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Where the cities end. Intramural urban fabric, walls and continentia aedificia between epigraphic evidence and classical Roman jurisprudence
No full textThe paper offers an in-depth review of legal, literary and epigraphic sources for defining key terms and concepts of spatial organisation such as urbs, oppidum, suburbium, ager and the Augustan notion of continentia aedificia
Artificial Intelligence Algorithms for Distant Reading of Archives
No full textQuesta tesi indaga l’utilizzo dell’intelligenza artificiale (IA) per potenziare l’estrazione automatica di informazioni dai documenti d’archivio, con un duplice obiettivo: migliorare l’affidabilità e contenere i costi computazionali. Il lavoro prende spunto dall’uso sempre più diffuso dell’IA in ambito archivistico, sia da parte degli archivisti sia degli utenti, e mette in luce come l’accesso tradizionale si basi su metadati — informazioni strutturate derivanti dalla descrizione archivistica — che, pur essendo essenziali per il reperimento dei dati, limitano le possibili domande di ricerca con cui esplorare i fondi archivistici. Dal punto di vista degli utenti, l’IA può essere sfruttata per una “lettura a distanza” dei documenti, che consenta l’estrazione automatica di informazioni con cui arricchire i metadati già esistenti e ampliare così le possibilità di scoperta e analisi. Dopo un’introduzione alla scienza archivistica e alla necessità di integrare l’IA nei contesti archivistici, la tesi offre una rassegna dello stato dell’arte sui modelli visivo-linguistici (VLM) e sui grandi modelli linguistici (LLM), classificandoli in base alle architetture e alle funzioni di addestramento e illustrandone le applicazioni più recenti in contesti archivistici. Successivamente, vengono presentati due casi di studio empirici basati su dati sanitari: il primo utilizza XGBoost in un contesto di addestramento federato per estrarre informazioni diagnostiche da cartelle cliniche elettroniche; il secondo applica reti neurali convoluzionali (CNN) all’analisi di video in profondità di neonati pretermine per stimarne la posa, studiarne la motilità e valutare eventuali deficit motori. In entrambi i casi i dati sono documenti autonomi e non legati da alcun vincolo archivistico, in linea con il modo in cui gli strumenti automatici processano i record d’archivio. La tesi evidenzia poi i costi economici associati a questi strumenti — poiché CNN, VLM e LLM richiedono elevate risorse computazionali — e sottolinea come tale aspetto rappresenti un ostacolo per le istituzioni piccole o con limitate disponibilità, tipico soprattutto del settore sanitario. Per garantire coerenza con la teoria archivistica e mantenere l’affidabilità delle raccolte, il lavoro dedica particolare attenzione all’analisi di possibili errori e bias (specialmente per gli LLM) e fornisce regole empiriche su quando sia preferibile impiegare determinati modelli (in particolare, XGBoost). Colmando il divario tra il potenziale dell’estrazione di informazioni basata su IA e i vincoli pratici di costi e sostenibilità, questa ricerca propone non solo soluzioni tecnologiche avanzate, ma anche linee guida per lo sviluppo di sistemi di IA più economici, sostenibili e affidabili, in grado di rispondere alle rigorose esigenze di fiducia proprie del lavoro archivistico digitale.This dissertation investigates the use of artificial intelligence (AI) to enhance the automatic ex- traction of information from archival records, with a dual focus on improving trustworthiness and reducing computational costs. This research moves from the increasingly common use of AI inside archival contexts, both from archivists and archival users. Conventional archival access relies on metadata—structured information about records that result from the process of archival description—which, despite their crucial importance in data retrieval, limit the range of possible research questions through which an archive can be investigated. From the point of view of archival users, AI can be leveraged to conduct a distant read of records by automatically extracting information about them. In doing so, the research aims to provide novel access keys to enrich archival metadata, thereby expanding the scope of archival discovery and research. After introducing archival science and the call for the integration of AI in archival contexts, the thesis provides a state-of-the-art review of visual-language models (VLMs) and large language models (LLMs). The models are classified by architectures and the functions used to train them, including their recent applications in archival contexts. The thesis then provides two empirical case studies drawn from healthcare data. The first study involves a machine learning algorithm (XGBoost) in a federated learning context to extract diagnostic information from electronic health records, while the second study relies on convolutional neural networks (CNNs) to carry out human pose estimation on videos of preterm to study their motility and assess potential impairments. In these studies, data records are treated as independent, stand-alone documents, i.e., the same way in which automatic tools would process archival records. The dissertation also highlights the economic costs of these tools, which may hinder their deployment since CNNs, VLMs, and LLMs are compute-demanding models. This is important for small and underfunded institutions, like those in healthcare context, where resource constraints are a barrier to the adoption of advanced AI tools. Moreover, to comply with archival theory and the trustworthiness of archives, particular attention is given to the reliability of these models, providing insight about their possible errors and bias (in the case of LLMs), as well as empirical rules on when it might be best to use them (for XGBoost). By bridging the gap between the potential of AI-driven information extraction from data that may be records and the practical constraints of cost and sustainability, this dissertation provides a critical perspective on the future of digital archival research and AI. It proposes not only technological enhancements but also guidelines for developing more economically viable and reliable AI systems that meet the rigorous demands of archival trustworthiness
Fiscal incentives for energy poverty in Italy: Bridging the gap or missing the mark?
