Antonis Antoniou

This systematic literature review of factors influencing public trust in science provides insights for strengthening science-society relationships and informing responsible research practices. Our analysis of 124 empirical studies reveals multiple factors that are linked to trust in science, which we organize into three key categories: (a) Receiver—individual characteristics of the public, (b) Message—scientific information and its delivery, and (c) Source—scientists and the scientific method. Our synthesis demonstrates that trust in science is related to multiple factors, from individual factors (prior beliefs, attitudes, values, epistemic beliefs) to communication (such as channel credibility, ethics communication) and scientific practices (open science, replicability). Our findings have several important implications for research practice, science communication and education. Most notably, the findings highlight the pivotal role of research integrity in fostering and maintaining public trust in science, with scientists’ perceived integrity, epistemic practices that support transparency, and epistemic inclusion all being fundamentally interconnected in shaping public trust.

Modified Newtonian Dynamics (MOND) is a framework of theories that adjust Newton's laws of gravity to explain effects such as galactic rotation anomalies, offering an alternative to dark matter. This essay examines the justification of MOND by assessing its inter-theoretical relationship to established theories across relevant scales, in particular its connection to Newtonian gravitation. We argue that MOND fails a key condition for a theory's justification—what we call 'reduction-wise justification'—since it does not adequately reduce to Newtonian gravity in a fully non-arbitrary way. More precisely, despite satisfying the standard formal criteria for successful limiting reduction, MOND does not properly reduce to Newtonian gravitation because of (i) the absence of a fundamental theoretical framework to justify the interpolating function introduced in MOND and (ii) the lack of a unified mathematical structure working across all scales, independent of Newtonian theory. Hence, the case study of MOND provides crucial results for the general debate on inter-theoretic reduction in science: MOND’s failure as a case of reduction highlights important limitations in standard accounts of limiting reduction. We respond by proposing a more refined framework for limiting reduction that introduces two additional criteria to better distinguish successful from pathological reductions. More broadly, this case illustrates how analysing reduction-wise justification can serve as a powerful tool for evaluating the validity of novel theories that are not yet empirically established.

Certain considerations from cosmology (Ellis, in: arXiv preprint, 2006. arXiv:astro-ph/0602280 ; Stud Hist Philos Mod Phys 46:5–23, 2014) and other areas of physics (Sklar, in: PSA Proceedings of the biennial meeting of the philosophy of science association, pp. 551–564, 1990; Frisch, in: Philos Sci 71:696–706, 2004) pose challenges to the traditional distinction between laws and initial conditions, indicating the need for a more nuanced understanding of physical modality. A solution to these challenges is provided by presenting a conceptual framework according to which laws and fundamental lawlike assumptions within a theory’s nomic structure determine what is physically necessary and what is physically contingent from a physical theory’s point of view. Initial conditions are defined within this framework in terms of the possible configurations of a physical system allowed by the laws and other lawlike assumptions of a theory. The proposed deflationary framework of physical modality offers an alternative way of understanding the distinction between laws and initial conditions and allows the question of the modal status of the initial conditions of the Universe to be asked in a meaningful way.

The perturbative treatment of realistic quantum field theories, such as quantum electrodynamics, requires the use of mathematical idealizations in the approximation series for scattering amplitudes. Such mathematical idealizations are necessary to derive empirically relevant models from the theory. Mathematical idealizations can be either controlled or uncontrolled, depending on whether current scientific knowledge can explain whether the effects of the idealization are negligible or not. Drawing upon negative mathematical results in asymptotic analysis (failure of Borel summability) and renormalization group theory (failure of asymptotic safety), we argue that the mathematical idealizations applied in perturbative quantum electrodynamics should be understood as uncontrolled. This, in turn, leads to the problematic conclusion that such theories do not have theoretical models in the natural understanding of this term. The existence of unquestionable empirically successful theories without theoretical models has significant implications both for our understanding of the theory-model relationship in physics and the concept of empirical adequacy.

Current cosmological observations place little constraints on the nature of dark matter, allowing the development of a large number of models and various methods for probing their properties, which seem to provide ideal grounds for the employment of robustness arguments. In this article, the extent to which such arguments can be used to overcome various methodological and theoretical challenges is examined. The conclusion is that although robustness arguments have a limited scope in the context of dark-matter research, they can still be used to increase the scientists’ confidence about the properties of specific models.

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice.

What are scientific models? Philosophers of science have been trying to answer this question during the last three decades by putting forward a number of different proposals. Some say that models are best understood as abstract Platonic objects or fictional entities akin to Sherlock Holmes, while others focus on their mathematical nature and see them as set theoretical structures. Although each account has its own strengths in offering various insights on the nature of models, several objections have been raised against these views which still remain unanswered, making the debate on the ontology of models seem unresolvable. The primary aim of this paper is to show that a large part of these difficulties stems from an inappropriate reading of the main question on the ontology of models as a purely metaphysical question. Building on Carnap, it is argued that the question of the ontology of scientific models is either an internal theoretical question within an already accepted linguistic framework or an external practical question regarding the choice of the most appropriate form of language in order to describe and explain the practice of scientific modelling. The main implication of this view is that the question of the ontology of models becomes a means of probing other related questions regarding the overall practice of scientific modelling, such as questions on the capacity of models to provide knowledge and the relation of models with background theories.