Strategic narrative and public diplomacy:What Artificial Intelligence Means for the Endless Problem of Plural Meanings of Plural Things

This 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

Essays on Automation and Future of Skills

The adoption of automation in the labour market introduces a complex array of effectson employment, skill demand, and human capital investment. This thesis encompassesthree distinct but interrelated studies exploring the multifaceted impacts of technologicaladvancement and automation on the labour market, focusing on different types of au-tomation and how it affects human capital investment and the demand for skills.Chapter 1 delves into the differentiated impact of various forms of automation on thehuman capital investment. Utilising data from the international survey of adult skills(PIAAC) across 22 European countries, the study categorises automation based on tasksand technology (e.g., software, robots, AI) and explores how these factors influence work-ers’ decisions to invest in training. The findings reveal a nuanced landscape where theeffect of automation on training varies significantly depending on the type of technologyand the level of a country’s technological readiness. For example, while AI is associatedwith an increase in the investment in human capital, the reverse is true for other automa-tion technologies.Chapters 2 and 3 exploit online job advertisements data in the UK to measure the changesin the employers’ demand for skills. One of the key challenges when using this type datais to correctly identify occupation from the advertisement text. In Chapter 2, I addressthis issue by proposing a novel methodology for classifying occupations using advancedlanguage processing models. To implement it, I used web-scraping to collect data onjob advertisements in the UK. Incorporating job titles and descriptions into the classi-fication process, in particular skill requirements, significantly improves the accuracy ofoccupational classification from job advertisements, opening new areas of labour marketresearch based on job ads.Building on the previous chapter, chapter 3 uses online vacancy data to examine thechanges in the demand for specific skills and the associated wage premiums within andacross occupations, especially during and after the COVID-19 pandemic. The analysisis based on the universe of job advertisements in UK, aggregated by online job plat-form Adzuna. This study uses a novel text classification method I developed by using aLarge Language Model (LLM), specifically the GPT-4 API, to categorise job postings intoskill groups. It finds that workers with ICT and AI skills command significantly higherwages compared to those with interpersonal skills. Interestingly, the study suggests thatCOVID-19 has had no long-term impact on either the demand for specific skills withinan occupation or the wages offered for those skills.Collectively, these studies offer critical insights into the evolving dynamics of the labourmarket in the face of technological change, emphasising the importance of adaptabilityin skill development and the potential that the advanced data analysis techniques offerfor informing policy and practice

Loneliness, office space arrangement and mental well-being of Gen Z PR professionals.:Falling into the trap of an agile office?

