We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
While there are affinities between Collingwood’s views and pragmatism, their shared considerations of the socio-historical dimensions of scientific knowledge have not been explored thus far. This chapter aims to fill this gap by comparing Collingwood’s views from An Essay on Metaphysics with pragmatist stances in contemporary philosophy of science by Philip Kitcher and Hasok Chang. In addition to similarities regarding the importance of the purposes of inquiry and framing knowledge in relation to a system of practice, there are disagreements between Collingwood and this strand of pragmatism regarding truth, propositional knowledge, and drawing out political implications. I argue that Collingwood’s approach can supply tools that can assist the pragmatist goals of improving scientific practice, mainly through analyzing cases from the history of science. This warrants mapping Collingwood’s place in twentieth-century philosophy as a precursor to recent attempts to overcome the clash between logical and historical approaches to scientific knowledge.
Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real-time and predict future attacks. However, current operationalizations of ‘terrorist’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches.
Chapter 2 covers the basics of research design.It is written so that students without any research design experience or coursework can learn common research designs to enable them to conduct statistical analyses in the text.Hypotheses development with variable construction (dependent and independent variables) are covered and applied to experimental and non-experimental designs.Survey methods including question construction and implementation of surveys is presented.
The chapter defends a particular philosophical engagement with medicine (i.e., normative philosophy of medicine) that is directly connected to the problem of determining the nature of philosophical inquiry. It starts with the discontinuity view, notably advocated by Pellegrino (1986; 2001), suggesting philosophy and science are discrete. Two primary arguments support the discontinuity view: science is empirical while philosophy is conceptual (Dummett 2010), and science is descriptive while philosophy is normative (Thomasson 2015; 2017). The chapter critically examines and ultimately rejects these claims, introducing the continuity view as an alternative, positing a close relationship between philosophical inquiry and science. Building on the works of Sober (2008), Kaiser (2019), and Kitcher (2011), a normative philosophy of science approach is proposed, distinguishing three levels of analysis (aims, nature, and key concepts), which mirror the types of questions posed by modern medical challenges. The chapter concludes by endorsing philosophy of medicine as a legitimate subdiscipline of philosophy of science, and arguing for the comprehensive value of this approach over conventional perspectives.
In this chapter, a case is made for the inclusion of computational approaches to linguistics within the theoretical fold. Computational models aimed at application are a special case of predictive models. The status quo in the philosophy of linguistics is that explanation is scientifically prior to prediction. This is a mistake. Once corrected, the theoretical place of prediction is restored and, with it, computational models of language. The chapter first describes the history behind the emergence of explanation over prediction views in the general philosophy of science. It’s then suggested that this post-positivist intellectual milieu influenced the rejection of computational linguistics in the philosophy of theoretical linguistics. A case study of the predictive power already embedded in contemporary linguistic theory is presented through some work on negative polarity items. The discussion moves to the competence–performance divide informed by the so-called Galilean style in linguistics that retains the explanatory over prediction ideal. In the final sections of the chapter, continuous methods, such as probabilistic linguistics, are used to showcase the explanatory and predictive possibilities of nondiscrete approaches, before a discussion of the contemporary field of deep learning in natural language processing (NLP), where these predictive possibilities are further amplified.
This chapter discusses the application of the logic to actual theories. In particular, it discusses the use of this relevant logic to make inferences about classical theories. It also examines the internal structure of theories and the nature of the conditionals in those theories. At the end of the chapter, some suggestions are made about generalizing the semantical theory of the book to treat the application of background scientific theories to other theories and some consequences for scientific confirmation.
The rapid growth of cultural evolutionary science, its expansion into numerous fields, its use of diverse methods, and several conceptual problems have outpaced corollary developments in theory and philosophy of science. This has led to concern, exemplified in results from a recent survey conducted with members of the Cultural Evolution Society, that the field lacks ‘knowledge synthesis’, is poorly supported by ‘theory’, has an ambiguous relation to biological evolution and uses key terms (e.g. ‘culture’, ‘social learning’, ‘cumulative culture’) in ways that hamper operationalization in models, experiments and field studies. Although numerous review papers in the field represent and categorize its empirical findings, the field's theoretical challenges receive less critical attention even though challenges of a theoretical or conceptual nature underlie most of the problems identified by Cultural Evolution Society members. Guided by the heterogeneous ‘grand challenges’ emergent in this survey, this paper restates those challenges and adopts an organizational style requisite to discussion of them. The paper's goal is to contribute to increasing conceptual clarity and theoretical discernment around the most pressing challenges facing the field of cultural evolutionary science. It will be of most interest to cultural evolutionary scientists, theoreticians, philosophers of science and interdisciplinary researchers.
