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Part of book: ISBN 978-1-009-10023-6QC 20221219

Extractivism has been predominant in the Arctic since whaling and sealing campaigns began in the sixteenth century, followed by mining and drilling for oil and gas. In this chapter we present some of the main features of this ‘extractivist history’ of the circumpolar region. We organize this development along a set of themes. First, we explore the extractive frame of mind in Western thought and how it has continued to shape visions of the region. Second, we explore the material and social impacts of historical extractivism. Third, we use the theoretical lens of colonialism and decolonialism to understand the social and political relations, especially with aboriginal populations. Fourth, we examine the, often fraught, recent and contemporary debates around contemporary and future extractivism and its implications for the Arctic. The historical overview serves the purpose of providing a legible pattern from what is also a range of diverse and rich variations. A key finding is that extractivism is a lasting legacy and a path dependency of the region. At the same time resource extraction has many problematic sides that the seeking of new Arctic futures will have to deal with. QC 20221215Chapter in book: ISBN 978-1-009-10023-6

Part of book: ISBN 978-1-009-10023-6QC 20221219

This chapter explores knowledge associated with the humanities that hasbeen developed in practice-oriented research domains of the Swedishgovernment to help solve societal challenges. I study the Swedish NationalDefense Research Establishment [Försvarets forskningsanstalt] (FOA). Theconcept of “borderline humanities” refers to research activities that didnot abide by academic distinctions between the humanities, the socialsciences, and the natural sciences. The study shows how knowledge onhuman culture, history, language, and beliefs developed in a researchenvironment that drew on diverse fields of both research and practice.The chapter brings to the fore shared themes and concepts betweendifferent research fields and draws attention to how this affects the viewof research impact. Part of book: ISBN 978-90-485-5502-4QC 20220815

QC 20221011 ERC StG 716211

This article discusses David Lowenthal's last book, Quest for the Unity of Knowledge, which was published posthumously by Routledge in 2019 (available in print from November 2018). The book is based on a series of lectures that he gave while a visiting fellow with the KTH Royal Institute of Technology’s Environmental Humanities Laboratory in Stockholm in 2012. Aimed at a general academic audience, it is an erudite and passionate overview showing how ingrained bias towards unity or diversity shapes major issues in education, religion, genetics, race relations, heritage governance, and environmental policy. Quest for the Unity of Knowledge explores the Two Cultures debate, initiated by C.P. Snow, concerning the gulf between the sciences and the humanities. It covers areas such as conservation, ecology, history of ideas, museology, landscape, and heritage studies, aligning with Lowenthal's career-long research interests, and serving as well as a meta-comment to the emerging Environmental Humanities. QC 20220412

In the late twentieth century, as the United Nations struggled to come up with a new legal system for the oceans, one woman saw the opportunity to promote radical new ideas of justice and internationalism. Ocean governance expert Elisabeth Mann Borgese (1918–2002) spent decades working with the United Nations Law of the Sea Convention. Throughout this sprawling series of global conferences, she navigated allegiances and enmities, intrigues and setbacks, fighting determinedly to develop a just ocean order. Featuring extensive research and new interviews with Mann Borgese’s colleagues and family, this book explores timeless questions of justice and international collaboration and asks whether the extraordinary drive and vision of a single person can influence the course of international law. QC 20220412

The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. The objective of this thesis was to evaluate a state-of-the-art model for Document Layout Analysis on a public and custom dataset. Additionally, the objective was to build a pipeline for building a dataset specifically for Visually Rich Documents. The research methodology consisted of a literature study to find the state-of-the-art model for Document Layout Analysis and a relevant dataset used to evaluate the chosen model. The literature study also included research on how existing datasets in the domain were collected and processed. Finally, an evaluation framework was created. The evaluation showed that the chosen multi-modal transformer network, LayoutLMv2, performed well on the Docbank dataset. The custom build dataset was limited by class imbalance, although good performance for the larger classes. The annotator tool and its auto-tagging feature performed well and the proposed pipelined showed great promise for creating datasets with Visually Rich Documents. In conclusion, this thesis project answers the research questions and suggests two main opportunities. The first is to encourage others to build datasets with Visually Rich Documents using a similar pipeline to the one presented in this paper. The second is to evaluate the possibility of creating the visual token information for LayoutLMv2 as part of the transformer network rather than using a separate CNN. Användningen av Deep Learning-metoder för dokumentförståelse har anammats av forskarvärlden de senaste åren. Ett krav för Deep Learning-metoder och speciellt Transformer Networks är tillgång till stora datamängder. Syftet med denna avhandling var att utvärdera en state-of-the-art modell för analys av dokumentlayout på en offentligt tillgängligt dataset. Dessutom var målet att bygga en pipeline för att bygga en dataset specifikt för Visuallt Rika Dokument. Forskningsmetodiken bestod av en litteraturstudie för att hitta modellen för Document Layout Analys och ett relevant dataset som användes för att utvärdera den valda modellen. Litteraturstudien omfattade också forskning om hur befintliga dataset i domänen samlades in och bearbetades. Slutligen skapades en utvärderingsram. Utvärderingen visade att det valda multimodala transformatornätverket, LayoutLMv2, fungerade bra på Docbank-datasetet. Den skapade datasetet begränsades av klassobalans även om bra prestanda för de större klasserna erhölls. Annotatorverktyget och dess autotaggningsfunktion fungerade bra och den föreslagna pipelinen visade sig vara mycket lovande för att skapa dataset med VVisuallt Rika Dokument.svis besvarar detta examensarbete forskningsfrågorna och föreslår två huvudsakliga möjligheter. Den första är att uppmuntra andra att bygga datauppsättningar med Visuallt Rika Dokument med en liknande pipeline som den som presenteras i denna uppsats. Det andra är att utvärdera möjligheten att skapa den visuella tokeninformationen för LayoutLMv2 som en del av transformatornätverket snarare än att använda en separat CNN.

