among the entities potentially described by a certain concept. Otherwise, the use of language would be unfeasible: the world, including the cultural one, has sharp categories that language must grasp to be functional.
It is worth examining Wittgenstein’s solution to the wide-open texture problem so as to reveal the counterintuitive implications of family resemblance within the context of demarcation. In order to develop his philosophical conception of meaning, he chose to combine definitions by family resemblance and a pragmatist theory of truth whose potential relativist nature has been widely discussed ― authors such as Williams (2007) and Coliva (2010) argue that his contextualism is not relativistic, although the relativistic interpretation of Wittgenstein has been particularly influential (Crayford, 1997; Boghossian, 2006).
The concept form of life is particularly relevant in this regard. It is defined as the set of language-games of a community of speakers that generates a shared worldview (Wittgenstein, 1958, p. 11). So, in this context, a form of life implies that “there must be agreement not only in definitions but also (queer as this may sound) in judgments” (Wittgenstein, 1958, p. 88). Language-games would not only constrain our range of potential interactions and expressive possibilities, but also the perception and consequent acceptance of facts: “’So you are saying that human agreement decides what is true and what is false?’ — It is what human beings say that is true and false, and they agree in the language they use. That is not agreement in opinions but in form of life” (Wittgenstein, 1958, p. 88). Wittgenstein’s relativism is only clearly limited by the rules that define language-games, which he considers to be almost immutable ― in fact, the evolution of these rules is one of the most problematic issues of his philosophical system (Kripke, 1982; Baker & Hacker, 1984). Hence, according to the relativist interpretation, and in a more restrained way for the contextualist one, an individual does not know facts, but understands a form of life defined by socially inculcated rules.
Taking all these characteristics of Wittgenstein’s philosophical conceptions into account, it is difficult to understand how Pigliucci and other authors directly influenced by him intend to base a demarcation criterion on ideas from which other readers, and not precisely marginal ones, have derived a rather radical relativism ― for example, Rorty’s neopragmatism (Boghossian, 2006). That is to say, pseudoscience can not be a Wittgensteinian family resemblance concept and, at the same time, be subjected to normative demarcation and decision-making. In fact, from this standpoint, the demarcation problem would not be a philosophical problem. Instead, it would be a descriptive issue ― the ethnographic study of a form of life ―, so the normative use of such demarcation criterion would commit a kind of semantic elitism already criticized by Lakatos (1978).
Perhaps the authors who seek to carry out the demarcation of pseudoscience by means of family resemblance definitions do not follow Wittgenstein in all his philosophical commitments. So, they could be considering this strategy as more promising merely due to the alleged fuzzy borderline between science and pseudoscience, thus leaving aside the futile nature of Wittgensteinian family resemblance for demarcation purposes ― although there is no convincing argument to support this interpretation. However, even accepting an interpretation of family resemblance detached from strong Wittgensteinian relativism, gradualist demarcation does not get rid of the challenge of establishing a criterion with normative power. The only difference is that it would be called “relevance criterion” and used to specify a set of variables among the overwhelming amount of resemblant characteristics of science and pseudoscience.
In sum, Wittgensteinian family resemblance banishes the demarcation problem from philosophy, whereas a weak interpretation of family resemblance has no deep implications ― it just moves the age-old demarcation problem from one place to another.
Gradualist demarcation hinders decision-making
The lack of normative power of gradualist demarcation is also a problem for pragmatic decision-making about pseudoscience as a social affair. The demarcation criterion of pseudoscience has a prominent role in the implementation of policies and, consequently, justifying decision-making within the framework of public reason ― e.g. policies related to disinformation, social media algorithms, public health, professional ethics, education, etc. Nevertheless, even though a demarcation criterion is a useful conceptual tool for the assessment and implementation of such policies, it is not prescriptive on its own within this pragmatic dimension ― in other words, policies are not directly derived from the philosophical elucidation of pseudoscience. Instead, policy makers should engage in expert deliberation, taking account of issues that are beyond philosophy, such as public/private distinction, the available resources to fund and deploy interventions, the legal framework, economic concerns, etc.
The public acceptance of pseudoscience could involve harmful social consequences, as was the case of Lysenkoism (Kolchinsky, Kutschera, Hossfeld, & Levit, 2017), “scientific” racism (Paludi and Haley, 2014), and social Darwinism (Paul, 2003). Therefore, it is a well-documented danger, which threatens key issues within the public sphere, such as food (Mulet, 2018), education (Forrest & Gross, 2004), health (e.g. Lilienfeld, Lynn, & Lohr, 2003; Ernst, Lee, & Choi, 2011), and justice (Snook, 2008). Accordingly, being characterised as pseudoscience has dire implications, particularly for researchers. In attempts to avoid the risk of promoting these kind of misleading ideas and to rationalise limited resources, researchers will not be given access to the funding and publication systems of science, they will not receive official professional accreditation, they will be banished from the educational system at primary, secondary, and university levels, and their practices and ideas will be considered as a threat to critical thinking and public deliberation. Because of all this, demarcation is a very important decision, and pseudoscientists, like everyone, deserve a fair trial.
Nevertheless, what should we do with a verdict that states that the accused is more or less guilty? If a unit of demarcation turns out to be quite, or not too much, or 7/10 pseudoscientific, there would still be a substantial chance for our verdict to be wrong. As happens with Gruenberger’s criterion ― and as could also happen with Pigliucci’s, depending on interpretation ― it may be easy for a unit of demarcation to achieve a certain degree of perceived scientificity through the exploitation of the less demanding variables of the criterion, such as internal coherence, open-mindedness, ad-hoc predictability, and controlled experiments. Because pseudoscientists fake dispensable, superficial characteristics of science, the inclusion of degrees of these characteristics in a demarcation criterion lends a helping hand by facilitating the production of an illusory level of scientificity. Therefore, gradualist demarcation tends to introduce uncertainty in a class of decisions that show, in most cases, the greatest certainty.
How to cope with these problems?
There is a tacit consensus about what is scientific and what is pseudoscientific, so people with the adequate motivational state can normally differentiate between both (e.g. van der Linden et al., 2015; Tabacchi and Cardaci, 2016; Garrett and Cutting, 2017). Hansson describes this state of affairs in his assertion: “distinguishing between science and pseudoscience is much like riding a bicycle. Most people can ride a bicycle, but only a few can account for how they do it” (Hansson, 2013, p. 61). There are numerous examples of consensual demarcation in encyclopaedic publications comprehensively focused on pseudoscience (e.g. Shermer, 2002) ― these, in turn, greatly converge with more informal lists of topics characterized as pseudoscience, published by online encyclopaedias (e.g. Wikipedia, 2019; RationalWiki, 2019) and official reports commissioned by governments (e.g. MSPSI, 2011; Clarke, Black, Stussman, Barnes, & Nahin, 2015). Accordingly, the demarcation problem can be defined as the project to justify and optimise this already existing consensus. Hence, it should not necessarily be a fuzzy task: a demarcation criterion could be restricted to discriminating between classes that are known in advance, thus offering philosophical justification to decisions that have already been made.
To properly understand the kind of discriminant demarcation I will develop in this section, it is important to be aware that “pseudoscience” will be regarded here as an extreme category. Therefore, it must be used only for radical instances of epistemic misconduct. Consensual pseudosciences, such as homeopathy, neuro-linguistic programming, and climate change denial show radically flawed epistemic dimensions: there are no studies supporting the theoretical framework and the clinical efficacy of homeopathy and neuro-linguistic programming, whereas anthropogenic global warming has been broadly confirmed by scientific evidence. Thus, discriminant demarcation is contrary to overstating the scope of pseudoscience, instead, considering it a narrow philosophical category. There are cases in which pseudoscience has been weaponised to perform ideology-driven social criticism ― for example, sociobiology (Thompson, 1980) and neoclassical economics (Bunge, 2016) have been labelled as pseudosciences. Even worse, radical instances of overstated scepticism, in which scientific theories and fields are accused of being pseudoscientific, constitute a popular rhetoric strategy among science deniers, known as “pseudo-scepticism” (Torcello, 2016).
To summarise, within discriminant demarcation, pseudoscience is regarded as:
(1) A sharp, greatly restricted category defined by radical epistemic negligences.
(2) An independent class that must be defined by its distinctive characteristics.
Consequently, due to (1) and (2), pseudoscience cannot be negatively defined solely as a by-product of the definition of science. Instead, it must be defined by its own distinctive characteristics, recognised by means of the following question: what characteristics does pseudoscience have that science and other types of non-science do not? Therefore, a definition of “science” cannot be extracted from a discriminant demarcation criterion of pseudoscience, composed of its exclusive features ― science, including formal and applied fields, involves other relevant and greatly nuanced non-discriminant multi-dimensional characteristics that are still discussed among philosophers of science, such as models, measurement, theories, prediction, and replication.
A discriminant metacriterion
A metacriterion capable of coping with the previously discussed problems should be made up of four requirements (hereby referred to as R1, R2, R3, and R4), classified into two groups. On one hand, R1 and R2 are desirable procedural requirements ― that is, they are general values that should guide the development of the demarcation criterion of pseudoscience. The fulfilment of R1 and R2 is not necessary, but they give rise to general conditions of plausibility: a demarcation criterion that does not satisfy these axiological standards would have serious problems achieving an optimal threshold of acceptance among experts. On the other hand, R3 and R4 are criterion requirements whose satisfaction is mandatory in order to provide a proper criterion of relevance for selecting the necessary and sufficient characteristics of pseudoscience.
R1: The demarcation criterion of pseudoscience should entail the least amount of philosophical commitments. This is desirable in order to provide the tool with elegance, parsimony, and the capability to achieve widespread acceptance. In this regard, it is important to incorporate well-elucidated variables, as well as to avoid the competition between multiple demarcation criteria producing disparate results according to the philosophical commitments they entail.
R2: The demarcation criterion of pseudoscience should explain and optimise current consensus. Firstly, it must be able to explain all the units of demarcation that are well-foundedly and unanimously characterised as pseudoscience. Secondly, as pseudoscience often performs processes of cultural evolution, the criterion must be able not only to demarcate future and unknown forms of pseudoscience but also to reach current levels of consensus with respect to them.
