2,541 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation

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    This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach. The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies

    Gabriel Harvey and the History of Reading: Essays by Lisa Jardine and others

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    Few articles in the humanities have had the impact of Lisa Jardine and Anthony Grafton’s seminal ‘Studied for Action’ (1990), a study of the reading practices of Elizabethan polymath and prolific annotator Gabriel Harvey. Their excavation of the setting, methods and ambitions of Harvey’s encounters with his books ignited the History of Reading, an interdisciplinary field which quickly became one of the most exciting corners of the scholarly cosmos. A generation inspired by the model of Harvey fanned out across the world’s libraries and archives, seeking to reveal the many creative, unexpected and curious ways that individuals throughout history responded to texts, and how these interpretations in turn illuminate past worlds. Three decades on, Harvey’s example and Jardine’s work remain central to cutting-edge scholarship in the History of Reading. By uniting ‘Studied for Action’ with published and unpublished studies on Harvey by Jardine, Grafton and the scholars they have influenced, this collection provides a unique lens on the place of marginalia in textual, intellectual and cultural history. The chapters capture subsequent work on Harvey and map the fields opened by Jardine and Grafton’s original article, collectively offering a posthumous tribute to Lisa Jardine and an authoritative overview of the History of Reading

    Distributed Ledger Technology (DLT) Applications in Payment, Clearing, and Settlement Systems:A Study of Blockchain-Based Payment Barriers and Potential Solutions, and DLT Application in Central Bank Payment System Functions

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    Payment, clearing, and settlement systems are essential components of the financial markets and exert considerable influence on the overall economy. While there have been considerable technological advancements in payment systems, the conventional systems still depend on centralized architecture, with inherent limitations and risks. The emergence of Distributed ledger technology (DLT) is being regarded as a potential solution to transform payment and settlement processes and address certain challenges posed by the centralized architecture of traditional payment systems (Bank for International Settlements, 2017). While proof-of-concept projects have demonstrated the technical feasibility of DLT, significant barriers still hinder its adoption and implementation. The overarching objective of this thesis is to contribute to the developing area of DLT application in payment, clearing and settlement systems, which is still in its initial stages of applications development and lacks a substantial body of scholarly literature and empirical research. This is achieved by identifying the socio-technical barriers to adoption and diffusion of blockchain-based payment systems and the solutions proposed to address them. Furthermore, the thesis examines and classifies various applications of DLT in central bank payment system functions, offering valuable insights into the motivations, DLT platforms used, and consensus algorithms for applicable use cases. To achieve these objectives, the methodology employed involved a systematic literature review (SLR) of academic literature on blockchain-based payment systems. Furthermore, we utilized a thematic analysis approach to examine data collected from various sources regarding the use of DLT applications in central bank payment system functions, such as central bank white papers, industry reports, and policy documents. The study's findings on blockchain-based payment systems barriers and proposed solutions; challenge the prevailing emphasis on technological and regulatory barriers in the literature and industry discourse regarding the adoption and implementation of blockchain-based payment systems. It highlights the importance of considering the broader socio-technical context and identifying barriers across all five dimensions of the social technical framework, including technological, infrastructural, user practices/market, regulatory, and cultural dimensions. Furthermore, the research identified seven DLT applications in central bank payment system functions. These are grouped into three overarching themes: central banks' operational responsibilities in payment and settlement systems, issuance of central bank digital money, and regulatory oversight/supervisory functions, along with other ancillary functions. Each of these applications has unique motivations or value proposition, which is the underlying reason for utilizing in that particular use case

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    Archaeological palaeoenvironmental archives: challenges and potential

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    This Arts and Humanities Research Council (AHRC) sponsored collaborative doctoral project represents one of the most significant efforts to collate quantitative and qualitative data that can elucidate practices related to archaeological palaeoenvironmental archiving in England. The research has revealed that archived palaeoenvironmental remains are valuable resources for archaeological research and can clarify subjects that include the adoption and importation of exotic species, plant and insect invasion, human health and diet, and plant and animal husbandry practices. In addition to scientific research, archived palaeoenvironmental remains can provide evidence-based narratives of human resilience and climate change and offer evidence of the scientific process, making them ideal resources for public science engagement. These areas of potential have been realised at an imperative time; given that waterlogged palaeoenvironmental remains at significant sites such as Star Carr, Must Farm, and Flag Fen, archaeological deposits in towns and cities are at risk of decay due to climate change-related factors, and unsustainable agricultural practices. Innovative approaches to collecting and archiving palaeoenvironmental remains and maintaining existing archives will permit the creation of an accessible and thorough national resource that can service archaeologists and researchers in the related fields of biology and natural history. Furthermore, a concerted effort to recognise absences in archaeological archives, matched by an effort to supply these deficiencies, can produce a resource that can contribute to an enduring geographical and temporal record of England's biodiversity, which can be used in perpetuity in the face of diminishing archaeological and contemporary natural resources. To realise these opportunities, particular challenges must be overcome. The most prominent of these include inconsistent collection policies resulting from pressures associated with shortages in storage capacity and declining specialist knowledge in museums and repositories combined with variable curation practices. Many of these challenges can be resolved by developing a dedicated storage facility that can focus on the ongoing conservation and curation of palaeoenvironmental remains. Combined with an OASIS + module designed to handle and disseminate data pertaining to palaeoenvironmental archives, remains would be findable, accessible, and interoperable with biological archives and collections worldwide. Providing a national centre for curating palaeoenvironmental remains and a dedicated digital repository will require significant funding. Funding sources could be identified through collaboration with other disciplines. If sufficient funding cannot be identified, options that would require less financial investment, such as high-level archive audits and the production of guidance documents, will be able to assist all stakeholders with the improved curation, management, and promotion of the archived resource

    Undergraduate Catalog of Studies, 2022-2023

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    Reframing museum epistemology for the information age: a discursive design approach to revealing complexity

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    This practice-based research inquiry examines the impact of an epistemic shift, brought about by the dawning of the information age and advances in networked communication technologies, on physical knowledge institutions - focusing on museums. The research charts adapting knowledge schemas used in museum knowledge organisation and discusses the potential for a new knowledge schema, the network, to establish a new epistemology for museums that reflects contemporary hyperlinked and networked knowledge. The research investigates the potential for networked and shared virtual reality spaces to reveal new ‘knowledge monuments’ reflecting the epistemic values of the network society and the space of flows. The central practice for this thesis focuses on two main elements. The first is applying networks and visual complexity to reveal multi-linearity and adapting perspectives in relational knowledge networks. This concept was explored through two discursive design projects, the Museum Collection Engine, which uses data visualisation, cloud data, and image recognition within an immersive projection dome to create a dynamic and searchable museum collection that returns new and interlinking constellations of museum objects and knowledge. The second discursive design project was Shared Pasts: Decoding Complexity, an AR app with a unique ‘anti-personalisation’ recommendation system designed to reveal complex narratives around historic objects and places. The second element is folksonomy and co-design in developing new community-focused archives using the community's language to build the dataset and socially tagged metadata. This was tested by developing two discursive prototypes, Women Reclaiming AI and Sanctuary Stories
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