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Strategic Narratives of China’s Foreign Policy: Host Diplomacy and National Image Branding
Get PDFThis thesis studies how China has used host diplomacy for national image branding. Mega diplomatic events in China deserve more research attention given their growing concerns and implications. Existing research has focused on summit diplomacy but has failed to explore the host’s perspective. Research on China’s host diplomacy has relied primarily on internal factors, resulting in overstating its effectiveness. Previous studies have disregarded the domestic audiences in host diplomacy. This project analyses three China’s host diplomacy events: (1) Belt and Road Forum, (2) World Internet Conference, and (3) China International Import Expo. The three case studies allow investigation of China’s rationale to use host diplomacy to (re)shape its global brand equity and address domestic social, economic and political challenges in the steps of strategic narratives and nation branding. The thesis combines both theoretical and empirical contributions to the subject via narrative analysis. The data for this project was collected through participant observation and semi-structured interviews on-site in 2019. The thesis finds that China adopts system, identity and policy narratives to foreign and Chinese audiences differently, with intended and unintended overlaps. The thesis examines the China brand, showing that it is effective domestically due to its socio-political setting. Its effectiveness varies internationally from producing positive engagement to shaping global public policy. The findings reveal internal and external factors underlying the formation and projection of narratives to maximise the host advantages of favourable timing, location and people, and control of agenda-setting. The project improves identification and conceptual definitions of host diplomacy and its application in strategic narratives and nation branding, building up theoretical linkages with their accompanying rationale in China. Ultimately, these findings address longstanding questions in IR about identity, communication and power, enable a direct contribution to strategic narratives in constructivist IR and assist practitioners in understanding diplomatic events in the framework of host diplomacy. This study demonstrates how China leverages host diplomacy as a strategic tool to construct an alternative world order while positioning itself as a provider of global public goods
Machine Learning Approaches for WiFi Round Trip Time Indoor Positioning Systems
Get PDFIndoor positioning systems based on WiFi Round-Trip Time (RTT) measurements, as per the IEEE 802.11mc standard, have demonstrated sub-metre level accuracy using trilateration under ideal indoor conditions. However, the efficacy of WiFi RTT positioning in complex, non-line-of-sight (NLOS) environments remains an open research question. Therefore, this thesis addresses the challenge by proposing novel machine learning algorithms and validating their performance through extensive empirical experiments in real-world testbeds. Recent literature has shown improvements in WiFi fingerprinting systems utilising deep learning methods, achieving sub-metre accuracy. However, it was observed that simpler neural networks can sometimes outperform complex ones in certain environments. Moreover, our comprehensive survey of public WiFi datasets has identified several limitations, all of which pose challenges to accessing or accurately using these datasets over time. To provide a comprehensive analysis of WiFi RTT for indoor positioning, we investigate its properties in several real-world indoor environments on heterogeneous smartphones. We present three publicly available datasets collected on large-scale real-world scenarios, containing both RTT and received signal strength (RSS) signal measures. Using the proposed datasets, we achieved a baseline accuracy below 0.7 metres. WiFi RTT has shown promising sub-metre level accuracy under a clear line-of-sight path to the user. However, typical workplace environments often cause wireless signals to reflect, attenuate, and diffract. Identifying the NLOS condition of WiFi Access Points (APs) is thus crucial for indoor positioning systems. To this end, we propose a novel feature selection algorithm for NLOS identification of WiFi APs. Utilising RSS and RTT as inputs, our algorithm employs multi-scale selection and machine learning-based weighting methods to identify the most optimal feature sets, achieving an accuracy of up to 98% in NLOS detection of APs. Different WiFi technologies and algorithms for indoor positioning have strengths and weaknesses that vary by location. Thus, we propose an algorithm to dynamically switch to the most effective positioning model for an unknown location using a machine learning-based weighted model selection algorithm. We evaluated our algorithm across different complex real-world indoor scenarios, demonstrating an improvement of up to 1.8 metres compared to the standard WiFi fingerprinting technique.<br/
Strategic narrative and public diplomacy:What Artificial Intelligence Means for the Endless Problem of Plural Meanings of Plural Things
Get PDFThis chapter advances a narrative approach to the study of public diplomacy. Webring together two phenomena: information disorder in communication, and order in world politics, to examine the challenges of narrating public diplomacy. We examine how actors can use tools of information disorder to further their strategic aims to shape international order. We do this in several ways. First, we set out these two (dis)order phenomena and their relationship. Second, we set out the dilemma of establishing and verifying truth claims in this information disorder. Third, we demonstrate why analysis of actors’ strategic narratives used in this context can explain how they are using information disorder to further their claims. Fourth and finally, we explore how generative artificial intelligence (AI) offers new tools for communication in foreign policy. It is important to examine both how actors use these tools, and how they try to control and direct the development of these tools. We argue that these tools add another dimension to a contested multipolarinternational order, one that extends a basic problem that generates politics: different people in different places prioritise different things and give things different meanings. Generative AI will not change this or solve this. This means an increasing complexity of communication since we wrote of strategic narrative in 2010. However, the distinct practices of actors using narratives to shape behaviour, and narratives being fundamental to how citizens view the world, remains unchanged
