7,334 research outputs found

    One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

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    OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

    Preferentialism and the conditionality of trade agreements. An application of the gravity model

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    Modern economic growth is driven by international trade, and the preferential trade agreement constitutes the primary fit-for-purpose mechanism of choice for establishing, facilitating, and governing its flows. However, too little attention has been afforded to the differences in content and conditionality associated with different trade agreements. This has led to an under-considered mischaracterisation of the design-flow relationship. Similarly, while the relationship between trade facilitation and trade is clear, the way trade facilitation affects other areas of economic activity, with respect to preferential trade agreements, has received considerably less attention. Particularly, in light of an increasingly globalised and interdependent trading system, the interplay between trade facilitation and foreign direct investment is of particular importance. Accordingly, this thesis explores the bilateral trade and investment effects of specific conditionality sets, as established within Preferential Trade Agreements (PTAs). Chapter one utilises recent content condition-indexes for depth, flexibility, and constraints on flexibility, established by DĂŒr et al. (2014) and Baccini et al. (2015), within a gravity framework to estimate the average treatment effect of trade agreement characteristics across bilateral trade relationships in the Association of Southeast Asian Nations (ASEAN) from 1948-2015. This chapter finds that the composition of a given ASEAN trade agreement’s characteristic set has significantly determined the concomitant bilateral trade flows. Conditions determining the classification of a trade agreements depth are positively associated with an increase to bilateral trade; hereby representing the furthered removal of trade barriers and frictions as facilitated by deeper trade agreements. Flexibility conditions, and constraint on flexibility conditions, are also identified as significant determiners for a given trade agreement’s treatment effect of subsequent bilateral trade flows. Given the political nature of their inclusion (i.e., the appropriate address to short term domestic discontent) this influence is negative as regards trade flows. These results highlight the longer implementation and time frame requirements for trade impediments to be removed in a market with higher domestic uncertainty. Chapter two explores the incorporation of non-trade issue (NTI) conditions in PTAs. Such conditions are increasing both at the intensive and extensive margins. There is a concern from developing nations that this growth of NTI inclusions serves as a way for high-income (HI) nations to dictate the trade agenda, such that developing nations are subject to ‘principled protectionism’. There is evidence that NTI provisions are partly driven by protectionist motives but the effect on trade flows remains largely undiscussed. Utilising the Gravity Model for trade, I test Lechner’s (2016) comprehensive NTI dataset for 202 bilateral country pairs across a 32-year timeframe and find that, on average, NTIs are associated with an increase to bilateral trade. Primarily this boost can be associated with the market access that a PTA utilising NTIs facilitates. In addition, these results are aligned theoretically with the discussions on market harmonisation, shared values, and the erosion of artificial production advantages. Instead of inhibiting trade through burdensome cost, NTIs are acting to support a more stable production and trading environment, motivated by enhanced market access. Employing a novel classification to capture the power supremacy associated with shaping NTIs, this chapter highlights that the positive impact of NTIs is largely driven by the relationship between HI nations and middle-to-low-income (MTLI) counterparts. Chapter Three employs the gravity model, theoretically augmented for foreign direct investment (FDI), to estimate the effects of trade facilitation conditions utilising indexes established by Neufeld (2014) and the bilateral FDI data curated by UNCTAD (2014). The resultant dataset covers 104 countries, covering a period of 12 years (2001–2012), containing 23,640 observations. The results highlight the bilateral-FDI enhancing effects of trade facilitation conditions in the ASEAN context, aligning itself with the theoretical branch of FDI-PTA literature that has outlined how the ratification of a trade agreement results in increased and positive economic prospect between partners (Medvedev, 2012) resulting from the interrelation between trade and investment as set within an improving regulatory environment. The results align with the expectation that an enhanced trade facilitation landscape (one in which such formalities, procedures, information, and expectations around trade facilitation are conditioned for) is expected to incentivise and attract FDI

    Moduli Stabilisation and the Statistics of Low-Energy Physics in the String Landscape

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    In this thesis we present a detailed analysis of the statistical properties of the type IIB flux landscape of string theory. We focus primarily on models constructed via the Large Volume Scenario (LVS) and KKLT and study the distribution of various phenomenologically relevant quantities. First, we compare our considerations with previous results and point out the importance of KĂ€hler moduli stabilisation, which has been neglected in this context so far. We perform different moduli stabilisation procedures and compare the resulting distributions. To this end, we derive the expressions for the gravitino mass, various quantities related to axion physics and other phenomenologically interesting quantities in terms of the fundamental flux dependent quantities gsg_s, W0W_0 and n\mathfrak{n}, the parameter which specifies the nature of the non-perturbative effects. Exploiting our knowledge of the distribution of these fundamental parameters, we can derive a distribution for all the quantities we are interested in. For models that are stabilised via LVS we find a logarithmic distribution, whereas for KKLT and perturbatively stabilised models we find a power-law distribution. We continue by investigating the statistical significance of a newly found class of KKLT vacua and present a search algorithm for such constructions. We conclude by presenting an application of our findings. Given the mild preference for higher scale supersymmetry breaking, we present a model of the early universe, which allows for additional periods of early matter domination and ultimately leads to rather sharp predictions for the dark matter mass in this model. We find the dark matter mass to be in the very heavy range mχ∌1010−1011 GeVm_{\chi}\sim 10^{10}-10^{11}\text{ GeV}

