Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized (...) conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science. (shrink)

To better navigate social interactions, we routinely (consciously or unconsciously) categorize people based on their distinctive features. One important way we do this is by ascribing political orientations to them. For example, based on certain behavioral cues, we might perceive someone as politically liberal, progressive, conservative, libertarian, Marxist, anarchist, or fascist. Although such ascriptions may appear to be mere descriptions, I argue that they can have deeper, regulative effects on their targets, potentially politicizing and polarizing them in ways that remain (...) underexplored in research on political polarization. To capture this dynamic, I introduce the notion of ‘politicizing mindshaping,’ distinguish it from other types of politicization, and review evidence suggesting that politicizing mindshaping is likely common. Finally, I contend that the pernicious effects of politicizing mindshaping may currently be significantly underestimated. This is because, unlike in many other cases of social labeling, negativity in political labeling is, to some extent, widely tolerated – even encouraged – and largely unconstrained by social norms in democratic countries. (shrink)

The phenomenon of interactive human kinds, namely kinds of people that undergo change in reaction to being studied or theorised about, matters not only for the reliability of scientific claims, but also for its wider, sometimes harmful effects at the group or societal level, such as contributing to negative stigmas or reinforcing existing inequalities. This paper focuses on the latter aspect of interactivity and argues that scientists studying interactive human kinds are responsible for foreseeing harmful effects of their research and (...) for devising ways of mitigating them. (shrink)

Drawing on empirical findings, a number of philosophers have recently argued that people who use English as a foreign language may face a linguistic bias in academia in that they or their contributions may be perceived more negatively than warranted because of their English. I take a critical look at this argument. I first distinguish different phenomena that may be conceptualized as linguistic bias but that should be kept separate to avoid overgeneralizations. I then examine a range of empirical studies (...) that philosophers have cited to argue that people who use English as a foreign language are subject to linguistic bias in academia. I contend that many of these studies do not sufficiently support key claims that philosophers have made about linguistic bias, are challenged by counterevidence, and lack generalizability. I end by introducing methodological recommendations that may help philosophers develop more convincing empirically informed arguments regarding linguistic bias. (shrink)

It is unknown to what extent medical researchers generalize study findings beyond their samples when their sample size, sample diversity, or knowledge of conditions that support external validity do not warrant it. It is also unknown to what extent medical researchers describe their results with precise quantifications or unquantified generalizations, i.e., generics, that can obscure variations between individuals. We therefore systematically reviewed all prospective studies (n = 533) published in the top four highest ranking medical journals, Lancet, New England Journal (...) of Medicine (NEJM), Journal of the American Medical Association (JAMA), and the British Medical Journal (BMJ), from January 2022 to May 2023. We additionally reviewed all NEJM Journal Watch clinical research summaries (n = 143) published during the same time. Of all research articles reporting prospective studies, 52.5% included generalizations beyond specific national study populations, with the numbers of articles with generics varying significantly between journals (JAMA = 12%; Lancet = 77%) (p < 0.001, V = 0.48). There was no evidence that articles containing broader generalizations or generics were correlated with larger or more nationally diverse samples. Moreover, only 10.2% of articles with generalizations beyond specific national populations reported external validity strengthening factors that could potentially support such extrapolations. There was no evidence that original research articles and NEJM Journal Watch summaries intended for practitioners differed in their use of broad generalizations, including generics. Finally, from the journal with the highest citation impact, articles containing broader conclusions were correlated with more citations. Since there was no evidence that studies with generalizations beyond specific national study populations or with generics were associated with larger, more nationally diverse samples, or with reports of population similarity that may permit extensions of conclusions, our findings suggest that the generalizations in many articles were insufficiently supported. Caution against overly broad generalizations in medical research is warranted. (shrink)

Recently, researchers and reporters have made a wide range of claims about the distribution, nature, and societal impact of political polarization. Here I offer reasons to believe that, even when they are correct and prima facie merely descriptive, many of these claims have the highly negative side effect of increasing political polarization. This is because of the interplay of two factors that have so far been neglected in the work on political polarization, namely that (1) people have a tendency to (...) conform to descriptive norms (i.e., norms capturing (perceptions of) what others commonly do, think, or feel), and (2) claims about political polarization often convey such norms. Many of these claims thus incline people to behave, cognize, and be affectively disposed in ways that contribute to social division. But there is a silver lining. People’s tendency to conform to descriptive norms also provides the basis for developing new, experimentally testable strategies for counteracting political polarization. I outline three. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...) are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (shrink)

Generics have historically proven difficult to analyse using the tools of formal semantics. In this paper, I argue that an influential theory of the meaning of generics due to Sarah-Jane Leslie, the Psychological Theory of Generics, is best interpreted not as a theory of their meaning, but as a theory of the psychological heuristics that we use to judge whether or not generics are true. I argue that Leslie’s methodology is not well-suited to producing a theory of the meaning of (...) generics, since it takes speakers’ judgments at face value and ignores the non-semantic factors that might affect these judgments. Leslie’s theory therefore overfits the data of our linguistic intuitions. I present a reconceptualised version of the Psychological Theory of Generics as a theory of how heuristics affect our judgements of the truth values of generics and discuss the application of this reconceptualised theory to some of the puzzles posed by generics, including their apparent content-sensitivity, their inferential asymmetry and their association with stereotyping and prejudice. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...) political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)