The propositions of a scientific theory are connected with empirical states of affairs. Determining how theoretical propositions are connected with empirical facts, what Carnap called the “empirical significance” of a theory, is a complex affair. Carnap’s account of the relationship between theoretical frameworks and methods of observation has come in for plentiful criticism, alleging that Carnap’s theory of science does not allow for a sophisticated entwinement of theory and observation, instead favoring heavy formalism and a brittle reductionism. I present evidence that Carnap’s account of the distinction between theoretical and observation languages is more flexible than it is usually depicted to be and is motivated by his philosophy of science. In particular, in his mature work Carnap argues that the "specific calculus" of a scientific theory, including mathematical structure and physical laws, are included in the axiomatic foundations and linguistic framework of that theory. Carnap’s account of language thus turns out to be deeply entangled with his philosophy of science, and one cannot be understood independently of the other.
Social network analysis is known to provide a wealth of insights relevant to many aspects of policymaking. Yet, the social data needed to construct social networks are not always available. Furthermore, even when they are, interpreting such networks often relies on extraneous knowledge. Here, we propose an approach to infer social networks directly from the texts produced by actors and the terminological similarities that these texts exhibit. This approach relies on fitting a topic model to the texts produced by these actors and measuring topic profile correlations between actors. This reveals what can be called “hidden communities of interest,” that is, groups of actors sharing similar semantic contents but whose social relationships with one another may be unknown or underlying. Network interpretation follows from the topic model. Diachronic perspectives can also be built by modeling the networks over different time periods and mapping genealogical relationships between communities. As a case study, the approach is deployed over a working corpus of academic articles (domain of philosophy of science; N=16,917).
What a scientific community holds to be its core beliefs change over time. Gilbert and Weatherall and Gilbert argue that a community’s core beliefs should be understood as a collective belief formed by a joint commitment and that these core group beliefs are difficult to change as it would require a new joint commitment to be formed. This chapter argues that the primary normative constraints on group belief revision are the weight of the evidence being considered by the group, and not the normative constraints that arise from joint commitments. This chapter sketches a positive view of how epistemic groups may respond to new evidence by looking to Kuhn’s own account of how crises arise and are resolved in science.
The influence of Kuhn’s Structure has been remarkably wide-ranging. The author was honored by the History of Science Society, the Philosophy of Science Association, and the Society for the Social Studies of Science, three very different academic societies. The chapter reviews the impact of Structure and the changing perceptions of its significance, one discipline at a time. It focuses on book reviews of Structure, some written soon after the book was first published, and others written as much as fifty years after its publication, in response to the publication of the fourth edition. It also discusses articles that reflect on the impact of the book and eulogies or appreciations of Kuhn marking his death in 1996.
Anthropological research in urban contexts reflects the fundamental mutations in social sciences. The boundaries between the traditional academic disciplines have become blurred. New clusters of interdisciplinary and transdisciplinary research emerge. These changes involve risks and chances. Philosophy of science insists on clear concepts and terminologies. Does it make sense to use the term ‘urban anthropology’? If new disciplines or sub-disciplines arise, they should have distinct shapes, and the nomenclature should reflect their scientific profile. Starting from a diachronic comparative analysis of anthropological theories and methodologies this article proposes a road map for heuristic and epistemological investigations of anthropological research in an increasingly urbanized world.
This Element will overview research using models to understand scientific practice. Models are useful for reasoning about groups and processes that are complicated and distributed across time and space, i.e., those that are difficult to study using empirical methods alone. Science fits this picture. For this reason, it is no surprise that researchers have turned to models over the last few decades to study various features of science. The different sections of the element are mostly organized around different modeling approaches. The models described in this element sometimes yield take-aways that are straightforward, and at other times more nuanced. The Element ultimately argues that while these models are epistemically useful, the best way to employ most of them to understand and improve science is in combination with empirical methods and other sorts of theorizing.