Facilitating generative AI literacy in the face of evolving technology:Interventions in marketing classrooms
Get PDFThe emergence of generative AI (GenAI) has illustrated that higher education needs to adapt to the technology. Its speed of evolution requires that we adequately prepare students for an ever-changing landscape. Toward achieving that aim, we draw on the concept of interpretive flexibility, where the interpretations, uses, and outcomes of a new technology can differ and evolve over time, often with dominant stakeholders controlling the process. To engage marketing students in this process, we propose that they be presented with these diverse interpretations now as part of GenAI literacy. Specifically, we offer three small-scale pedagogical interventions designed to address this urgent need. Given the newness of GenAI, our interventions are designed to be infused into existing marketing instruction, instead of requiring a redesign of a curriculum. With each intervention, students not only significantly decrease their confidence in the accuracy of what GenAI produces but also see reasons to examine the implications of it. Both these outcomes, we suggest, could help to maintain interpretive flexibility required to properly respond to and guide the technology as its uses, impacts, and evolution become evident. We encourage educators to prioritize a comprehensive notion of GenAI literacy in their pedagogy to maintain interpretive flexibility
Stability and change of basic personal values in mid-to-late adolescence:A 4-year longitudinal study
Get PDFThis paper presents the first longitudinal examination of stability and change in the 19 values of Schwartz’s refined theory. A total of N = 465 high-school students (75% boys) participated in the study. The Portrait Values Questionnaire-Revised was administered four times over three years in mid-to-late adolescence (ages 15–18). We investigated multiple types of stability. At the mean level, Power-Dominance and Universalism-Nature increased significantly in importance compared to the other values. By contrast, the relative importance of Benevolence, Stimulation, Hedonism, and Face decreased significantly. Correlations between the growth parameters of the 19 values showed that change occurred in a coherent and organized manner, mirroring the circular structure of Schwartz’s theory. A medium-to-high degree of rank-order consistency was observed over 3 years, with coefficients ranging from .30 (Self-Direction-Action) to .56 (Conformity-Rules). On average, overall and distinctive profile stabilities were .66 and .45, respectively. Whereas the hierarchical order of values was consistent over time for most adolescents, there were important interindividual differences in stability patterns. The results from this study are discussed and related to earlier findings on value change during adjacent developmental periods. Taken together, they contribute to drawing a roadmap of value development in late adolescence toward early adulthood
Queering Cloud: Music, Gender and Sexuality in Video Games
Get PDFListeners use music to construct, organize and shape their gender and sexual identities. This chapter proposes that music can act as a binding agent between player and game, and may encompass the performance of gender and sexuality in games. Games, and game music, allow us to play with the feeling of performing genders and sexualities beyond our everyday lives. The mimetic and motoric properties of music, linking players with avatars, lets us “feel along” with genders and sexualities in games.In Robert Yang’s game Stick Shift (collected as part of Radiator 2), music is used to present and structure a queer sexual experience for players, inviting them to share in the erotic trajectory. In Final Fantasy VII: Remake, players musically adopt and perform different orientations related to gender and sexuality: the hero Cloud’s heterosexual experience is presented visually and musically, so players can listen and feel along with the music as an analogue for his sensations. Later, a sequence uses rhythm-game mechanics and mimetic motor imagery of the music to help us feel along with an intimate queer encounter. Overall, the chapter argues that games can use music (1) as an analogue for sexual encounters, (2) to invoke musical tropes and traditions to explore desire, (3) for gestures that provide a sense of performance and embodiment, (4) as an opportunity to resist essentialist notions of sexuality, and (5) to open out gender depiction beyond heteronormative assumptions
Measuring Software Resilience Using Socially Aware Truck Factor Estimation
Get PDFContinued timely maintenance is a key aspect of project security, but typically requires in-depth knowledge of a project's code base. Truck Factor is a metric that aims to represent how vulnerable a project is to losing this knowledge through the attrition of key contributors. However, the accuracy of existing Truck Factor estimators scales poorly with project size since they tend to ignore influential team members in managerial roles, which are more common in large projects.This work proposes SNet, a novel socially aware Truck Factor estimator based on social network analysis. SNet uses network centrality measures and social signals such as GitHub Issue interactions to estimate Truck Factor and identify Truck Factor contributors. We evaluate SNet against an existing ground truth comprised of twenty-six open source projects. Our social network analysis approach achieves superior contributor classification performance (Median F1 score = 0.8) while reducing computation time by over 2x compared to state-of-the-art estimators
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