R3: Mimicry of science is a necessary requirement to be pseudoscience, given that this is its distinctive feature as a subclass of non-science.
R4: All the items of the demarcation criterion must be discriminant with respect to science. “Discriminant” refers to characteristics not included in any basic definition of science, but which, instead, can be found among certain instances of pseudoscience. Hence, a sufficient definition of pseudoscience must entail fulfilment of R3 and of at least one R4-type variable.
The internal structure for a demarcation criterion that satisfy this metacriterion would be as follows:
Pseudoscience: R3 and at least one R4-type item.
- R3 is necessary.
- At least one R4-type item is necessary.
- The conjunction of R3 and at least one R4-type item is sufficient.
Given that science justifiably shows the trappings of science, it satisfies R3, so this requirement discerns between the set that includes both science and pseudoscience, on the one hand, and other forms of non-science, on the other. Therefore, what is truly problematic about the demarcation criterion of pseudoscience is distinguishing it from science. That is the task of R4-type items, selected by consensual negative answers to the following question: “can something with this feature be science?”.
Furthermore, both the philosophical requirements and the internal structure established by this metacriterion are ahistorical. This means that even though the exhaustive definition of R4-type variables may vary over time, according to new forms and strategies of pseudoscience, their inclusion in the demarcation criterion of pseudoscience should not change through time. This is because dramatically regressive changes in the definition of science are not expected ― at least, not dramatically enough to turn the distinctive radical epistemic flaws of pseudoscience into shared characteristics.
Regarding the problems detected in the analysis of gradualist demarcation:
– The axiology expressed by R1 and R2 helps solve the problem of normative power and the problem of the definition of the variables by establishing as neutral as possible philosophical foundations, from which practical consensus could be derived. Accordingly, philosophical agendas should not be a burden for the demarcation tool. For example, this kind of theoretical burden can be found in the demarcation criterion proposed by Bunge (1982), which demands social support, the use of nomological reasoning, and commitment to strong scientific realism ― highly controversial requirements that do not have relevant implications for the definition of pseudoscience.
Contrarily, the problem of measuring the variables still exists, although its severity has been substantially restricted. Firstly, it has been quantitatively palliated by account for only a few discriminant items, which reduces the problem of measuring an excessive number of variables. Secondly, given that the conjunction of R4-type items characterises all current instances of pseudoscience, but any science, the problem is also qualitatively reduced. As these variables are meaningful as discriminant characteristics as long as they constitute radical misconducts, it is unnecessary to measure a spectrum of degrees of deficiency. For example, if, for gradualist demarcation, it is necessary to measure the continuum of methodological reliability, it would now be enough to define the absolute lack of methodological reliability that takes place in some instances of pseudoscience.
– R3 neutralises the problem of the scope: as pseudoscience is characterised by science mimicry, its demarcation criterion should only be used over units that are publicly presented as science. Additionally, the conjunction of R3 and at least one R4-type characteristic facilitates the distinction of pseudoscience from other types of radical non-science ― as soon as conspiracy theorists and proponents of the paranormal engage in science mimicry they become pseudoscientific. As ensuring a narrow and well-elucidated conceptual scope for pseudoscience constitutes a relevant purpose of discriminant demarcation, mimicry of science accounted by R3 solely refers to the fields of knowledge usually included within the conceptual scope of the English word “science”. Accordingly, R3 does not denote mimicry of humanities and art. In contrast, Hansson (2013) argues that it is not crucial whether something is called science but whether it is claimed to have the function of science, namely to provide the most reliable information available about its subject-matter; thus, appealing to the German concept Wissenschaft to delimit the scope of pseudoscience. Consequently, within Hansson’s framework, pseudophilosophy should also be deemed pseudoscience. This conception of science mimicry is too wide to fulfil the purposes of discriminant demarcation, as R4-type characteristics of pseudoscience and pseudophilosophy greatly differ — for example, regarding methods and empirical evidence (related remarks on Hansson’s use of the concept Wissenschaft can be found in Fasce, 2018; for a discussion of Hansson’s demarcation criterion as an unfavourably wide concept see Letrud, 2019).
– R4 solves the problem of the criterion of relevance, the problem of the weight of the variables, and the problem of normative power. Regarding the problem of the criterion of relevance, R4 states that we must only take into account variables that can be found in pseudoscience, but not in science. Hence, shared characteristics, even in different degrees, must be regarded as irrelevant for demarcation purposes. Moreover, as these variables are radical, exclusive, and not subjected to controversies, they entail normative power, thus establishing an intersubjective and robust borderline between science and pseudoscience. All R4-type items have the same weight by showing the same discriminant potential. Hence, it is worth wondering why it is necessary to include all R4-type variables in the demarcation criterion of pseudoscience instead of just one. This is because different pseudosciences are defined by distinctive R4-type variables ― that is to say, the reasons why they are considered radical instances of non-science are diverse. Accordingly, a certain unit of demarcation may be considered pseudoscience due to problems relating to its domain, whereas another may be due to lack of evidentiary support.
