Christophe Malaterre

The central role of such epistemic concepts as theory, explanation, model, or mechanism is rarely questioned in philosophy of science. Yet, what is their actual use in the practice of science? In this philosophy of science project, we deploy text-mining methods to investigate the usage of 61 epistemic notions in a corpus of full-text articles from the biological and biomedical sciences (N=73,771). The influence of disciplinary context is also examined by splitting the corpus into sub-disciplinary clusters. The results reveal the intricate semantic networks that these concepts actually form in the scientific discourse, not always following our intuitions, at least in some parts of science. The research is published in: Malaterre, Christophe & Martin Léonard. 2023. "Epistemic markers in the scientific discourse", Philosophy of Science.

Disagreement in science exists in a variety of strengths, from doubt-raising articles and issues of non-reproducibility up to raging disputes and major controversies. An often-latent form of disagreement consists of misconceptions whereby false ideas are held that run contrary to what is commonly accepted as knowledge. Misconceptions have been the focus of much research in education science and psychology. Here we draw attention to misconceptions that may arise in the very practice of science. We highlight formal features that can be used to characterize misconceptions and distinguish them from controversies, in addition to how they relate to knowledge creation.

Scientific networks are often investigated by means of citation analyses. Yet, interpretation of such networks in terms of semantic (and often disciplinary) content heavily depends on supplementary knowledge, notably about author research specialties. Similar situations arise more generally in many types of social networks whose semantic interpretation relies on supplementary information. Here, author community net-works are inferred from a topic model which provides direct insights into the semantic specificity of the identified “hidden communities of interest” (HCoI). Using a philosophy of science corpus of full-text articles (N=16,917), we investigate its underlying communities by measuring topic profile correlations be-tween authors. A diachronic perspective is built by modeling the research networks over different time-periods and mapping genealogical relationships be-tween communities. The results show a marked in-crease in philosophy of science communities over time and trace the progressive appearance of the specialization areas that structure the field today.

Topic model is a well proven tool to investigate the semantic content of textual corpora. Yet corpora sometimes include texts in several languages, making it impossible to apply language-specific computational approaches over their entire content. This is the problem we encountered when setting to analyze a philosophy of science corpus spanning over eight decades and including original articles in Dutch, German and French, on top of a large majority of articles in English. To circumvent this multilingual problem, we use machine-translation tools to bulk translate non-English documents into English. Though largely imperfect, especially syntactically, these translations nevertheless provide correctly translated terms and preserve the semantic proximity of documents with respect to one another. To assess the quality of this translation step, we develop a “semantic topology preservation test” that relies on estimating the extent to which document-to-document distances have been preserved during translation. We then conduct an LDA topic-model analysis over the entire corpus of translated and English original texts, and compare it to a topic-model done over the English original texts only. We thereby identify the specific contribution of the translated texts. These studies reveal a more complete picture of main topics that can found in the philosophy of science literature, especially during the early days of the discipline when numerous articles were published in languages other than English.

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of “heritable variation in fitness among individuals”. Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with “life-like” or “proto-biotic” systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection can be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems.

Pluralism is widely appealed to in many areas of philosophy of science, though what is meant by ‘pluralism’ may profoundly vary. Because explanations of behaviour have been a favoured target for pluralistic theses, the sciences of behaviour offer a rich context in which to further investigate pluralism. This is what the topical collection The Biology of Behaviour: Explanatory pluralism across the life sciences is about. In the present introduction, we briefly review major strands of pluralist theses and their motivations. We highlight three distinct types of pluralisms—type pluralism, fragmentation pluralism and insular pluralism—and introduce the articles of the topical collection.

As a discipline of its own, the philosophy of science can be traced back to the founding of its academic journals, some of which go back to the first half of the twentieth century. While the discipline has been the object of many historical studies, notably focusing on specific schools or major figures of the field, little work has focused on the journals themselves. Here, we investigate contemporary philosophy of science by means of computational text-mining approaches: we apply topic-modeling algorithms to eight major philosophy of science journals, from the 1930s up until 2017. Based on the full-text content of some 15,897 articles, we identified 25 research themes and 8 thematic clusters that show how the research agenda of the philosophy of science has changed in its content over the course of the last eight decades, up to the philosophy of science we now know. We also show how each one of the journals contributed in its own way to this thematic evolution.

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology with a complementary data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged.

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology with a complementary data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged.

La vie est-elle un phénomène émergent ? Traduit-elle l'apparition de propriétés nouvelles au niveau d'un tout, qui seraient irréductibles aux propriétés et à l'organisation des composants de ce tout, ou encore imprédictibles à partir de ces mêmes éléments ? Développée à la charnière des XIXe et XXe siècles comme alternative aux deux approches antinomiques du vivant que sont le vitalisme et le mécanisme, la notion philosophique d'émergence connaît aujourd'hui de nouveaux développements : avec la prise de conscience de la complexité du vivant, un nouveau discours émergentiste refait surface en biologie et dans le champ scientifique des origines de la vie. Que signifie la notion d'émergence lorsqu'elle s'applique à l'apparition de la vie sur Terre ? Quelles sont sa pertinence et sa portée ? Dans ce livre, Christophe Malaterre propose une clarification conceptuelle de la notion philosophique d'émergence ; il en défend une conception épistémique et contextuelle, adossée à la notion d'explication. En s'inspirant des travaux les plus contemporains sur les origines de la vie, il montre que, selon le contexte épistémique dans lequel le phénomène est évalué, la qualification de l'apparition de la vie comme émergente est, ou non, justifiée. Il défend alors la thèse selon laquelle la caractérisation émergentiste de l'apparition de la vie n'est qu'une conséquence temporaire des limites de nos connaissances scientifiques.

