The bridges of Königsberg case has been widely cited in recent philosophical discussions on scientific explanation as a potential example of a structural explanation of a physical phenomenon. However, when discussing this case, different authors have focused on two different versions, depending on what they take the explanandum to be. In one version, the explanandum is the _failure_ of a given individual in performing an Eulerian walk over the bridge system. In the other version, the explanandum is the _impossibility_ of (...) performing an Eulerian walk over the bridges. The goal of this paper is to show that only the latter version amounts to a real case of a structural explanation. I will also suggest how to fix the first version, and show how my remarks apply to other purported cases of structural explanations. (shrink)

Artificial neural networks and supervised learning have become an essential part of science. Beyond using them for accurate input-output mapping, there is growing attention to a new feature-oriented approach. Under the assumption that networks optimised for a task may have learned to represent and utilise important features of the target system for that task, scientists examine how those networks manipulate inputs and employ the features networks capture for scientific discovery. We analyse this approach, show its hidden caveats, and suggest its (...) legitimate use. We distinguish three things that scientists call a ‘feature’: parametric, diagnostic, and real-world features. The feature-oriented approach aims for real-world features by interpreting the former two, which also partially rely on the network. We argue that this approach faces a problem of non-uniqueness: there are numerous discordant parametric and diagnostic features and ways to interpret them. When the approach aims at novel discovery, scientists often need to choose between those options, but they lack the background knowledge to justify their choices. Consequentially, features thus identified are not promised to be real. We argue that they should not be used as evidence but only used instrumentally. We also suggest transparency in feature selection and the plurality of choices. (shrink)

This paper employs network theory, mining data and bibliometric analysis when mapping the scientific contribution of Nobel Prize candidate; Manuel Sandoval Vallarta, the first and most renowned Mexican physicist and important figure in Latin American science. Vallarta died in 1977, and the existing literature is about his life and contributions to science but not about how those are still valuable today. This paper is the first to highlight, with mapping tools, that his contributions are relevant to the international community of (...) cosmic rays (as he was pioneer and leader), quantum mechanics and relativity. These tools delivered three findings: Identify how he built his own field of study, same as universal knowledge. Unveil that the backward and forward Vallarta citations follow a _scale-free_ network distribution. Determine social factors that benefited or affected his scientific activities—such as World War II interrupting Vallarta’s successful productivity at Massachusetts Institute of Technology. Furthermore, this study confirmed the interdisciplinary nature of the mapping studies of the scientist's contributions using scientometric tools. As a result, several interesting questions arose throughout our research, some of which were answered from the history and philosophy of science. However, others need to be analyzed by experts in the fields of Vallarta. Mapping research sends an invitation to interdisciplinary dialogue/research between experts in different areas of study to better understand the process of knowledge production both, individual and collective. (shrink)