José Ferraz-Caetano

Scientists often disagree on the best theory to describe a scientific event. While such debates are a natural part of healthy scientific discourse, the timeframe for scientists to converge on an ideal method may not always align with real-life knowledge dynamics. In this article, I use an event from the history of chemistry as inspiration to develop Agent-Based Models of epistemic networks, exploring method selection within a scientific community. These models reveal several situations where incorrect, simpler methods can persist, even when substantial evidence supports a more complex method. This becomes particularly evident when different evidence-sharing timeframes are analyzed. The network structure connecting the scientists plays a crucial role in determining how and when convergence on the correct method is achieved, guided by real-world evidence. This framework provides a foundation for further exploration of scientists’ behavior in past and future discoveries, as well as how agents internalize scientific information.

This article explores the institutionalization process of scientific organizations, with a focus on the case-study of the Laboratório Químico Municipal do Porto (LQMP). Within the context of Institutionalization Theory (IT) and framed by the history of chemistry, the LQMP’s lifecycle is examined to propose a new model for the institutionalization of scientific entities. The article reveals the dynamic interplay between historical developments in chemical science, micro-level scientific practices and macro-level societal changes, offering a comprehensive understanding of how scientific institutions influence public policy and societal norms. Through the lens of IT, the article provides a nuanced exploration of the LQMP’s inception, development, and closure, shedding light on the broader implications of scientific institutionalization. This research addresses a gap in LQMP’s institutional understanding as a historical peripheral landmark in the emergence of science-based legislation. Its findings contribute to the theoretical discourse on the institutionalization of scientific practices, with implications for policy formulation on public health and food regulation.

Gamification can be a useful tool when engaging young students with science. When designing a new game, it is pivotal that a featured storytelling element can draw students' attention while providing an enjoyable experience. However, building such an insightful science dissemination narrative from scratch is often challenging. As real-life situations are frequently more appealing to students, adding alternative history elements can be helpful when designing such a game. In this paper, we present a novel educational framework to design an educational, science-based storytelling activity using the history of science and alternative hypothesis as ground tools for its inception. By analysing student's feedback when playing a science-based game with a historic episode outline, they report that their learning experience was fun and relatable, matching their scientific performance during the game.

In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases can be used to predict and understand how scientific evidence can influence the emergence (or fall-back) of science based-legislation. We also present frameworks on how to use past episodes of History of Science and Alternative History insights to build epistemic models, based on previous successful approaches on the spread of scientific misinformation. These models will help the accuracy of the design of eventual alternative realities, that can come insightful on present decision making methodologies.

Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on ‘description’, ‘classification’, and ‘prediction’, we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing ‘things in the world’, these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research questions? In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors.