In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness, with the aim to develop a common language, that allows us to weave together different strands of robustness research. We define robustness as (...) the relative stability of a robustness target with respect to specific interventions on a modifier. Our account captures the various sub-types of robustness that are discussed in the research literature, including robustness to distribution shifts, prediction robustness, or the robustness of algorithmic explanations. Finally, we delineate robustness from adjacent key concepts in ML, such as extrapolation, generalization, and uncertainty, and establish it as an independent epistemic concept. (shrink)

Best known for his influential History of Greece, the historian and politician George Grote wrote this account of Plato's dialogues as a philosophical supplement to the History. First published in 1865 and written in dialogic form, Grote's account of Plato's works includes substantial footnotes and marginalia. This first volume focuses on Plato's early and transitional dialogues, all of which feature Socrates. It also includes a preface to the whole project which discusses the meaning and importance of philosophy itself, and extensive (...) introductory material on pre-Socratic philosophy, the life of Plato and history of the Platonic canon. With three volumes each running to over six hundred pages, Grote's scholarship is formidably comprehensive. The publication of Plato and the Other Companions of Sokrates confirmed him as one of the greatest authorities on Plato in the nineteenth century. (shrink)

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials—particularly randomised controlled trials (RCTs)—is gaining ground. However, an issue that (...) has been overlooked so far in the debate is that, compared with drug RCTs, AI RCTs require methodological adjustments, which entail ethical challenges. This paper sets out to develop a systematic account of the ethics of AI RCTs by focusing on the moral principles of clinical equipoise, informed consent and fairness. This way, the objective is to animate further debate on the (research) ethics of medical AI. (shrink)

Broad in its geographic scope and yet grounded in original archival research, this book situates the inception of modern aesthetic theory – the philosophical analysis of art and beauty - in theological contexts that are crucial to explaining why it arose. Simon Grote presents seminal aesthetic theories of the German and Scottish Enlightenments as outgrowths of a quintessentially Enlightenment project: the search for a natural 'foundation of morality' and a means of helping naturally self-interested human beings transcend their own self-interest. (...) This conclusion represents an important alternative to the standard history of aesthetics as a series of preludes to the achievements of Immanuel Kant, as well as a reinterpretation of several canonical figures in the German and Scottish Enlightenments. It also offers a foundation for a transnational history of the Enlightenment without the French philosophes at its centre, while solidly endorsing historians' growing reluctance to call the Enlightenment a secularising movement. (shrink)

Bioethics, Volume 36, Issue 2, Page 134-142, February 2022.

Various publications claim that medical AI systems perform as well, or better, than clinical experts. However, there have been very few controlled trials and the quality of existing studies has been called into question. There is growing concern that existing studies overestimate the clinical benefits of AI systems. This has led to calls for more, and higher-quality, randomized controlled trials of medical AI systems. While this a welcome development, AI RCTs raise novel methodological challenges that have seen little discussion. We (...) discuss some of the challenges arising in the context of AI RCTs and make some suggestions for how to meet them. (shrink)

There is much to learn from Durán and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence. In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defensive medicine or paternalism2—the issue that lies at the heart is an epistemic problem: how can physicians (...) know whether to trust decisions made by AI systems? In this manner, various studies examining the interaction of AI systems and physicians have shown that without being able to evaluate their trustworthiness, especially novice physicians become over-reliant on algorithmic support—and ultimately are led astray by incorrect decisions.3–5 This leads to a second insight from the paper, namely that even if some AI system happens to be opaque, it is still not built on the moon. To assess its trustworthiness, AI developers or physicians have different sorts of higher order evidence at hand. Most importantly, …. (shrink)

This paper develops an account of the opacity problem in medical machine learning (ML). Guided by pragmatist assumptions, I argue that opacity in ML models is problematic insofar as it potentially undermines the achievement of two key purposes: ensuring generalizability and optimizing clinician–machine decision-making. Three opacity amelioration strategies are examined, with explainable artificial intelligence (XAI) as the predominant approach, challenged by two revisionary strategies in the form of reliabilism and the interpretability by design. Comparing the three strategies, I argue that (...) interpretability by design is most promising to overcome opacity in medical ML. Looking beyond the individual opacity amelioration strategies, the paper also contributes to a deeper understanding of the problem space and the solution space regarding opacity in medical ML. (shrink)

Berichte zur Wissenschaftsgeschichte, EarlyView.