Lastly, R4-type variables also contribute in distinguishing pseudoscience from non-pseudoscientific non-science that publicly presents as scientific: as they do not fit with any R4-type criteria, these units of demarcation do not involve radical epistemic misconducts. Therefore, these are the kind of ambiguous cases that discriminant demarcation excludes as instances of pseudoscience ― even if close to being pseudoscience, they are not deviant enough. Accordingly, these units must be classified as instances of proto-science, bad science, soft science, and the like.
Empirical outcomes and concluding remarks
There are two related tasks within the demarcation problem: (A) the demarcation of current consensual instances of pseudoscience and (B) the demarcation of ambiguous and future forms of pseudoscience. (A) is the basic aim of the demarcation problem and its solution should justify the tacit demarcation already made. However, although (A) is a feasible task for both approaches, it is substantially more problematic within the gradualist framework. Particularly, due to the profuse inclusion of non-discriminant variables lacking normative power, that are hard to select, define, rank, measure, and to be proven as shared with science. Contrastingly, as the discriminant approach is focused on the distinctive traits of pseudoscience, it leaves aside these problematic resemblant features of science and pseudoscience.
This discriminant metacriterion strongly restricts (B), as it should optimise, not extend, (A). Hence, a unit of demarcation not accounted by (A) should be characterised as pseudoscience only if it shows the same level of accordance with a demarcation criterion as consensual instances of pseudoscience. In contrast, (B) is greatly problematic for the gradualist approach, particularly due to its tendency to unnecessarily stretch the scope of the analysis of pseudoscience. In sum, discriminant demarcation is less demanding regarding (A) and more demanding regarding (B) than gradualist demarcation.
In Fasce (2018) I developed a demarcation criterion that fulfils the requirements of this discriminant metacriterion, where pseudoscience radically differs from science regarding domain, method, and evidence. So, it can be identified by being uncontroversially outside the domain of science, particularly due to untestable content ― such as reiki, morphic fields, acupuncture’s qi, and vertebral subluxations ― and through the use of radically flawed methods ― for example research on EMDR without controlling exposure (Herbert, Lilienfeld, Lohr, Montgomery, O’Donohue, Rosen, & Tolin, 2000), the use of projective tests (Lilienfeld, Wood, & Garb, 2000), pendulum diagnosis, and unspecific cases, such as illusions of causality (Torres, Barberia, & Rodríguez-Ferreiro, 2020). Some others are characterised by a complete lack of evidence ― e.g. climate change denial, orgone (Klee, 2005), Bach flowers (Ernst, 2010), facilitated communication (Hemsley, Bryant, Schlosser, Shane, Lang, Paul, et al., 2018), electromagnetic hypersensitivity (Rubin, Hiller, Nieto-Hernandez, van Rongen, & Oftedal, 2011), and the wide range of pseudophenomena considered by parapsychology, such as telepathy, clairvoyance, and precognition. Lastly, some pseudosciences are defined by a mix of these three R4-type variables, such as anthroposophy (Hansson, 1991), pseudoarchaeology (Fagan, 2006), and intelligent design (Boudry, Blancke, & Braeckman, 2010).
Moreover, the characterisation of certain units of demarcation as instances of pseudoscience using this discriminant demarcation criterion is the cornerstone of the Pseudoscientific Belief Scale (PSEUDO; Fasce & Picó, 2019a) ― the only currently validated scale of general pseudoscientific beliefs with tested psychometric soundness ― and of a recent operational definition of Complementary and Alternative Psychotherapies (CAP; Fasce & Adrián-Ventura, 2020). Accordingly, the outcomes of discriminant demarcation show high internal consistency (α = .90), as well as strong convergent and discriminant validity (e.g. Fasce & Picó, 2019b; Fasce, Adrián-Ventura, & Avendaño, 2020). Lastly, the conjunction of these results and a literature review of prior research outcomes on pseudoscience has led to a new explanatory model for the epidemiology of pseudoscientific beliefs: the Explanation-Polarisation model (EX-PO; Fasce, forthcoming). Hence, this discriminant demarcation is not only philosophically satisfactory, but scientifically fruitful.
I’ve developed a discriminant metacriterion based on the critical analysis of the gradualist approach and able to deal with some of the persistent shortcomings of demarcation projects. This metacriterion regards that of “pseudoscience” as an extreme label; characterised by the conjunction of radical epistemic flaws and science mimicry. This philosophical framework has been shown to be fruitful, through the definition of a validated psychological construct that has led to novel findings on the epidemiology and the psychological profile of pseudoscientific beliefs.
 Similar criticism over Pigliucci’s approach to demarcation can be found in Schindler (Forthcoming).
 This definition of science is linked to applied science, so pure science is beyond its scope. I’m using this example to show that Pigliucci’s definition involve a similar limitation regarding applied science, such as SETI, and studies focused on the detection of correlational patterns and prediction.
 For example: if the code of ethics of clinical psychologists states that clinicians must reject pseudoscientific practices, and a particular clinician accepts a demarcation criterion with normative power that defines primal therapy, bioenergetic analysis, thought field therapy, and orgone therapy as instances of pseudoscience, then the practitioner is ethically compelled to avoid the use of these techniques. Otherwise, she must either reject the code of ethics or the demarcation criterion of pseudoscience.