Many researchers consider cancer to have molecular causes, namely mutated genes that result in abnormal cell proliferation (e.g. Weinberg 1998); yet for others, the causes of cancer are to be found not at the molecular level but at the tissue level and carcinogenesis would consist in a disrupted tissue organization with downward causation effects on cells and cellular components (e.g. Sonnenschein & Soto 2008). In this contribution, I ponder how to make sense of such downward causation claims. Adopting a manipulationist account of causation (Woodward 2003), I propose a formal definition of downward causation, and discuss further requirements (in light of Baumgartner 2009). I then show that such an account cannot be mobilized in support of non-reductive physicalism (contrary to Raatikainen 2010). However, I also argue that such downward causation claims might point at particularly interesting dynamic properties of causal relationships that might prove salient in characterizing causal relationships (following Woodward 2010).

Probably the most distinctive feature of synthetic biology is its being “synthetic” in some sense or another. For some, synthesis plays a unique role in the production of knowledge that is most distinct from that played by analysis: it is claimed to deliver knowledge that would otherwise not be attained. In this contribution, my aim is to explore how synthetic biology delivers knowledge via synthesis, and to assess the extent to which this knowledge is distinctly synthetic. On the basis of distinctions between knowledge-how and knowledge-why, and between syntheses that succeed and syntheses that fail, I argue that the contribution of synthesis to knowledge is best understood when syntheses are construed as experimental interventions that aim at probing causal relationships between properties of the entities that are combined through these syntheses and properties of their target products. The distinctiveness of synthetic biology in its quest for knowledge through synthesis stems from its ability to sample at will a space of empirical possibilities that is not only huge but also that has been so scarcely sampled by nature.

Functional diversity holds the promise of understanding ecosystems in ways unattainable by taxonomic diversity studies. Underlying this promise is the intuition that investigating the diversity of what organisms actually do—i.e. their functional traits—within ecosystems will generate more reliable insights into the ways these ecosystems behave, compared to considering only species diversity. But this promise also rests on several conceptual and methodological—i.e. epistemic—assumptions that cut across various theories and domains of ecology. These assumptions should be clearly addressed, notably for the sake of an effective comparison and integration across domains, and for assessing whether or not to use functional diversity approaches for developing ecological management strategies. The objective of this contribution is to identify and critically analyze the most salient of these assumptions. To this aim, we provide an “epistemic roadmap” that pinpoints these assumptions along a set of historical, conceptual, empirical, theoretical, and normative dimensions.

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key research topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches.

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities.

Do trees of life have roots? What do these roots look like? In this contribution, I argue that research on the origins of life might offer glimpses on the topology of these very roots. More specifically, I argue (1) that the roots of the tree of life go well below the level of the commonly mentioned ‘ancestral organisms’ down into the level of much simpler, minimally living entities that might be referred to as ‘protoliving systems’, and (2) that further below, a system of roots gradually dissolves into non-living matter along several functional dimensions. In between non-living and living matter, one finds physico-chemical systems that I propose to characterize by a ‘lifeness signature’. In turn, this ‘lifeness signature’ might also account for a diverse range of biochemical entities that are found to be ‘less-than-living’ yet ‘more-than-non-living’.

The plurality of definitions of life is often perceived as an unsatisfying situation stemming from still incomplete knowledge about ‘what it is to live’ as well as from the existence of a variety of methods for reaching a definition. For many, such plurality is to be remedied and the search for a unique and fully satisfactory definition of life pursued. In this contribution on the contrary, it is argued that the existence of such a variety of definitions of life undermines the very feasibility of ever reaching a unique unambiguous definition. It is argued that focusing on the definitions of specific types of ‘living systems’ – somehow in the same way that one can define specific types of ‘flying systems’ – could be more fruitful from a heuristic point of view than looking for ‘the’ right definition of life, and probably more accurate in terms of carving Nature at its joints.

Science is now studying biodiversity on a massive scale. These studies are occurring not just at the scale of larger plants and animals, but also at the scale of minute entities such as bacteria and viruses. This expansion has led to the development of a specific sub-field of “microbial diversity”. In this paper, I investigate how microbial diversity faces two of the classical issues encountered by the concept of “ biodiversity ”: the issues of defining the units of biodiversity and of choosing a mathematical measure of diversity. I also show that the extension of the scope of biodiversity to microbial entities such as viruses and many other not-clearly-alive entities raises yet another foundational issue: that of defining a “lower-limit” of biodiversity

In recent cancer research, strong and apparently conflicting epistemological stances have been advocated by different research teams in a mist of an ever-growing body of knowledge ignited by ever-more perplexing and non-conclusive experimental facts: in the past few years, an 'organicist' approach investigating cancer development at the tissue level has challenged the established and so-called 'reductionist' approach focusing on disentangling the genetic and molecular circuitry of carcinogenesis. This article reviews the ways in which 'organicism' and 'reductionism' are used and opposed in this context, with an aim at clarifying the debate. Methodological, epistemological and ontological implications of both approaches are discussed. We argue that the 'organicist/reductionist' opposition in the present case of carcinogenesis is more a matter of diverging heuristics than a claim about theoretical or ontological (ir)reducibility. As a matter of fact, except for the downward causation claim, which we question, we argue that the organicist arguments are compatible with the reductionist approach. Moreover, we speculate that both approaches, which currently focus on specific entities i.e., genes versus tissues, will need to shift their conceptual frameworks to studying complex arrays of relationships potentially ranging over several levels of entities, as is the case with 'systems biology'.

Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to many other types of formal networks and weakens the emergentist claim attached to bio-molecular networks, and by extension to living systems.