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

We invite systematic consideration of the metaphors of cycles and circulation as a long-term theme in the history of the life and environmental sciences and medicine. Ubiquitous in ancient religious and philosophical traditions, especially in representing the seasons and the motions of celestial bodies, circles once symbolized perfection. Over the centuries cyclic images in western medicine, natural philosophy, natural history and eventually biology gained independence from cosmology and theology and came to depend less on strictly circular forms. As potent ‘canonical (...) icons’, cycles also interacted with representations of linear and irreversible change, including arrows, arcs, scales, series and trees, as in theories of the Earth and of evolution. In modern times life cycles and reproductive cycles have often been held to characterize life, in some cases especially female life, while human efforts selectively to foster and disrupt these cycles have harnessed their productivity in medicine and agriculture. But strong cyclic metaphors have continued to link physiology and climatology, medicine and economics, and biology and manufacturing, notably through the relations between land, food and population. From the grand nineteenth-century transformations of matter to systems ecology, the circulation of molecules through organic and inorganic compartments has posed the problem of maintaining identity in the face of flux and highlights the seductive ability of cyclic schemes to imply closure where no original state was in fact restored. More concerted attention to cycles and circulation will enrich analyses of the power of metaphors to naturalize understandings of life and their shaping by practical interests and political imaginations. (shrink)

This essay considers how scholarly approaches to the development of molecular biology have too often narrowed the historical aperture to genes, overlooking the ways in which other objects and processes contributed to the molecularization of life. From structural and dynamic studies of biomolecules to cellular membranes and organelles to metabolism and nutrition, new work by historians, philosophers, and STS scholars of the life sciences has revitalized older issues, such as the relationship of life to matter, or of physicochemical inquiries to (...) biology. This scholarship points to a novel molecular vista that opens up a pluralist view of molecularizations in the twentieth century and considers their relevance to current science. (shrink)

In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments. Thereby, we examine many of the limitations of current machine learning algorithms (with deep learning in particular) (...) and highlight their relevance for medical diagnostics. Among the problems we inspect are the theoretical foundations of deep learning (which are not yet adequately understood), the opacity of algorithmic decisions, and the vulnerabilities of machine learning models, as well as concerns regarding the quality of medical data used to train the models. Building on this, we discuss different desiderata for an uncertainty amelioration strategy that ensures that the integration of machine learning into clinical settings proves to be medically beneficial in a meaningful way. (shrink)

This paper develops an account of how the implementation of ML models into healthcare settings requires revising the methodological apparatus of philosophical bioethics. On this account, ML models are cognitive interventions that provide decision-support to physicians and patients. Due to reliability issues, opaque reasoning processes, and information asymmetries, ML models pose inferential problems for them. These inferential problems lay the grounds for many ethical problems that currently claim centre-stage in the bioethical debate. Accordingly, this paper argues that the best way (...) to make progress in remedying these ethical problems is to distil their epistemic core and to identify appropriate epistemic norms as guardrails. The viability of this approach will be highlighted based on four key issues: trust, responsibility, paternalism, and fairness. In that respect, the paper also contributes to a more pronounced view of the specific methodological challenges for ethical theorizing, when the point of reference are complex statistical models that make predictions, rather than individual agents, acting for reasons. (shrink)

The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; (...) that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair. (shrink)

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain (...) in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