 For example: two philosophers agree on Gruenberger’s criteria — Pigliucci’s can also be applied to the same case. Further, they agree on the degree of authoritarianism, humility, open-mindedness, and paranoia of flat-earthers, orgone therapists, or graphologists by means of some reliable and intersubjective measurement tools — a scenario regarded as practically impossible by Gruenberger, but let’s consider it as feasible. Insofar as there are no constraints on how, when, and where to establish the borderline, they could choose different tolerance thresholds. That is to say, one can consider a unit of demarcation as pseudoscientific starting from forty points, whereas the other can do so starting from fifteen points. Under these conditions, demarcation is not an intersubjective and externally consistent task. These philosophers do not need to reject their shared non-normative demarcation criterion in order to justify their endorsement of practices and beliefs that the other regards as pseudoscientific.
 I’m using the term “measurement” in a wide sense, also covering classification into categories not designated by numbers.
 (1) Canada and the United Stated have border disputes. (2) Mexico and the United States have border disputes. (1) and (2) do not entail that Canada and Mexico have border disputes.
 Personally, I consider it very likely that science has blurred boundaries with, among others, proto-science and soft science. Moreover, I also consider it likely that pseudoscience could have blurred boundaries with other types of radical non-science, as some forms of science mimicry are ambiguous. Nevertheless, this situation is not as problematic as these authors state. If pseudoscience is accounted as an extreme category ― denoting radical epistemic flaws ―, it does not directly overlap with science. This conception entails an explicit rejection of ambiguous cases as instances of pseudoscience (e.g. string theory, sociobiology, evolutionary psychology, neoclassical economics, astrobiology, and the like), thus ensuring a narrower conceptual scope.
 As happens with the promotion of pseudoscientific beliefs, one can participate in misconducts unwittingly ― deliberate intentions to deceive are a necessary condition for science fraud, but not for pseudoscience. By means of self-deception and motivated reasoning, pseudoscientific believers are often disconnected from the search for truth. Hence, pseudoscience is to intentional fraud as bullshit is to lies (for an in-depth discussion on this issue see Ladyman, 2013).
 Alchemy is better defined as hermeticism, or even as a proto-science, not as a form of pseudoscience in contemporary terms.
 Conspiracy theories are defined as “lay beliefs that attribute the ultimate cause of an event, or the concealment of an event from public knowledge, to a secret, unlawful, and malevolent plot by multiple actors working together” (Swami, Chamorro-Premuzic, & Furnham, 2010, p. 749). Conspiracies exist and there are well-known historical examples, although these beliefs are unwarranted when they are an “unnecessary assumption (…) when other explanations are more likely” (Aaronovitch, 2009, p. 5).
 It should not be assumed that the Wittgensteinian family resemblance only refers to behavioural or observable aspects. Wittgenstein could have admitted that genetic, unobservable kinship is part of the family resemblance of cousins. Nevertheless, he might not have accepted an explanation in objective terms on why this specific feature is more relevant than others. Instead, he would have possibly stated that its pre-eminence is based on its role within language-games that involve cousins.
 Other authors, such as Kuhn, have provided another potential solution for the wide-open texture problem by including not only similarities between members of the same class, but also dissimilarities to members of other classes (Andersen, 2000). So, as contrasting concepts may mutually limit the extensions of each other, it would be better to concentrate on the dissimilarity between contrast classes. It is my intention to defend a similar solution within the context of demarcation: pseudoscience would be better defined as a contrast class by means of its distinctive dissimilarities or “discriminant” characteristics.
 It is worth mentioning that “to study pseudoscience” is not the same as “to produce pseudoscience”. Indeed, unwarranted beliefs constitute a valid and worthy study domain, as can be seen from clinical research on alternative medicine, anomalistic psychology, and other related scientific research on conspiracy ideation and the cognitive roots of science denial. This scientific approach to pseudoscience is based on the acceptance of a normative approach to demarcation, as well as on some methodological standards that are beyond the demarcation problem — such as validated measurement, meaningful comparisons, and proper data analysis.
 Of course, there are risky and potentially fruitless projects that may be funded to increase research variability, but projects based on pseudoscientific assumptions are known beforehand to be unsuitable of funding — particularly, unworthy of public funding.
 As decision theory shows, we can perform decision-making under conditions of uncertainty through the assessment of probabilities. Nevertheless, probabilistic reasoning may be preferable or not, and surely all sources of uncertainty reduction are more than welcome. In the context of demarcation, a workable and well-founded cut off is preferable in general terms than probabilistic uncertainty. Gradualist demarcationists would agree on this general principle, although they do not consider sharp categorical assessment of pseudoscience as feasible. Discriminant demarcation, to be developed in the next section, is more optimistic in this regard.
 From my point of view, legitimate criticism over “silver bullets” (Popper’s falsifiability, Lakatos’ progressivity, Kuhn’s puzzle solving, etc.) led philosophers to favor “machine guns” of diffuse (often gradualist) criteria. As such, the problem that has impeded demarcation for the last century was a counterproductive dismissal of the discriminant characteristics of pseudoscience. Despite there being no such silver bullet, it is feasible to demarcate pseudoscience by means of few precise, discriminant criteria — most being already well-known, e.g. untestability, and complete lack of both reliability and confirmatory evidence.
