Metascientific ontology differs from philosophical ontologies in its objectives, objects and methods. By an examination of the ontological theories of Mario Bunge, we will show their main objective is a unified representation of the world as known through the sciences, that their objects of study are scientific concepts, and that their methods do not differ from those that one expects to find in any rational activity. Metascientific ontology is therefore not transcendent because it does not seek to represent non-concrete objects (...) alien to the world we inhabit and to the sciences that study it, and therefore does not need special faculties and methods to carry out its research. Metascientific ontology is a general scientific discourse on the world because it was designed by and for the sciences. (shrink)

L’ontologie métascientifique se distingue des ontologies philosophiques par ses objectifs, ses objets et ses méthodes. Par un examen des théories ontologiques de Mario Bunge, nous montrerons que leur principal objectif est l’élaboration d’une représentation unifiée du monde tel que connu via les sciences, que leurs objets d’étude sont les concepts scientifiques, et que leurs méthodes ne diffèrent pas de celles qu’on s’attend à trouver dans toute activité rationnelle. L’ontologie métascientifique n’est donc pas transcendante parce qu’elle ne cherche pas à représenter (...) des objets étrangers au monde que nous habitons et aux sciences qui l’étudient, et par conséquent elle n’a pas besoin de facultés ni de méthodes spéciales pour mener à bien ses recherches. L’ontologie métascientifique est un discours général scientifique sur le monde parce que conçue par et pour les sciences. (shrink)

Conceptual engineering is sometimes presented as an alternative to conceptual analysis. But one important objection to conceptual analysis threatens to carry across: that philosophy investigates phenomena—knowledge, truth, freedom, etc.—rather than concepts of those phenomena. This poses a prima facie problem insofar as conceptual engineering targets concepts or terms rather than phenomena. Call it the ‘phenomenon objection’. I begin by examining recent discussions of the phenomenon objection by Cappelen and Scharp, rejecting their responses. I then clarify and strengthen the objection, discussing (...) the challenge that the ‘strong phenomenon objection’ poses to conceptual engineers. Finally, I develop a new response to the strong phenomenon objection. In doing so, I motivate, articulate and defend an account of philosophy and its problems on which conceptual engineering is the appropriate method for tackling many philosophical problems—as well as, perhaps surprisingly, some problems in the sciences. (shrink)

While the term “metaphilosophy” enjoys increasing popularity in Kant scholarship, it is neither clear what distinguishes a metaphilosophical theory from a philosophical one nor to what extent Kant’s philosophy contains metaphilosophical views. In the first part of the article, I will introduce a demarcation criterion and show how scholars fall prey to the fallacy of extension confusing Kant’s philosophical theories with his theories about philosophy. In the second part, I will analyze eight elements for an “imperfect definition” of philosophy outlining (...) the scope of Kant’s explicit metaphilosophy against the backdrop of recent metaphilosophical research: scientific concept of philosophy, philosophy as an activity, worldly concept, philosophy as a science, philosophy as an architectonic idea, philosophy as a social practice and the appropriate holding-to-be-true, reason as the absolute condition and subject of philosophy, and methodology of philosophy. I will put these elements together for an attempt to give an imperfect definition of philosophy – something that Kant promised but never did – in the conclusion. (shrink)

Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.

A distinction often drawn is one between conservative versus revisionary conceptions of philosophical analysis with respect to commonsensical beliefs and intuitions. This paper offers a comparative investigation of two revisionary methods: Carnapian explication and ameliorative analysis as developed by S. Haslanger. It is argued that they have a number of common features, and in particular that they share a crucial political dimension: they both have the potential to serve as instrument for social reform. Indeed, they may produce improved versions of (...) key concepts of everyday life, for example those pertaining to social categories such as gender and race, which in turn may lead to social change. The systematic comparison of these two frameworks offered here, where similarities as well as differences are discussed, is likely to provide useful guidance to practitioners of both approaches, as it will highlight important aspects of each of them that tend to remain implicit and under-theorized in existing applications of these methodologies to specific questions. (shrink)

In this paper, I describe some strategies for teaching an introductory philosophy of science course to Science, Technology, Engineering, and Mathematics (STEM) students, with reference to my own experience teaching a philosophy of science course in the Fall of 2020. The most important strategy that I advocate is what I call the “Second Philosophy” approach, according to which instructors ought to emphasize that the problems that concern philosophers of science are not manufactured and imposed by philosophers from the outside, but (...) rather are ones that arise internally, during the practice of science itself. To justify this approach, I appeal to considerations from both educational research and the epistemology of testimony. In addition, I defend some distinctive learning goals that philosophy of science instructors ought to adopt when teaching STEM students, which include rectifying empirically well-documented shortcomings in students’ conceptions of the “scientific method” and the “nature of science.” Although my primary focus will be on teaching philosophy of science to STEM students, much of what I propose can be applied to non-philosophy majors generally. Ultimately, as I argue, a successful philosophy of science course for non-philosophy majors must be one that advances a student’s science education. The strategies that I describe and defend here are aimed at precisely that end. (shrink)