Microbial diversity has become a leitmotiv of contemporary microbiology, as epitomized in the concept of the microbiome, with significant consequences for the classification of microbes. In this paper, I contrast microbiology’s current diversity ideal with its influential predecessor in the twentieth century, that of purity, as epitomized in Robert Koch’s bacteriological culture methods. Purity and diversity, the two polar opposites with regard to making sense of the microbial world, have been operationalized in microbiological practice by tools such as the “clean” (...) Petri dish versus the “dirty” Winogradsky column, the latter a container that mimics, in the laboratory, the natural environment that teems with diverse microbial life. By tracing the impact of the practices and concepts of purity and diversity on microbial classification through a history of techniques, tools, and manuals, I show the shifts in these concepts over the last century. Juxtaposing the dominant purity ideal with the more restricted, but continuously articulated, diversity ideal in microbial ecology not only provides a fresh perspective on microbial classification that goes beyond its intellectual history, but also contextualizes the present focus on diversity. By covering the period of a century, this paper outlines a revised longue durée historiography that takes its inspiration from artifacts, such as Petri dish and the Winogradsky column, and thereby simple, but influential technologies that often remain invisible. This enables the problem of historical continuity in modern science to be addressed and the accelerationist narratives of its development to be countered. (shrink)

Broadly defined as adherence to teachings of Christian Wolff (1679–1754), Wolffianism characterized much of the mainstream of German academic philosophy for at least half the eighteenth century. German Pietism, by contrast, defined in its narrowest sense as a late-seventeenth- and eighteenth-century movement for the renewal of the Lutheran Churches of the Holy Roman Empire, has long figured in the history of German “Enlightenment” philosophy as Wolffianism’s anti-philosophical, religious foil. The conventional portrait of Wolffianism and Pietism as antithetical to one another, (...) which has long structured the historiography of eighteenth-century German philosophy, is by no means implausible, but it has been undermined over the past several decades by a significant and growing body of new research. This essay offers a panoramic survey and critical assessment of the state of the field. (shrink)

The term ‘cell’, in addition to designating fundamental units of life, has also been applied since the nineteenth century to technical apparatuses such as fuel and galvanic cells. This paper shows that such technologies, based on the electrical effects of chemical reactions taking place in containers, had a far-reaching impact on the concept of the biological cell. My argument revolves around the controversy over oxidative phosphorylation in bioenergetics between 1961 and 1977. In this scientific conflict, a two-level mingling of technological (...) culture, physical chemistry and biological research can be observed. First, Peter Mitchell explained the chemiosmotic hypothesis of energy generation by representing cellular membrane processes via an analogy to fuel cells. Second, in the associated experimental scrutiny of membranes, material cell models were devised that reassembled spatialized molecular processes in vitro. Cells were thus modelled both on paper and in the test tube not as morphological structures but as compartments able to perform physicochemical work. The story of cells and membranes in bioenergetics points out the role that theories and practices in physical chemistry had in the molecularization of life. These approaches model the cell as a ‘topology of molecular action’, as I will call it, and it involves concepts of spaces, surfaces and movements. They epitomize an engineer’s vision of the organism that has influenced diverse fields in today’s life sciences. (shrink)

This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. (...) While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims, but associations and classification schema are more plausibly discovered in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained. (shrink)

In the context of 1960s research on biological membranes, scientists stumbled upon a curiously coloured material substance, which became called the “purple membrane.” Interactions with the material as well as chemical analyses led to the conclusion that the microbial membrane contained a photoactive molecule similar to rhodopsin, the light receptor of animals’ retinae. Until 1975, the find led to the formation of novel objects in science, and subsequently to the development of a field in the molecular life sciences that comprised (...) biophysics, bioenergetics as well as membrane and structural biology. Furthermore, the purple membrane and bacteriorhodopsin, as the photoactive membrane transport protein was baptized, inspired attempts at hybrid bio-optical engineering throughout the 1980s. A central motif of the research field was the identification of a functional biological structure, such as a membrane, with a reactive material substance that could be easily prepared and manipulated. Building on this premise, early purple membrane research will be taken as a case in point to understand the appearance and transformation of objects in science through work with material substances. Here, the role played by a perceptible material and its spontaneous change of colour, or reactivity, casts a different light on objects and experimental practices in the late twentieth century molecular life sciences. With respect to the impact of chemical working and thinking, the purple membrane and rhodopsins represent an influential domain straddling the life and chemical sciences as well as bio- and material technologies, which has received only little historical and philosophical attention. Re-drawing the boundary between the living and the non-enlivened, these researches explain and model organismic activity through the reactivity of macromolecular structures, and thus palpable material substances. (shrink)