 This discriminant metacriterion must be interpreted as a whole. For example, without R4, R1 would be more about achieving consensus than about getting it right.
 This does not mean that they are irrelevant when assessing the level of quality of science and when assessing how deviant pseudoscientific doctrines are.
 As I mentioned previously, the borderline between pseudoscience and other types of unwarranted beliefs could be blurred. Discriminant demarcation only sets a framework for a clear cut off between science and pseudoscience.
Andersen, H. 2000. “Kuhn’s Account of Family Resemblance: A Solution to the Problem of WideOpen Texture.” Erkenntnis 52: 313–337.
Baker, G., and P. Hacker. 1984. Scepticism, Rules and Language. Oxford: Blackwell.
Beyerstein, B. 1995. Distinguising Science from Pseudoscience. Accessed June 1, 2019. http://www.sld.cu/galerias/pdf/sitios/revsalud/beyerstein_cience_vs_pseudoscience.pdf.
Blancke, S., M. Boudry, and M. Pigliucci. 2017. “Why Do Irrational Beliefs Mimic Science? The Cultural Evolution of Pseudoscience.” Theoria 83 (1): 78–97.
Boghossian, P. 2006. Fear of Knowledge: Against Relativism and Constructivism. Oxford: Oxford University Press.
Boudry, M. 2013. “Loki’s Wager and Laudan’s Error: On Genuine and Territorial Demarcation.” In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, edited by M. Pigliucci, and M. Boudry, 79–100. Chicago: University of Chicago Press.
Boudry, M., S. Blancke, and J. Braeckman. 2010. “Irreducible Incoherence and Intelligent Design: A Look Into the Conceptual Toolbox of a Pseudoscience.” The Quarterly Review of Biology 85 (4): 473–482.
Bunge, M. 1982. “Demarcating Science From Pseudoscience.” Fundamenta Scientiae 3: 369–388.
Bunge, M. 2016. Between two Worlds: Memoirs of a Philosopher-Scientist. Cham: Springer International.
Clarke, T., L. Black, B. Stussman, P. Barnes, and R. Nahin. 2015. “Trends in the Use of Complementary Health Approaches Among Adults: United States, 2002–2012.” National Health Statistics Reports 10 (79): 1–16.
Coliva, A. 2010. “Was Wittgenstein an Epistemic Relativist?” Philosophical Investigations 33 (1): 1–23.
Crayford, J. 1997. The Radical Reading of Wittgenstein: Cavell, Kripke, and Bloor as a School of Wittgenstein Readers. Evanston: Northwestern University.
Dawes, G. 2018. “Identifying Pseudoscience: A Social Process Criterion.” Journal for General Philosophy of Science 49 (8): 1–16.
de Regt, H. 2017. Understanding Scientific Understanding. Oxford: Oxford University Press.
Dupré, J. 1993. The Disorder of Things: Metaphysical Foundations of the Disunity of Science. Cambridge: Harvard University Press.
Ernst, E. 2010. “Bach Flower Remedies: A Systematic Review of Randomised Clinical Trials.” Swiss Medical Weekly 140: w13079.
Ernst, E., M. Lee, and T. Choi. 2011. “Acupuncture: Does it Alleviate Pain and are There Serious Risks? A Review of Reviews.” Pain 152 (4): 755–764.
Fagan, G. 2006. Archaeological Fantasies: How Pseudoarchaeology Misrepresents the Past and Misleads the Public. New York: Routledge.
Fasce, A. 2017. “Los parásitos de la ciencia. Una caracterización psicocognitiva del engaño pseudocientífico.” Theoria. An International Journal for Theory, History and Foundations of Science 32 (3): 347–365.
Fasce, A. 2018. “What Do We Mean When We Speak of Pseudoscience? The Development of a Demarcation Criterion Based on the Analysis of Twenty-one Previous Attempts.” Disputatio. Philosophical Research Bulletin 6 (7): 459–488.
Fasce, A., and J. Adrián-Ventura. 2020. “Conceptual Elucidation and Epidemiological Framework.” Professional Psychology: Research and Practice, accepted manuscript.
Fasce, A., J. Adrián-Ventura, and D. Avendaño. 2020. “Do as the Romans Do: On the Authoritarian Roots of Pseudoscience.” Public Understanding of Science, accepted manuscript.
Fasce, A. Forthcoming. “The Explanation-Polarisation Model: Pseudoscience Spreads through Explanatory Satisfaction and Motivated Reasoning.”
Fasce, A., and A. Picó. 2019a. “Conceptual Foundations and Validation of the Pseudoscientific Belief Scale.” Applied Cognitive Psychology 33 (4): 617–628.
Fasce, A., and A. Picó. 2019b. “Science as a Vaccine. The Relation between Scientific Literacy and Unwarranted Beliefs.” Science & Education 28 (1–2): 109–125.
Forrest, B., and P. Gross. 2004. Creationism’s Trojan Horse: The Wedge of Intelligent Design. Oxford: Oxford University Press.
Fried, E. 2017. “What are Psychological Constructs? On the Nature and Statistical Modelling of Emotions, Intelligence, Personality Traits and Mental Disorders.” Health Psychology Review 11 (2): 130–134.