This chapter integrates the notion of “theory” into the causal-model framework that we use in the book. We describe an approach in which theoretical claims are thought of as model justifications within a hierarchy of causal models. The approach has implications for the consistency of inferences across models and for assessing when and how theory is useful for strengthening causal claims.
John Gould’s father was a gardener. A very, very good one – good enough to be head of the Royal Gardens at Windsor. John apprenticed, too, becoming a gardener in his own right at Ripley Castle, Yorkshire, in 1825. As good as he was at flowers and trees, birds became young John Gould’s true passion early in life. Like John Edmonstone, John Gould (1804–1881) adopted Charles Waterton’s preservation techniques that kept taxidermied bird feathers crisp and vibrant for decades (some still exist in museums today), and he began to employ the technique to make extra cash. He sold preserved birds and their eggs to fancy Eton schoolboys near his father’s work. His collecting side-hustle soon landed him a professional post: curator and preserver of the new Zoological Society of London. They paid him £100 a year, a respectable sum for an uneducated son of a gardener, though not enough to make him Charles Darwin’s social equal (Darwin initially received a £400 annual allowance from his father plus £10,000 as a wedding present).
Darwin claimed that On the Origin of Species, or the Preservation of Favoured Races in the Struggle for Life was only an “abstract” of that much longer book he had begun to write in 1856, after his irreverent meeting with J. D. Hooker, T. H. Huxley, and T. V. Wollaston, and Lyell’s exasperated encouragement in May. But he never completed that larger book. Instead, he worked on plants and pigeons and collected information through surveys from other naturalists and professional specimen hunters like Alfred Russel Wallace for the better part of a decade.
For all their scientific prowess and public renown, there is no comparable Lyell-ism, Faraday-ism, Einstein-ism, Curie-ism, Hawking-ism, or deGrasse-Tyson-ism. So, there must be something even more powerful than scientific ideas alone caught in the net of this ism attached to Darwin. And whatever the term meant, it’s fair to say that Darwinism frightened Bryan.
Historian Everett Mendelsohn was intrigued. In the middle of writing a review of an annual survey of academic publications in the History of Science, he marveled that an article in that volume contained almost 40 pages’ worth of references to works on Darwin published in just the years between 1959 and 1963. Almost 200 works published in a handful of years – no single figure in the history of science commanded such an impressive academic following. Yet Mendelsohn noted that, paradoxically, no one had written a proper biography of Darwin by 1965. Oh sure, there was commentary. Lots of commentary. But so many of the authors were retired biologists who had a tendency toward hagiography or, the opposite, with axes to grind.
Meeting the “White Raja of Sarawak” in Singapore in 1853 had been a stroke of luck. Honestly, it could have been a major turning point in what had been an unlucky career so far for 30-year-old collector Alfred Russel Wallace (1823–1913) (Figure 4.1). But the steep, rocky, sweaty climb up Borneo’s Mt. Serembu (also known as Bung Moan or Bukit Peninjau) in the last week of December 1855 wasn’t exactly what Wallace expected. His eyeglasses fogged in the humidity. Bamboo taller than buildings crowded the narrow path. Near the top, the rainforest finally parted. But it revealed neither a temple nor some sort of massive colonial complex with all the trappings of empire worthy of a “raja.” Instead, there leaned a modest, very un-colonial-ruler-like white cabin. When he saw it, Wallace literally called it “rude.”
Charles Darwin spent nearly the whole of his writing career attempting to convince his colleagues, the general public, and, by extension, you and me, that change occurs gradually. Tiny slivers of difference accumulate over time like grains of sand in a vast hourglass. Change happens, in other words. It’s painfully slow, but it’s inevitable. By implication, two organisms that look different enough to us to be classified as separate species share, many tens of thousands or even millions of generations back, the same ancestors. (Inbreeding means we don’t even need to go back quite that many generations to demonstrate overlap, but you get the point.) But change that gradual means, as Darwin himself well recognized, that looking for “missing links” would be a pretty silly errand. Differences between one generation and the next look to our eyes just like common variation. It’s one grain falling from the top of the hourglass to the bottom. You can’t perceive the change. You would have to go back in time to find the very first individuals who possessed a particular trait – bat-like wings, say, or human-ish hands – and then, turning to their parents, you would see something almost identical.