What are the driving forces of cultural macroevolution, the evolution of cultural traits that characterize societies or populations? This question has engaged anthropologists for more than a century, with little consensus regarding the answer. We develop and fit autologistic models, built upon both spatial and linguistic neighbor graphs, for 44 cultural traits of 172 societies in the Western North American Indian (WNAI) database. For each trait, we compare models including or excluding one or both neighbor graphs, and for the majority (...) of traits we find strong evidence in favor of a model which uses both spatial and linguistic neighbors to predict a trait’s distribution. Our results run counter to the assertion that cultural trait distributions can be explained largely by the transmission of traits from parent to daughter populations and are thus best analyzed with phylogenies. In contrast, we show that vertical and horizontal transmission pathways can be incorporated in a single model, that both transmission modes may indeed operate on the same trait, and that for most traits in the WNAI database, accounting for only one mode of transmission would result in a loss of information. (shrink)

As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement learning and active inference, reconceptualize mental disorders. Although each paradigm borrows heavily from machine learning, they differ significantly (...) in their methodology, their preferred level of description, the role they assign to the environment and, especially, the degree to which they aim to assimilate psychiatric disorders to a standard medical disease model. By imagining how these paradigms might evolve, we bring into focus three rather different visions for the future of psychiatric research. Although machine learning plays a crucial role in the articulation of these paradigms, it is clear that we are far from automating the process of conceptual revision. The leading role continues to be played by the theoretical, metaphysical and methodological commitments of the competing paradigms. (shrink)

After a wave of breakthroughs in image‐based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. However, prominent researchers are claiming that another paradigm shift in medical ML is imminent—due to most recent staggering successes of large language models—from single‐purpose applications toward generalist models, driven by natural language. This article investigates the implications of this paradigm shift for the ethical debate. Focusing on issues like trust, transparency, threats of patient autonomy, responsibility issues in the (...) collaboration of clinicians and ML models, fairness, and privacy, it will be argued that the main problems will be continuous with the current debate. However, due to functioning of large language models, the complexity of all these problems increases. In addition, the article discusses some profound challenges for the clinical evaluation of large language models and threats to the reproducibility and replicability of studies about large language models in medicine due to corporate interests. (shrink)

Contrary to the view that Francis Hutcheson attempted to expound, defend, and further develop the philosophical system described in Shaftesbury's Characteristics, some contemporaries of Hutcheson considered Hutcheson's differences from Shaftesbury to be at least as profound as the similarities. The clearest descriptions of those differences can be found in William Leechman's preface to Hutcheson's 1755 System of Moral Philosophy, and more elaborately in a review of Hutcheson's System, probably by Hugh Blair, published in the 1755 Edinburgh Review. Examining Shaftesbury's and (...) Hutcheson's moral philosophies in light of these two descriptions reveals that Shaftesbury's defense of virtue as natural to human beings, while a plausible response to Locke, clearly did not escape Hutcheson's censure of Epicurean egoism. (shrink)

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, (...) in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models. (shrink)

