Christophe Malaterre

Proof of Concept Research (PoCR) is a prevalent facet of scientific inquiry, yet its epistemic features remain poorly understood. While novelty has been highlighted as a key characteristic, projectability—understood as the likelihood of being applicable to a broader range of contexts—is another. This study endeavours to construct a formal model that elucidates the implicit ampliative reasoning inherent in PoCR. Our model hinges on probability assumptions for target objects to simultaneously exhibit three properties: one that is a defining characteristic of these target objects; a second that is desired of them and whose demonstration is the empirical aim of PoCR; and a third that is promised in the background. Depending on assumptions about when these properties jointly obtain, we delineate paradigmatic, alternative, and tangential modes of reasoning. This classification and associated decision tree unveil distinct argumentative strategies that, despite not being deductively valid, may be employed to motivate PoCR and justify subsequent inferences upon successful proof of concept demonstration. The model and decision tree together provide a framework with which to better understand the general structure of widely used inferences in PoCR, and with which researchers and evaluators can more precisely design and assess PoCR projects.

What is philosophy of science? Numerous manuals, anthologies, and essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this article, we address the question from a complementary perspective: we target the content of one major journal in 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 82 years. We also tracked those topics’ evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes in 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, and 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.

Scientific articles have semantic contents that are usually quite specific to their disciplinary origins. To characterize such semantic contents, topic-modeling algorithms make it possible to identify topics that run throughout corpora. However, they remain limited when it comes to investigating the extent to which topics are jointly used together in specific documents and form particular associative patterns. Here, we propose to characterize such patterns through the identification of “topic associative rules” that describe how topics are associated within given sets of documents. As a case study, we use a corpus from a subfield of the humanities—the philosophy of science—consisting of the complete full-text content of one of its main journals: Philosophy of Science. On the basis of a pre-existing topic modeling, we develop a methodology with which we infer a set of 96 topic associative rules that characterize specific types of articles depending on how these articles combine topics in peculiar patterns. Such rules offer a finer-grained window onto the semantic content of the corpus and can be interpreted as “topical recipes” for distinct types of philosophy of science articles. Examining rule networks and rule predictive success for different article types, we find a positive correlation between topological features of rule networks (connectivity) and the reliability of rule predictions (as summarized by the F-measure). Topic associative rules thereby not only contribute to characterizing the semantic contents of corpora at a finer granularity than topic modeling, but may also help to classify documents or identify document types, for instance to improve natural language generation processes.

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.

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.

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).

Numerous phenomena prompt inquiries into their origins, spanning from the cosmos and life, to species, civilizations, and pandemics. Answering these questions entails offering origin explanations. Here, I explore the distinctive characteristics of origin explanations that distinguish them from other types of explanations. I explicate the concept of an origin phenomenon and suggest conceptualizing an origin explanation as a specific form of causal explanation—one that reveals a bottleneck in the causal network leading to an origin phenomenon. The resulting framework highlights the specificity of origin explanations across disciplines, pinpointing distinctive topological features within the causal structure of the world.

Function pluralists argue against the possibility of any unified account of function that would capture the multiple functional ascriptions we make in our use of ‘function’. Here, I contribute to this pluralist view. I dissect a specific case of functional ascription in molecular biology—the attribution of functions to catalytic RNAs called ribozymes—and argue that it highlights serious issues for the philosophical project of proposing a unified account of function. These issues stem from the multiple contexts in which ribozymes are found: they occur in living organisms; they are artificially synthesized in the laboratory; they are even hypothesized to have spontaneously occurred on Earth 3.8 billion years ago, as proposed by the RNA-world scenario of the origin of life. I argue that ribosomal functional ascription in these different contexts supports a strong form of pluralism for accounts of function that I call ‘local-pluralism’.

It is a most commonly accepted hypothesis that life originated from inanimate matter, somehow being a synthetic product of organic aggregates, and as such a result of some sort of prebiotic synthetic biology. In the past decades, the newly formed scientific discipline of synthetic biology has set ambitious goals by pursuing the complete design and production of genetic circuits, entire genomes, or even whole organisms. I argue that synthetic biology might also shed some novel and interesting perspectives on the question of the origins of life, and that, in addition, it might challenge our most commonly accepted definitions of life, thereby changing the ways we might think about life and its origins.

Founded in 1974, Philosophiques has established itself as a leading journal of French-speaking philosophy in Quebec. Its fiftieth anniversary provides an opportunity to look back on half a century of articles, book reviews and thematic dossiers published in its pages. To this end, we present here the results of a computer-assisted thematic analysis of the complete corpus of the journal to date, comprising 1569 texts of philosophical content. This analysis carried out using two thematic modeling algorithms, LDA and Top2Vec, also provides an opportunity to compare the performance of these two approaches. The results reveal the diversity of topics addressed in the journal since the Quiet Revolution. The evolution of these topics over time highlights the various influences and currents that have marked contemporary Quebec philosophy, thus contributing to a better understanding of the history of philosophical thought in Quebec.

The optimism vs. pessimism debate about the historical sciences is often framed in terms of arguments about the relative importance of overdetermination vs. underdetermination of historical claims by available evidence. While the interplay between natural processes that create multiple traces of past events (thereby conducive of overdetermination) and processes that erase past information (whence underdetermination) cannot be ignored, I locate the root of the debate in the epistemic granularity, or intuitively the level of detail, that pervades any historical claim justification network. To reveal the role played by granularity, I elaborate a model of historical claim justification. This model maps out the different elements that enter the justification of historical claims (incl., actual and inferred states of affairs, dating and information reconstructing theories). It also incorporates the different types of processes that affect traces of past events (information creating, preserving, modifying, and destroying processes). Granularity is shown to play a pivotal role in all elements of this model, and thereby in the inferred justification of any historical claim. As a result, while upward or downward shifts in granularity may explain changes about claims being considered as overdetermined or underdetermined, epistemic granularity constitutes an integral part of evidential reasoning in the historical sciences (and possibly elsewhere).

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.

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 ? Développées 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 connait 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 epistémique 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 epistémique dans lequel le phénomène est évalué, la qualification de l'apparition de la vie comme émergente est, ou non, justifié. 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.