    Coloniality and the Courtroom: Understanding Pre-trial Judicial Decision Making in Brazil

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    This thesis focuses on judicial decision making during custody hearings in Rio de Janeiro, Brazil. The impetus for the study is that while national and international protocols mandate the use of pre-trial detention only as a last resort, judges continue to detain people pre-trial in large numbers. Custody hearings were introduced in 2015, but the initiative has not produced the reduction in pre-trial detention that was hoped. This study aims to understand what informs judicial decision making at this stage. The research is approached through a decolonial lens to foreground legacies of colonialism, overlooked in mainstream criminological scholarship. This is an interview-based study, where key court actors (judges, prosecutors, and public defenders) and subject matter specialists were asked about influences on judicial decision making. Interview data is complemented by non-participatory observation of custody hearings. The research responds directly to Aliverti et al.'s (2021) call to ‘decolonize the criminal question’ by exposing and explaining how colonialism informs criminal justice practices. Answering the call in relation to judicial decision making, findings provide evidence that colonial-era assumptions, dynamics, and hierarchies were evident in the practice of custody hearings and continue to inform judges’ decisions, thus demonstrating the coloniality of justice. This study is significant for the new empirical data presented and theoretical innovation is also offered via the introduction of the ‘anticitizen’. The concept builds on Souza’s (2007) ‘subcitizen’ to account for the active pursuit of dangerous Others by judges casting themselves as crime fighters in a modern moral crusade. The findings point to the limited utility of human rights discourse – the normative approach to influencing judicial decision making around pre-trial detention – as a plurality of conceptualisations compete for dominance. This study has important implications for all actors aiming to reduce pre-trial detention in Brazil because unless underpinning colonial logics are addressed, every innovation risks becoming the next lei para inglĂȘs ver (law [just] for the English to see)

    Karttatypografia: luettavuuden parantaminen kirjainmuotoilun keinoin topografisissa kartoissa