Wie kann man einen historischen Blick auf das eigene Fach werfen? Diese Frage ist nicht einfach zu beantworten – will man einerseits nicht in einer Nabelschau und Hagiographie enden, andererseits aber auch keinen umfassenden Entwurf einer zukünftigen Historiographie vorlegen. Die hier in loser Folge publizierten Interviews mit bekannten Protagonist:innen der Berliner Wissenschaftsgeschichte von ca. 1970–1990 in West und Ost rücken die Geschichte des Faches deshalb in einem bestimmten Milieu in den Fokus und versuchen, die Historiographie jenseits einer Institutionen- oder Theoriegeschichte (...) voranzutreiben. Welche Motivationen oder Probleme bewegten einzelne Wissenschaftler:innen, sich der Geschichte ihres Faches zu widmen oder sich etwa aus der Soziologie oder Philosophie in die Wissenschaftsgeschichte zu bewegen? Welche Ausbildungspraktiken existierten in diesem heterogenen, zwischen den Disziplinen angesiedelten Feld, welche Anregungen bezog man aus welchen Kontexten? Wie war Lehre strukturiert und welche Netzwerke bildeten sich mit der Zeit? Kurz: Mit welchem Interesse kam man zur Wissenschaftsgeschichte und was wurde daraus? Die Auswahl der Interviewees erfolgt ohne Anspruch auf Vollständigkeit oder Proporz; der Fragenkatalog der Interviews richtet sich individuell nach den Biographien und dem Werk und entfaltet sich oft spontan im Gespräch. Die Interviews wurden digital aufgezeichnet, transkribiert, der Schriftsprache angepasst, gegebenenfalls gekürzt, annotiert und von den Interviewees authentifiziert. Wir beabsichtigen mit dieser Serie von Interviews zunächst die Dokumentation rezenter Geschichte durch eine Oral History, die subjektive Wahrnehmungen und persönliche Erlebnisse einschließt. Auf diese Weise werden Segmente einer größtenteils ungeschriebenen Geschichte anhand von Biographien erfahrbar und damit auch einer weiteren kritischen Bearbeitung und Integration in ein Gesamtbild zugänglich. Da uns im Zuge der jeweiligen Vorbereitung und Durchführung, Transkription und Abstimmung der Interviews daran gelegen war, aus Sicht der Akteur:innen wichtige Sammelbände und Aufsätze, Monographien oder auch „graue Literatur“ zu erfassen, wird nebenbei eine kommentierte Bibliographie zur Geschichte der Wissenschaftsgeschichte entstehen. Unsere Hoffnung besteht darin, mittels dieser Sammlung mit Berlin einen fruchtbaren Raum und mit den siebziger und achtziger Jahren eine produktive Zeit des Faches jenseits von Reminiszenz oder Nostalgie zu erkunden – nicht zuletzt auch, um den Blick für gegenwärtige Herausforderungen des Faches zu schärfen. (shrink)

Factors such as subsistence turnover, warfare, or interaction between different groups can be major sources of cultural change in human populations. Global demographic shifts such as the transition to agriculture during the Neolithic and more recently the urbanization and globalization of the twentieth century have been major catalysts for cultural change. Here, we test whether cultural traits such as patri/matrilocality and postmarital migration persist in the face of social upheaval and gene flow during the past 150 years in postcolonial South (...) Africa. The recent history of South Africa has seen major demographic shifts that resulted in the displacement and forced sedentism of indigenous Khoekhoe and San populations. During the expansion of the colonial frontier, the Khoe-San admixed with European colonists and enslaved individuals from West/Central Africa, Indonesia, and South Asia, introducing novel cultural norms. We conducted demographic interviews among Nama and Cederberg communities representing nearly 3,000 individuals across three generations. Despite the history of colonial expansion, and the subsequent incorporation of Khoe-San and Khoe-San-descendant communities into a colonial society with strong patrilocal norms, patrilocality is the least common postmarital residence pattern in our study populations today. Our results suggest that more recent forces of integration into the market economy are likely the primary drivers of change in the cultural traits examined in our study. Birthplace had a strong effect on an individual’s odds of migration, distance moved, and postmarital residence form. These effects are at least partially explained by the population size of the birthplace. Our results suggest that market factors local to birthplaces are important drivers of residence decisions, although the frequency of matrilocal residence and a geographic and temporal cline in migration and residence patterns also indicate the persistence of some historic Khoe-San cultural traits in contemporary groups. (shrink)