Purpose – The purpose of the study was to assess how the well-being and loneliness of public relations and communication professionals are impacted by the post-pandemic characteristics of the work environment: flexible work schemes, non-territorial office arrangements and video communication technologies. It was hypothesised that the post-pandemic workplace landscape poses several new challenges to the practice of PR – an industry which invariably relies on working with other people and demands a good level of social resilience. Loneliness and well-being both depend on the experience of having good and efficient social relationships, but the pandemic has directly and indirectly led to their deterioration.Design/methodology/approach – The project employed a correlational design and used an online survey system to collect responses from Gen Z professionals employed in the public relations and communications industry in the UK and the US via the Prolific platform. Demographical and workplace- related characteristics were assessed to investigate links with loneliness (measured using a three-item scale adopted from Russell et al., 1980 in Hughes, 2004) and well-being (using a short Warwick- Edinburgh Mental Well-being Scale scale). Causal relationships between data were tested using regression analysis for continuous variables and analysis of covariance for categorical factors. Bootstrapping was used to test mediated relationships that explain loneliness, job satisfaction and the well-being of Gen Z PR professionals.Findings – Several types of flexible working schemes, defined as the ability to work from home on any number of weeks, showed an impact on loneliness and job satisfaction but not on well-being. However, all remaining aspects of the post-pandemic office did manifest as important predictors. In the sample, 30% of Gen Z PR professionals showed signs of mild to clinical levels of depression, and the best protection from this state was the presence of a significant other. Lower levels of loneliness were related to non-territorial office arrangements and job satisfaction. The use of hot desks and open-plan arrangements led to a significantly lower level of job satisfaction than a traditional, cellular office. Both excessive online meetings and face-to-face only interactions led to marginally lower levels of loneliness and job satisfaction.Research limitations/implications – The present research is limited in several aspects. Firstly, while the project evaluated loneliness, job satisfaction and mental well-being (with each of these elements including a component of the requirement for building effective relationships), the quality of relationships built by PR professionals was not measured. Secondly, the project focused only on post-pandemic aspects of the workplace and did not cover other important components of job satisfaction. Lastly, the measure of online meetings was declarative rather than behavioural, and greater control of the number of online meetings held would be required to show more reliable links between variables.Practical implications – This study calls for proposing recommendations for employers to develop organisational-level measures and programmes to counteract loneliness. While traditionally intimate relationships of employees were not a direct focus of HR programmes, employers should develop elements of organisational culture that would support employees in building effective intimate relationships. Separately from this, despite immediate financial benefits, employers should avoid using open-space and hot desk policies, as they contribute negatively to job satisfaction (and indirectly to well-being). The sample of UK and US professionals was chosen for analysis because in these countries employers have more capacity to introduce changes to tangible characteristics of the workplace and work culture, which may positively impact the well-being of their employees.Social implications – It is expected that both employers and employees will revisit their approach to post- pandemic financial and logistic challenges related to the workplace. A lower level of job satisfaction and well- being is linked to the lack of assigned office space, but the ability to work exclusively from home leads to loneliness. Employees – when offered this possibility – should work in offices they are provided. Employers must appreciate the negative link between open and hot-desking policies and job satisfaction and well-being of their employees.Originality/value – This study is the first to examine the post-pandemic workplace and personal characteristics of public relations and communications professionals in the UK and US and show how they impact job satisfaction and well-being. The study shows that 30% of employed in the PR industry are at risk of depression or anxiety. The connecting factor between personal and work-related characteristics that explains this problem is loneliness

Contrastive Translation With Dynamical Temperature for Sequential Recommendation

Contrastive learning is a promising solution to the problem of data sparsity in the field of recommendation system since it is able to extract self-supervised signals from raw data. The traditional contrastive learning-based sequential recommendation algorithms generate augmentations of original item sequences by utilizing crop, mask and reorder operations. However, those augmentation schemes destroy the underlying semantics of item sequences, resulting in difficulty in accurately defining positive and negative samples. To address this issue, we propose a contrastive translation based sequential recommendation algorithm, namely, CT4Rec. Specifically, CT4Rec generates augmented views of item sequences by injecting noises into embeddings of users and items, which is able to guarantee that the underlying semantics of augmented views are consistent with those of original item sequence. Hence, CT4Rec is able to effectively learn the invariances among the augmented views. In addition, the personalized translation operations are utilized to model the third-order relationships among entities. Moreover, it is difficult for contrastive learning-based recommendation algorithms with static temperature to simultaneously capture the differences among individual users/items and among the clusters of users/items. Hence, we utilize a dynamic temperature strategy to enhance CT4Rec, which endows CT4Rec with the capabilities of group-wise discrimination and instance discrimination. Our validation on five benchmark datasets shows that CT4Rec outperforms SOTA sequential recommendation methods. Our code is released at https://github.com/zar123123/CT4Rec

The Capacity of a Finite Field Matrix Channel

The Additive-Multiplicative Matrix Channel (AMMC) was introduced by Silva, Kschischang and Kötter in 2010 to model data transmission using random linear network coding. The input and output of the channel are $n\times m$ matrices over a finite field $\mathbb{F}_q$. When the matrix $X$ is input, the channel outputs $Y=A(X+W)$ where $A$ is a uniformly chosen $n\times n$ invertible matrix over $\mathbb{F}_q$ and where $W$ is a uniformly chosen $n\times m$ matrix over $\mathbb{F}_q$ of rank $t$.Silva et al. considered the case when $2n\leq m$. They determined the asymptotic capacity of the AMMC when $t$, $n$ and $m$ are fixed and $q\rightarrow\infty$. They also determined the leading term of the capacity when $q$ is fixed, and $t$, $n$ and $m$ grow linearly. We generalise these results, showing that the condition $2n\geq m$ can be removed. (Our formula for the capacity falls into two cases, one of which generalises the $2n\geq m$ case.) We also improve the error term in the case when $q$ is fixed