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    This thesis examines the legibility of type on maps and aims to find out ways to improve it through type design. As type often is an integral part of maps – something that helps the map user navigate, understand, and perceive a wide range of information in an effective way – type design and legibility must be regarded as important design elements. However, even though cartography and typography have extensive theoretical bases, the subject of legibility has not been comprehensively researched in cartographic context. Thus, by combining type design theory and scientific legibility studies with cartographic theory, the legibility of type on maps could be improved. The topic is first studied by an extensive literature review to cover existing concepts and theories of cartography, cartographic typography, and typography. After a competent knowledge basis of these concepts and theories is acquired, the findings are utilised in the design component. The design component is a type family designed specifically to be used with topographic maps: it consists of two elements, a project description that follows the design process of the type family, relating design choices to the theoretical findings and perspectives presented in the literary review, and the finished type family. In conclusion of the design component, several visual studies are made both to compare the design component (type family) to other relevant typefaces, and to validate the possible functionality of the design component in the chosen cartographic application (topographic map). A broad understanding of the topics of the literature review was formed. Cartographic theory observed the overall nature of maps and specified the various map elements and their intended uses. Cartographic typography deepened the understanding of type on maps – it highlighted the specific needs that must be taken into consideration, demonstrated the diversity of typographic situations that might occur, and presented a large set of guidelines to help the mapmaker to achieve better results. Typography and type design focused on the micro-level of type: how the minor design choices affect the whole, and furthermore, through legibility studies, validated certain views and brought new topics into consideration. By combining theoretical literature from these domains, this thesis helped to form a foundation for an improved framework for type de-sign for (topographic) maps. Furthermore, the domains of cartographic typography and typography and type design gave clear suggestions on how the legibility of type on topographic maps can be improved: legibility of type in this context constitutes from multiple components that must be both taken into consideration and be applied to processes of mapmaking and type design.TĂ€ssĂ€ opinnĂ€ytetyössĂ€ tutkitaan karttatypografiaa ja pyritÀÀn löytĂ€mÀÀn keinoja parantaa luettavuutta kirjainmuotoilun keinoin. Teksti on usein elimellinen osa karttoja: se helpottaa kartan kĂ€yttĂ€jÀÀ navigoimaan ja sisĂ€istĂ€mÀÀn suuren mÀÀrĂ€n informaatiota tehokkaasti. SiispĂ€ kirjainmuotoilua ja luettavuutta tulee pitÀÀ tĂ€rkeinĂ€ karttasuunnittelun työkaluina. Vaikka sekĂ€ kartografiassa ettĂ€ typografiassa on olemassa laajat teoreettiset perustat, luettavuutta ei ole kattavasti tutkittu kartografisessa kontekstissa. YhdistĂ€mĂ€llĂ€ kirjainmuotoilun ja tieteelliset luettavuustutkimukset kartografiseen teoriaan, karttatekstien luettavuutta voidaan parantaa. Aluksi tutustutaan olemassa oleviin konsepteihin ja kartografisiin teorioihin kattavan kirjallisuuskatsauksen avulla. Kun tarpeellinen tietopohja on rakennettu, saavutettua tietĂ€mystĂ€ hyödynnetÀÀn opinnĂ€ytetyön projektiosassa, joka tĂ€ssĂ€ tapauksessa on topografisten karttojen yhteydessĂ€ kĂ€ytettĂ€vĂ€ kirjainperhe. Projektiosio on kaksijakoinen ja pitÀÀ sisĂ€llÀÀn sekĂ€ valmiin kirjainperheen, ettĂ€ projektikuvauksen. Projektikuvaus seuraa suunnitteluprosessia ja peilaa tehtyjĂ€ valintoja kirjallisuuskatsauksessa esiteltyihin löydöksiin. Projektiosion pÀÀtelmĂ€ssĂ€ tutkitaan visuaalisesti kirjainperheen toimintaa ja kĂ€yttökelpoisuutta topografisessa karttaympĂ€ristössĂ€, sekĂ€ verrataan kirjainperheen toimivuutta suhteessa muihin kirjaintyyppeihin. Tutkimuksen perusteella muodostuu laaja ymmĂ€rrys aiheesta. Kartografinen teoria valottaa yleisesti karttojen olemusta ja toimintaa, sekĂ€ esittelee erilaisia karttalementtejĂ€ ja niiden toimintatapoja. Karttatypografian teoria syventÀÀ ymmĂ€rrystĂ€ tekstin kĂ€yttĂ€ytymisestĂ€ karttaympĂ€ristössĂ€, esittelee karttatypografian erityispiirteitĂ€, ja tarjoaa laajan karttatypografisen ohjeiston. Typografian ja kirjainmuotoilun teoria keskittyy mikrotason aiheisiin: kuinka vĂ€hĂ€pĂ€töisiltĂ€ vaikuttavat suunnitteluvalinnat vaikuttavat kokonaisuuteen, ja kuinka luettavuustutkimukset auttavat nĂ€kemÀÀn asioita uudessa valossa. TĂ€mĂ€ opinnĂ€ytetyö auttaa parantamaan kirjainmuotoilua (topografisessa) karttaympĂ€ristössĂ€ yhdistĂ€mĂ€llĂ€ edellĂ€ mainittujen alojen teorioita keskenÀÀn ja pohjustamalla paranneltuja suunniteluvalintoja. Yhdistetty teoria viittaa selkeĂ€sti siihen, ettĂ€ luettavuus karttaympĂ€ristössĂ€ koostuu lukuisista osatekijöistĂ€ – nĂ€mĂ€ osatekijĂ€t tulee ymmĂ€rtÀÀ, ottaa huomioon, ja soveltaa sekĂ€ karttojen ettĂ€ niille suunniteltujen kirjaintyyppien suunnitteluprosesseissa

    Machine learning for managing structured and semi-structured data

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    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer grĂ¶ĂŸere Datenmengen verfĂŒgbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlĂ€sslich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren ZusammenhĂ€nge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfĂŒgbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmĂ€ĂŸigen Gittern auf allgemeine (unregelmĂ€ĂŸige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten ĂŒber EntitĂ€ten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollstĂ€ndig, d. h. es fehlen Fakten. Die manuelle ÜberprĂŒfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstĂŒtzt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der WissensgraphenvervollstĂ€ndigung lĂ€sst sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen EntitĂ€ten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame EntitĂ€ten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknĂŒpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der VervollstĂ€ndigung von Wissensgraphen vor. FĂŒr das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, wĂ€hrend die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die LeistungsfĂ€higkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. FĂŒr die Link Prediction demonstrieren wir, wie die Vorhersage fĂŒr unbekannte EntitĂ€ten zur Trainingszeit verbessert werden kann, indem zusĂ€tzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfĂŒgbar sind. GestĂŒtzt auf Ergebnisse einer groß angelegten experimentellen Studie prĂ€sentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugĂ€nglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik fĂŒr die Bewertung von Ranking-Ergebnissen vor, wie sie fĂŒr beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in FĂ€llen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen fĂŒr beide Aufgaben vorkommen