Wie kann man einen historischen Blick auf das eigene Fach werfen? Diese Frage ist nicht einfach zu beantworten – will man einerseits nicht in einer Nabelschau und Hagiographie enden, andererseits aber auch keinen umfassenden Entwurf einer zukünftigen Historiographie vorlegen. Die hier als Bausteine zu einer Oral History der Wissenschaftsgeschichte in loser Folge publizierten Interviews mit bekannten Protagonisten der Berliner Wissenschaftsgeschichte von ca. 1970–1990 in West und Ost rücken die Geschichte des Faches deshalb in einem bestimmten Milieu in den Fokus und (...) versuchen, die Historiographie jenseits einer Institutionen‐ oder Theoriegeschichte voranzutreiben. Welche Motivationen oder Probleme bewegten einzelne Wissenschaftler:innen, sich der Geschichte ihres Faches zu widmen oder sich etwa aus der Soziologie oder Philosophie in die Wissenschaftsgeschichte zu bewegen? Welche Ausbildungspraxen existierten in diesem heterogenen, zwischen den Disziplinen angesiedelten Feld, welche Anregungen bezog man aus welchen Kontexten? Wie war Lehre strukturiert und welche Netzwerke bildeten sich mit der Zeit? Kurz: Mit welchem Interesse kam man zur Wissenschaftsgeschichte und was wurde daraus? Die Auswahl der Interviewees erfolgt ohne Anspruch auf Vollständigkeit oder Proporz; der Fragenkatalog der Interviews richtet sich individuell nach den Biographien und dem Werk und entfaltet sich oft spontan im Gespräch. Die Interviews wurden digital aufgezeichnet, transkribiert, der Schriftsprache angepasst, gegebenenfalls gekürzt, annotiert und von den Interviewees authentifiziert. Wir beabsichtigen mit dieser Serie von Interviews zunächst die Dokumentation rezenter Geschichte durch eine Oral History, die subjektive Wahrnehmungen und persönliche Erlebnisse einschließt. Auf diese Weise werden Segmente einer größtenteils ungeschriebenen Geschichte anhand von Biographien erfahrbar und damit auch einer weiteren kritischen Bearbeitung und Integration in ein Gesamtbild zugänglich. Da uns im Zuge der jeweiligen Vorbereitung und Durchführung, Transkription und Abstimmung der Interviews daran gelegen war, aus Sicht der Akteure wichtige Sammelbände und Aufsätze, Graue Literatur oder Monographien zu erfassen, wird nebenbei eine kommentierte Bibliographie zur Geschichte der Wissenschaftsgeschichte entstehen. Unsere Hoffnung besteht darin, mittels dieser Sammlung mit Berlin einen fruchtbaren Raum und mit den 1970er und 1980er Jahren eine produktive Zeit des Faches jenseits von Reminiszenz oder Nostalgie zu erkunden nicht zuletzt auch, um den Blick für gegenwärtige Herausforderungen des Faches zu schärfen.Mathias Grote, Anke te Heesen. (shrink)

I would recommend this book to anyone wanting to major in business or start their own company.

Subject of the Moment, Reloaded. The catalogue of Ludwig Darmstaedter's document collection, which contains sources as well as a categorization conspicuous of a history of knowledge avant la lettre, invites for a revisiting of the history of our discipline, thereby asking for its current role in academia and beyond. We argue that as historians of science or as historians of knowledge, our discipline disposes of the tools and topics necessary to respond to urgent epistemic and political problems.

The so-called .Karlsschulrede. (1793) of the German naturalist Carl Friedrich Kielmeyer can be considered as a keystone to the understanding of"Naturphilosophie" both in German idealism (Schelling) and the romantic period.Kielmeyer's work considers life as the result of specific forces in the organic realm and thereby searches to explain the harmony of organic existence anddevelopment. Taking into account Kant.s outlines for a lifescience in the "Kritik der Urteilskraft" (1790), Kielmeyer's notion of teleological processes in nature is sketched. The historical and epistemological (...) relevance of this "vital-materialistic" (Lenoir) theory of life can be characterized by three major transformations in the understanding of nature in the "Karlsschulrede": First, the development of a holistic, organological view on the world, second, the emphasis on phenomena of life as historical processes and third the analogy between organism and mind. These issues found the strong influence of Kielmeyer's text on philosophy and science in the early 19th-century. (shrink)

Open peer commentary on the article “The Circular Conditions of Second-order Science Sporadically Illustrated with Agent-based Experiments at the Roots of Observation” by Manfred Füllsack. Upshot: Observers construct an internal, local state of order for the perspective of their observations, but in doing so they increase the overall entropy of the system they belong to, e.g., society, by adding more options for potential courses of action. Thus, the Second Law of Thermodynamics is not just satisfied in the circular condition of (...) second-order observations, but the increase in entropy is actually exploited as a resource by a system such as society, where it serves as a prerequisite for double contingency. (shrink)