Garrett, B., and R. Cutting. 2017. “Magical Beliefs and Discriminating Science From Pseudoscience in Undergraduate Professional Students.” Heliyon 3 (11): e00433.
Givón, T. 1986. “Prototypes: Between Plato and Wittgenstein.” In Noun Classes and Categorization, edited by C. Craig, 77–102. Philadelphia: John Benjamins Publishing Company.
Gruenberger, F.  1964. “A Measure for Crackpots.” Science 25: 1413–1415.
Hansson, S. O. 1991. “Is Anthroposophy Science?” Conceptus: Zeitschrift Fur Philosophie 25 (64): 37–49.
Hansson, S. O. 2009. “Cutting the Gordian Knot of Demarcation.” International Studies in the Philosophy of Science 23 (3): 237–243.
Hansson, S. O. 2013. “Defining Pseudoscience and Science.” In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, edited by M. Pigliucci and M. Boudry, 61–78. Chicago: University of Chicago Press.
Hansson, S. O. 2017. “Science and Pseudo-Science.” In The Stanford Encyclopedia of Philosophy, edited by E. Zalta. Accessed June 1, 2019. https://plato.stanford.edu/archives/sum2017/entries/pseudo-science/.
Hemsley, B., L. Bryant, R. W. Schlosser, H. C. Shane, R. Lang, D. Paul, et al. 2018. “Systematic Review of Facilitated Communication 2014–2018 Finds no new Evidence That Messages Delivered Using Facilitated Communication are Authored by the Person with Disability.” Autism & Developmental Language Impairments 3: 1–8.
Herbert, J., S. Lilienfeld, J. Lohr, R. Montgomery, W. O’Donohue, G. Rosen, and D. Tolin. 2000. “Science and Pseudoscience in the Development of eye Movement Desensitization and Reprocessing: Implications for Clinical Psychology.” Clinical Psychology Review 20 (8): 945–971.
Hull, D. 1965. “The Effect of Essentialism on Taxonomy—Two Thousand Years of Stasis.” British Journal for the Philosophy of Science 16: 314–326.
Irwin, D., J. Irwin, and T. Price. 2001. “Ring Species as Bridges Between Microevolution and Especiation.” Genetica 112 (113): 223–243.
Irzik, G., and R. Nola. 2011. “A Family Resemblance Approach to the Nature of Science for Science Education.” Science & Education 20 (7-8): 591–607.
Kitcher, P. 1982. Abusing Science: The Case Against Creationism. Cambridge, MA: MIT Press.
Klee, G. 2005. “The Resurrection of Wilhelm Reich and Orgone Therapy.” The Scientific Review of Mental Health Practice 4 (1): 6–8.
Kolchinsky, E., U. Kutschera, U. Hossfeld, and G. Levit. 2017. “Russia’s new Lysenkoism.” Current Biology 27 (19): R1042–R1047.
Kripke, S. 1982. Wittgenstein on Rules and Private Language: An Elementary Exposition. Oxford: Blackwell.
Kruger, J., and D. Dunning. 1999. “Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments.” Journal of Personality and Social Psychology 77 (6): 1121–1134.
Ladyman, J. 2013. “Toward a Demarcation of Science From Pseudoscience.” In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, edited by M. Pigliucci, and M.
Boudry, 45–60. Chicago: University of Chicago Press.
Lakatos, I. 1978a. The Methodology of Scientific Research Programmes. Cambridge: Cambridge University Press.
Lakatos, I. 1978b. Mathematics, Science, and Epistemology. Cambridge: Cambridge University Press.
Laudan, L. 1983. “The Demise of the Demarcation Problem.” In Physics, Philosophy and Psychoanalysis. Essays in Honour of Adolf Grünbaum, edited by R. Cohen, and L. Laudan, 111–127. Dordrecht: Reidel.
Letrud, K. 2019. “The Gordian Knot of Demarcation: Tying Up Some Loose Ends.” International Studies in the Philosophy of Science 32 (1): 3–11.
Lilienfeld, S., R. Ammirati, and M. David. 2012. “Distinguishing Science From Pseudoscience in School Psychology: Science and Scientific Thinking as Safeguards Against Human Error.” Journal of School Psychology 50 (1): 7–36.
Lilienfeld, S., S. Lynn, and J. Lohr. 2003. Science and Pseudoscience in Clinical Psychology. New York: The Guilford Press.
Lilienfeld, S., J. Wood, and H. Garb. 2000. “The Scientific Status of Projective Techniques.” Psychological Science in the Public Interest 1 (2): 27–66.
Lugg, A. 1987. “Bunkum, Flim-Flam and Quackery: Pseudoscience as a Philosophical Problem.” Dialectica 41: 221–230.
Mahner, M. 2013. “Science and Pseudoscience. How to Demarcate After the (Alleged) Demise of the Demarcation Problem.” In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, edited by M. Pigliucci, and M. Boudry, 29–44. Chicago: University of Chicago Press.
MSPSI. 2011. Informe sobre uso de medicina natural en España. Accessed October 27, 2019 https://www.mscbs.gob.es/novedades/docs/analisisSituacionTNatu.pdf.