No full textThis study evaluates the effectiveness of Italian fiscal incentives for energy retrofitting, with a particular focus on their role in addressing energy poverty. It examines the distribution of these incentives across households, assessing their impact on energy-vulnerable groups using well-established energy-poverty indicators. Drawing on data from the 2022 Household Budget Survey by the Italian National Institute of Statistics (ISTAT), the analysis employs Propensity Score Matching (PSM) to determine the extent to which tax credits for energy-efficient renovations benefit energy-poor households—an aspect of policy effectiveness largely overlooked in the literature. The findings reveal that higher-income households disproportionately benefit from these incentives, highlighting inefficiencies in targeting mechanisms. Despite promoting energy efficiency improvements, fiscal subsidies remain largely inaccessible to low-income, energy-poor households. The study underscores the need for policy refinements, such as income-based eligibility criteria and enhanced outreach efforts, to ensure more equitable access to energy-saving incentives. Furthermore, it acknowledges data limitations, particularly the absence of longitudinal tracking, and calls for more granular data collection to assess long-term impacts effectively. These insights contribute to the broader discourse on optimizing fiscal policies to mitigate energy poverty and support sustainable energy transitions
The Paradoxes of AI in Management Education: Comparing Human and AI Feedback in Student Global Virtual Teams
No full textThe κ-Statistics Approach to Income Distribution Analysis
No full textThis Element presents the κ-generalized distribution, a statistical model tailored for the analysis of income distribution. Developed over years of collaborative, multidisciplinary research, it clarifies the statistical properties of the model, assesses its empirical validity, and compares its effectiveness with other parametric models. It also presents formulas for calculating inequality indices within the κ-generalized framework, including the widely used Gini coefficient and the relatively lesser-known Zanardi index of Lorenz curve asymmetry. Through empirical illustrations, the book criticizes the conventional application of the Gini index, pointing out its inadequacy in capturing the full spectrum of inequality characteristics. Instead, it advocates the adoption of the Zanardi index, accentuating its ability to capture the inherent heterogeneity and asymmetry in income distributions
Experiences of Learning, Teaching, and Investigating Chinese Philosophy in Europe
No full textIn this paper, I will present the experience gained in the two workshops—“
Teaching Chinese Philosophy in Europe” (October 13–15, 2023) and “Global
Chinese Philosophy: European Perspectives” (February 16–18, 2024)—held at Free
Berlin University and organized by Prof. Fabian Heubel and Prof. Hans Feger. The
argument will be divided, like the workshops themselves, into two directions. In
the first part, I will present the activity of the Education Network of the European
Association for Chinese Philosophy, my teaching activity with the specific perspectives,
strategies, and peculiarities. I will also present two dream courses that well
represent the peculiarities of my research path. In the second part, on the other
hand, I will illustrate a research project on the human-nature relationship that is
also an integral part of my university teaching activity. Starting from a definition
of new trends in ecological discourse, I will trace the specific Chinese contribution
of authors such as Wang Yangming, Wang Fuzhi, and Mou Zongsan to the global
debate through a comparison with the post-turn Heidegger
Kering's Strategy for Digital and Sustainable Transformation: Twin Transition Through NFTs in the Luxury Industry
No full textKering’s pioneering approach to integrating digital and sustainability strategies within the luxury industry heralds an era of responsible luxury. As an exemplar of the twin transition toward digital and sustainable futures, Kering leverages the synergistic potential of digitalization and sustainability to propel economic, social, and environmental progress. Through the lens of the Triple Bottom Line (TBL) framework, we explore how Kering’s focus on profit, people, and the planet guides its business practices, underscoring a holistic view of sustainable business impact. Kering’s journey from its foundational years to its contemporary status as a global luxury leader demonstrates strategic transformation and commitment to innovation, sustainability, and digital strategies.
The chapter delves into Kering’s digital forays, with a focus on its steps into the world of Non-Fungible Tokens (NFTs) and digital art, highlighting how these ventures align with sustainability goals and cater to evolving consumer expectations.
Findings underscore the viability of sustainable luxury, presenting Kering as a model for the industry’s future, where digitalization and responsibility coexist harmoniously in the luxury industry
Flipping the academic classroom: insights from an action-research in humanities
No full textThis paper makes a case for the implementation of the flipped learning model in higher education humanities classes.
While there is extensive evidence on the effectiveness of such a method in science classes where it was first introduced,
the practice of reversing the traditional learning model in humanities is still poorly discussed in research literature. Based
on a participatory action-research approach, the paper explores two applications of the flipped instructional model: one
in relation to skill-oriented classes on language teaching methodology, the other in relation to content-oriented theoretical
literary studies. Both case studies have been conducted with medium or small-sized classes of undergraduate students in
an Italian academic environment. The research design was qualitative: data have been collected through a semi-structured
students’ survey, from which an overall picture of the effectiveness of the flipped instructional model has been discussed
and evaluated. Findings indicated a highly positive appreciation of the efficacy of the flipped strategy in enhancing learning
outcomes, cognitive awareness, communication and interactive skills. These research findings are all the more remarkable
if we take into account the dominant teacher-centered educational culture of the Italian academic setting. Students’
satisfaction and engagement with the flipped learning method is therefore proof of the potential benefits inherent in
reversing the classroom and shifting the educational paradigm in the humanities