Anouk Barberousse

Philosophy of science studies the methods, theories, and concepts used by scientists. It mainly developed as a field in its own right during the twentieth century and is now a diversified and lively research area. This book surveys the current state of the discipline by focusing on central themes such as confirmation of scientific hypotheses, scientific explanation, causality, the relationship between science and metaphysics, scientific change, the relationship between philosophy of science and science studies, the role of theories and models, and unity of science. These themes define general philosophy of science. The book also presents subdisciplines in the philosophy of science dealing with the main sciences: logic, mathematics, physics, biology, medicine, cognitive science, linguistics, social sciences, and economics. Although it is common to address the specific philosophical problems raised by physics and biology in such a book, the place assigned to the philosophy of special sciences is much more unusual. Most authors collaborate on a regular basis in their research or teaching and share a common vision of philosophy of science and its place within philosophy and academia in general. The chapters have been written in close accordance with the three editors, thus achieving strong unity of style and tone.

Philosophical reflections on physics and its theories have been shaping the agenda of general philosophy of science, including issues such as the nature of scientific theories, the meaning of scientific terms, and scientific modeling. But physics is also well worth being considered a special science of its own, whose methods and tools raised specific questions for philosophers of science: this is the approach followed in this chapter, which has as its main thread the role of mathematics in physical theories. It tackles classical issues concerning measurement and determinism and long-standing controversies in the philosophy of statistical mechanics (how may the reversible laws of mechanics account for the irreversible principles of thermodynamics?) and in the philosophy of quantum physics (does quantum mechanics provide us a with a complete description of quantum phenomena?). It also addresses emerging issues in the field, such as computer simulations and their role between theory and observation.

How should we think of the dynamics of science? What are the relationships between an earlier theory and the theory that has superseded it? This chapter introduces the heated debates on the nature of scientific change, at the intersection of philosophy of science and history of science, and their bearing on the more general question of the rationality of the scientific enterprise. It focuses on the issue of the continuity or discontinuity of scientific change and the various versions of the incommensurability thesis one may uphold. Historicist views are balanced against nagging questions regarding scientific progress (Is there such a thing? If so, how should it be defined?), the causes of scientific change (Are they to be found within scientific method itself?), and its necessity (Is the history of scientific developments an argument in favor of realism, or could we have had entirely different sciences?).

Science studies have proposed a novel and integrated outlook on scientific activity, to be described in a quasi-anthropological way and without the supposed normative prejudice of most philosophers of science. This chapter examines the stiff competition between philosophy of science and science studies in accounting for what science really is about. This competition is best framed at a methodological level, in particular in terms of the relationship between the study of science and its history. Should one strive for ahistorical generalizations or focus on the irremediably singular low-level interactions that constitute the day-to-day business of scientific life? Should one accept to practice “judged history,” and put past theories in perspective with what we learned to be right and wrong about them, or should one refrain from any such overseeing perspective? The chapter concludes with prospects for accounting from within philosophy of science for the important phenomena that science studies have contributed.

BackgroundResearch on research integrity (RI) has grown exponentially over the past several decades. Although the earliest publications emerged in the 1980 s, more than half of the existing literature has been produced within the last five years. Given that the most recent comprehensive literature review is now eight years old, the present study aims to extend and update previous findings.MethodWe conducted a systematic search of the Web of Science and Constellate databases for articles published between 2015 and 2023. To structure our overview and guide our inquiry, we addressed the following seven broad questions about the field:-What topics does the empirical literature on RI explore?What are the primary objectives of the empirical literature on RI?What methodologies are prevalent in the empirical literature on RI?What populations or organizations are studied in the empirical literature on RI?Where are the empirical studies on RI conducted?Where is the empirical literature on RI published?To what degree is the general literature on RI grounded in empirical research? Additionally, we used the previous scoping review as a benchmark to identify emerging trends and shifts.Results Our search yielded a total of 3,282 studies, of which 660 articles met our inclusion criteria. All research questions were comprehensively addressed. Notably, we observed a significant shift in methodologies: the reliance on interviews and surveys decreased from 51 to 30%, whereas the application of meta-scientific methods increased from 17 to 31%. In terms of theoretical orientation, the previously dominant “Bad Apple” hypothesis declined from 54 to 30%, while the “Wicked System” hypothesis increased from 46 to 52%. Furthermore, there has been a pronounced trend toward testing solutions, rising from 31 to 56% at the expense of merely describing the problem, which fell from 69 to 44%.ConclusionThree gaps highlighted eight years ago by the previous scoping review remain unresolved. Research on decision makers (e.g., scientists in positions of power, policymakers, accounting for 3%), the private research sector and patents (4.7%), and the peer review system (0.3%) continues to be underexplored. Even more concerning, if current trends persist, these gaps are likely to become increasingly problematic.

This chapter presents the contribution of databanks to the development of biodiversity knowledge through the example of marine biodiversity databanks. Focusing on the marine field allows us to insist on the imbalance of the unknown vs. the better known part. The chapter emphasizes the role of taxonomic and genetic databanks as well as the ongoing transformations that databanks are submitted to in order to answer pressing demands due to the biodiversity crisis. It aims to analyse the requirements biodiversity databanks have to satisfy in order to help both researchers and conservationists in their respective endeavors. It begins by pointing out the main characteristics and limits of biodiversity knowledge and defend the view that databanks are well-suited to overcome these limits as soon as they are widely accessible and interoperable. These constraints are analysed as both technical and scientific. Their dynamic dimension is emphasized as databanks must comply with the rapid evolution of scientific knowledge. We also propose a view on the relationships between biodiversity knowledge, assessment, and conservation.

The starting point of the chapter is a twofold observation: (i) most current physical theories make use of a continuous parameter t that plays the role of time; (ii) the current practice of modeling, namely, of computational modeling, makes use of a discrete parameter t k (k = 1, 2, …, n) that plays the role of time, because computers cannot handle continuous quantities. A “parameter” here is a symbol that does not play the same role as the other symbols in the physical theories or models: whereas the other symbols represent physical quantities, t (or t k ) just plays the role of that within which other quantities evolve. It is “time”, but in a rather shallow sense.

La philosophie de la biologie est un domaine extrêmement actif de la recherche dans la tradition philosophique anglo-saxonne. Elle réunit philosophes et biologistes autour de la question de la définition des concepts fondamentaux : gène, cellule, organisme, espèce, développement, évolution, adaptation, etc. Ce livre, qui rassemble les contributions d’une trentaine de spécialistes français et étrangers, présente en 24 chapitres l’état de la recherche actuelle dans tous les principaux domaines de la biologie. Il peut être utilisé comme manuel pour les cours de philosophie des sciences au niveau Master.

Le Précis de philosophie des sciences vise à présenter, de manière pédagogique, l'état actuel des grandes questions et des grands domaines de la philosophie des sciences. C'est un ouvrage de niveau "intermédiaire", entre les ouvrages d'initiation et les ouvrages de recherche. Il peut être utilisé comme manuel pour des cours de philosophie des sciences au niveau Master, ainsi que dans le cadre de la préparation aux nouvelles épreuves d'épistémologie des CAPES scientifiques. Il a notamment pour vocation de servir de support de cours pour des enseignements en épistémologie et philosophie des sciences de niveau L3, M1 et M2.

For several decades now, biologists have been developing digital databanks, which are remarkable scientific instruments allowing scientists to accelerate the development of biological knowledge. From the beginnings of the Human Genome Project (HGP) onwards, genetic databanks have been a major component of current biological knowledge, and biodiversity databanks have also been developed in the wake of the HGP. The purpose of this paper is to identify the specific features of biodiversity data and databanks, and to point out their contribution to biodiversity knowledge.

Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only to be found in the detailed analysis of their semantic levels. We provide such an analysis and we determine the actual consequences of physical implementation for simulations

Les théories physiques sont aujourd'hui très mathématisées, et ce que les scientifiques manipulent pour décrire, prédire et contrôler les phénomènes, ce sont (entre autres) des équations, comportant de nombreux symboles mathématiques. Ces objets mathématiques n'ont pas de signification physique en eux-mêmes : ils ne « parlent » pas d'eux-mêmes des phénomènes. Une interprétation est nécessaire. Ce qui nous intéresse dans cet article est ainsi l'interprétation dont une théorie physique doit faire l'objet pour remplir son rôle. Nous commençons par expliciter une distinction traditionnelle : l'interprétation « pauvre » (simple instrument permettant d'assigner aux symboles de la théorie un sens physique strictement limité aux résultats des expériences) diffère de l'interprétation « riche » (laquelle compose une image du monde compatible avec la façon dont la théorie décrit mathématiquement les résultats des expériences). Notre but dans cet article est de montrer que cette distinction doit être amendée. Nous nous appuyons sur l'exemple de la mécanique quantique, mais la distinction se veut valable en général pour toute théorie physique.