Mulet, J. M. 2018. “The Appeal-to-Nature Fallacy. Homeopathy and Biodynamic Agriculture in Official EU Regulations.” Mètode Science Studies Journal 95: 173–179.
Paludi, M., and S. Haley. 2014. “Scientific Racism.” In Encyclopedia of Critical Psychology, edited by T. Teo, 1697–1700. Berlín: Springer.
Paul, D. 2003. “Darwin, Social Darwinism and Eugenics.” In The Cambridge Companion to Darwin, edited by J. Hodge, and G. Radick, 214–239. Cambridge: Cambridge University Press.
Pigliucci, M. 2003. “Species as Family Resemblance Concepts: The (Dis-)Solution of the Species Problem?” BioEssays 25: 596–602.
Pigliucci, M. 2013. “A (Belated) Response to Laudan.” In Philosophy of Pseudoscience: Reconsidering the Demarcation Problem, edited by M. Pigliucci, and M. Boudry, 9–28. Chicago: University of Chicago Press.
Pompa, L. 1967. “Family Resemblance.” The Philosophical Quarterly 17 (66): 63–69.
Popper, K. 1963. Conjectures and Refutations. New York: Basic Books.
RationalWiki. 2019. List of pseudosciences. Accessed October 27, 2019. https://rationalwiki.org/wiki/List_of_pseudosciences.
Richman, R. 1962. “Something Common.” The Journal of Philosophy 59 (26): 821–830.
Rozenblit, L., and F. Keil. 2002. “The Misunderstood Limits of Folk Science: An Illusion of Explanatory Depth.” Cognitive Science 26: 521–562.
Rubin, J., L. Hiller, R. Nieto-Hernandez, E. van Rongen, and G. Oftedal. 2011. “Do People with Idiopathic Environmental Intolerance Attributed to Electromagnetic Fields Display Physiological Effects When Exposed to Electromagnetic Fields? A Systematic Review of Provocation Studies.” Bioelectromagnetics 32: 593–609.
Ruse, M. 1982. “Creation-Science is Not Science.” Science, Technology, and Human Values 7 (40): 72–78.
Schindler, S. Forthcoming. Pseudo-solutions to the Demarcation Problem.
Shermer, M. 2002. Encyclopedia of Pseudoscience. Santa Bárbara: ABC-CLIO.
Smolin, L. 2007. The Trouble with Physics. Boston: Mariner Books.
Snook, B. 2008. “Pseudoscientific Policing Practices and Beliefs [Special Issue].” Criminal Justice and Behavior 35 (10): 1211–1214.
Swami, V., T. Chamorro-Premuzic, and A. Furnham. 2010. “Unanswered Questions: A Preliminary Investigation of Personality and Individual Difference Predictors of 9/11 Conspiracy Beliefs.” Applied Cognitive Psychology 24: 749–761.
Tabacchi, M., and M. Cardaci. 2016. “Preferential Biases for Texts That Include Neuroscientific Jargon.” Psychological Reports 118 (3): 793–803.
Templeton, A. 1992. “The Meaning of Species and Speciation: A Genetic Perspective.” In The Units of Evolution: Essays on the Nature of Species, edited by M. Ereshefsky, 3–27. Cambridge: MIT Press.
Thagard, P. 1988. Computational Philosophy of Science. Cambridge: The MIT Press.
Thompson, P. 1980. “Is Sociobiology a Pseudoscience?” In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, edited by P. Asquith and R. Giere, 363–370. Chicago: University of Chicago Press.
Torcello, L. 2016. “The Ethics of Belief, Cognition, and Climate Change Pseudoskepticism: Implications for Public Discourse.” Topics in Cognitive Science 8 (1): 19–48.
Torcello, L. 2020. “La democracia y los límites de la razón: por qué es necesaria una defensa continuada de los compromisos liberales para contrarrestar la desinformación y la xenofobia.” Disputatio, Philosophical Research Bulletin. In press.
Torres, M., I. Barberia, and J. Rodríguez-Ferreiro. 2020. “Causal Illusion as a Cognitive Basis of Pseudoscientific Beliefs.” British Journal of Psychology, doi:10.1111/bjop.12441.
Toulmin, S. 1972. Human Understanding. Princeton: Princeton University Press.
Toulmin, S. 1984. “The new Philosophy of Science and the “Paranormal”.” Skeptical Inquirer 9: 48–55.
van der Linden, S., A. Leiserowitz, G. Feinberg, and E. Maibach. 2015. “The Scientific Consensus on Climate Change as a Gateway Belief: Experimental Evidence.” PLoS ONE 10 (2): e0118489.
Wikipedia. 2019. List of Topics Characterized as Pseudoscience. Accessed October 27, 2019 https://en.wikipedia.org/wiki/List_of_topics_characterized_as_pseudoscience.
Williams, M. 2007. “Why Wittgensteinian Contextualism is not Relativism.” Episteme: A Journal of Social Epistemology 4 (1): 93–114.
Wilson, F. 2000. The Logic and Methodology of Science and Pseudoscience. Toronto: Canadian Scholars’ Press.
Wittgenstein, L. 1958. Philosophical Investigations. New Jersey: John Wiley & Sons.
Woit, P. 2007. Not Even Wrong. New York: Basic Books.