    Freelance subtitlers in a subtitle production network in the OTT industry in Thailand: a longitudinal study

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    The present study sets out to investigate a subtitle production network in the over-the-top (OTT) industry in Thailand through the perspective of freelance subtitlers. A qualitative longitudinal research design was adopted to gain insights into (1) the way the work practices of freelance subtitlers are influenced by both human and non-human actors in the network, (2) the evolution of the network, and (3) how the freelance subtitlers’ perception of quality is influenced by changes occurring in the network. Eleven subtitlers were interviewed every six months over a period of two years, contributing to over 60 hours of interview data. The data analysis was informed by selected concepts from Actor-Network Theory (ANT) (Law 1992, 2009; Latour 1996, 2005; Mol 2010), and complemented by the three-dimensional quality model proposed by Abdallah (2016, 2017). Reflexive thematic analysis (Braun and Clarke 2019a, 2020b) was used to generate themes and sub-themes which address the research questions and tell compelling stories about the actor-network. It was found that from July 2017 to September 2019, the subtitle production network, which was sustained by complex interrelationships between actors, underwent a number of changes. The changes affected the work practices of freelance subtitlers in a more negative than positive way, demonstrating their precarious position in an industry that has widely adopted the vendor model (Moorkens 2017). Moreover, as perceived by the research participants, under increasingly undesirable working conditions, it became more challenging to maintain a quality process and to produce quality subtitles. Finally, translation technology and tools, including machine translation, were found to be key non-human actors that catalyse the changes in the network under study

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods

    Songs without borders: complex interpretative song worlds and the audiences that inhabit them

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    The genre of music commonly referred to as art song often elicits emotionally charged responses in accounts of audience experiences. However, scholarship has largely neglected the object of inquiry where these responses and experiences materialise: the live art song event. The principal research task in this study is to investigate audience experience of live art song events in the UK. The audiences and events at the centre of this inquiry coalesce around the work of the art song promoter Oxford Lieder. Using a mixed method approach (questionnaire, diary methods and guided interviews), statistical and thematic analysis, and Interpretative Phenomenological Analysis, is applied to a dataset that utilises 82 individual participants’ experiences of live art song events, including regular attendees and those experiencing live art song for the first time. To frame the findings of this inquiry, this study establishes the concept of complex interpretative song worlds: defined as a collection of interactions that audience members draw upon to construct their experience of live art song events, through a dynamic and multi-faceted interplay with the system of possibilities afforded by live art song environments. In this study, complex interpretative song world theorising takes place across three levels of audiencing: (1) Interactions with the live art song domain (the norms, behaviours, and conventions of live art song environments) are gathered under three themes. Collecting activity sees a desire for participants to scrutinise song objects, embrace familiar artists and repertoire, and adopt a connoisseur-like approach to knowledge acquisition. Connecting activity reveals a prized sense of close psycho-social resonance, which takes place between songs, performers, spaces and everyday experiences. Venerating activity foregrounds a view of songs as inviolable objects, where perceived changes to songs are deemed heretical by some, examined through the (re)introduction of sung English translations into the live art song corpus. (2) Interactions with live art song objects (the lexical and musical features that make up songs) reveal the ways audience members process words and music, and prioritise either, or both features during live art song events. The presentation of these materials in ways that blur senses (sights and sounds), and time (before, during, and after performances), are shown to be as additive to audience member conceptualisations of the nature of lexical-musical relationships as they are disruptive. (3) Interactions with live art song actors (performers, producers, and audiences) reveal processes of role formation at work, where vocal acts, non-vocal acts, and fixed and non-fixed traits complicate the way audience members derive impressions of performers. Art song’s hybridity as a genre, which is not a dramatic form, yet ‘not not’ a dramatic form, reveals the imbricated way audience members construct identities of performers: as professional musicians; as human beings; and as inhabitants of roles defined textually through a song’s poetic content. This interdisciplinary study draws predominantly on three overlapping areas of scholarship, and makes new contributions to knowledge in all three. For musicology, this inquiry develops deeper understandings of live art song objects to complement the hegemony of hermeneutic, musico-analytical and historiographical research that typifies much of the existing art song literature. For audience studies, these findings provide new audiencing insights, by examining an art form not yet analysed by empirical audience research methods, and one that simultaneously combines both words and music as a mode of expression. For translation theory, this inquiry responds to calls within the existing literature for more research to understand the reception of translation in music. This study also generates dividends outside of the academy, providing new insights for performers and promoters of art song to inform approaches to programming, presentation, production, marketing and audience development
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