The agricultural sector is the main contributor for the warming from non-CO2 gases, especially methane and nitrous oxide. Existing measures to mitigate these emissions can only reduce but not eliminate these emissions. Owing to the diffused nature of these emissions, it is hard to design a single point measure to address the emissions from the agricultural sector. In our work, we present the first-of-a-kind direct air capture-based process to mitigate these diverse emissions. The process is designed based on thermal catalytic route for the methane conversion, which is coupled to a direct air capture unit for CO2 capture. The process was modelled based on steady state assumptions to estimate the energy requirement per tonne of CO2 equivalent mitigated. Energy estimations were later compared for the two methane removal systems with and without CO2 capture unit. The energy demand per tonne CO2-equivalent removed from the system without CO2 capture unit (only CH4 removal) was found to be 16.54 GJ. For the methane removal system with CO2 capture unit (co-removal of CO2 and CH4), the energy demand is 15.42 GJ per tonne-CO2 equivalent. QC 20230120

School teachers spend approximately 30 percent of their time grading exams and other assessments. With an increasingly digitized education, a research field have been initiated that aims to reduce the time spent on grading by automating it. This is an easy task for multiple-choice questions but much harder for open-ended questions requiring free-text answers, where the latter have shown to be superior for knowledge assessment and learning consolidation. While results in previous work have presented promising results of up to 90 percent grading accuracy, it is still problematic using a system that gives the wrong grade in 10 percent of the cases. This has given rise to a research field focusing on assisting teachers in the grading process, instead of fully replacing them. Cluster analysis has been the most popular tool for this, grouping similar answers together and letting teachers process groups of answers at once, instead of evaluating each question one-at-a-time. This approach has shown evidence to decrease the time spent on grading substantially, however, the methods for performing the clustering vary widely between studies, leaving no apparent methodology choice for real-use implementation. Using several techniques for pre-processing, text representation and choice of clustering algorithm, this work compared various methods for clustering free-text answers by evaluating them on a dataset containing almost 400 000 student answers. The results showed that using all of the tested pre-processing techniques led to the best performance, although the difference to using minimum pre-processing were small. Sentence embeddings were the text representation approach that performed the best, however, it remains to be answered how it should be used when spelling and grammar is part of the assessment, as it lacks the ability to identify such errors. A suitable choice of clustering algorithm is one where the number of clusters can be specified, as determining this automatically proved to be difficult. Teachers can then easily adjust the number of clusters based on their judgement. Skollärare spenderar ungefär 30 procent av sin tid på rättning av prov och andra bedömningar. I takt med att mer utbildning digitaliseras, försöker forskare hitta sätt att automatisera rättning för att minska den administrativa bördan för lärare. Flervalsfrågor har fördelen att de enkelt kan rättas automatiskt, medan öppet ställda frågor som kräver ett fritt formulerat svar har visat sig vara ett bättre verktyg för att mäta elevers förståelse. Dessa typer av frågor är däremot betydligt svårare att rätta automatiskt, vilket lett till forskning inom automatisk rättning av dessa. Även om tidigare forskning har lyckats uppnå resultat med upp till 90 procents träffsäkerhet, är det fortfarande problematiskt att det blir fel i de resterande 10 procenten av fallen. Detta har lett till forskning som fokuserar på underlätta för lärare i rättningen, istället för att ersätta dem. Klusteranalys har varit det mest populära tillvägagångssättet för att åstadkomma detta, där liknande svar grupperas tillsammans, vilket möjliggör rättning av flera svar samtidigt. Denna metod har visat sig minska rättningstiden signifikant, däremot har metoderna för att göra klusteranalysen varierat brett, vilket gör det svårt att veta hur en implementering i ett verkligt scenario bör se ut. Genom att använda olika tekniker för textbearbetning, textrepresentation och val av klusteralgoritm, jämför detta arbete olika metoder för att klustra fritext-svar, genom att utvärdera dessa på nästan 400 000 riktiga elevsvar. Resultatet visar att mer textbearbetning generellt är bättre, även om skillnaderna är små. Användning av så kallade sentence embeddings ledde till bäst resultat när olika tekniker för textrepresentation jämfördes. Däremot har denna teknik svårare att identifiera grammatik- och stavningsfel, hur detta ska hanteras är en fråga för framtida forskning. Ett lämpligt val av klustringsalgoritm är en där antalet kluster kan bestämmas av användaren, då det visat sig svårt att bestämma det automatiskt. Lärare kan då justera antalet kluster ifall det skulle vara för få eller för många.