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Globally, teachers are operating in environments influenced by past, current and anticipated crises. Students today need to develop the critical skills that will empower them to be agents of change in response to these crises. Education for global citizenship offers an approach that can mediate both content and process priorities, yet many teachers do not have the tools and strategies needed to deliver these dual outcomes. Habermas’ theory of communicative action offers a framework through which teachers can harness the potential of the so-called learning lifeworld to educate for global citizenship. This is of particular importance when considering education through the lens of international sustainable development. The contextualisation of communicative acts within the learning lifeworld offers the prospect of elevating students as agentic leaders within their communities. This chapter focuses on and unpacks the concept of education for global citizenship as a key tool for overcoming current crises and positions the theory of communicative action as a viable theoretical framework in the delivery of that concept. The ethnographic case study presented explores students’ perspectives on how their learning lifeworlds shape their identities, highlighting the role of culture, society and person in combatting lifeworld colonisation and nurturing global citizens. It finds that the theory of communicative action can be used as a tool to help students develop self-directedness and independence. It is argued teachers can use communicative acts to promote and model the values of education for global citizenship, ultimately better preparing today’s students for tomorrow’s world.

This opening chapter outlines the background and focus of this book and conceptualises our key terms, such as ‘international development’ and ‘crises’. This chapter explains that by examining the relationship between education and international sustainable development in the context of crises, this book aims to provide a more comprehensive understanding of the role that education can play in international development and how international developments can shape education. The structure of this book is outlined as well at the end of the chapter.

This study explored whether women could “have it all,” both at home and in the workplace. Using neoliberal feminism, mental load theory and intergenerational perspective as theoretical frameworks, this study explored how neoliberal ideologies which emphasized individual agency, economic empowerment and self-responsibility interact with persistent gendered expectations/norms to influence women’s experiences in navigating familial commitments and career aspirations.

Around 140 (N = 140) women living in Australia were recruited to participate in a qualitative, open-ended questionnaire that aimed to explore their: (1) perceptions and (2) expectations about (a) how gender roles evolved for them from youth to adulthood in various contexts, (b) how their family structures and dynamics, such as attitudes toward marriage, caregiving and/or household responsibilities, have changed and (c) what has/have influenced their career aspirations and family choices.

Results revealed a trend of women who worked hard at home and professionally. Unlike women who in the past lived more traditional lives, women in our cohort focused on their career as an important part of their identity and self-fulfillment. However, many women did report heightened mental load, stress and a lack of physical exercise in their daily lives.

This study revealed complex interplay between societal norms, intergenerational influences and the cognitive burdens associated with managing multiple roles. By examining these dynamics and using an integrated theoretical framework, the article aimed to holistically explain the challenges women in Australia encounter as they try to balance familial obligations with career ambitions within changing socioeconomic contexts.

The present study explored the young women's lived experiences of discrimination and othering based on skin tone in two rural localities of Uttarakhand , State of India. The authors used intersectionality as the theoretical lens for this study.

The authors have adopted an interpretive phenomenological study in the conduct of this research. The authors interviewed twelve female participants in person using a semi-structured interview schedule. The data were analysed using the six-stage data analysis process of interpretive phenomenological analysis.

The study's findings underline the experiences of stigma, negative self-concept, marriage is a complex reality, media's influence and skin whitening is the first and last resort. Dark-skinned women experience stressful life events due to their skin tone and society's prejudice favouring white and fair skin tones. The experiences of bullying, social shame, guilt and low esteem were also vivid.

This study reveals women's exposure to negative experiences of skin-tone-based discrimination prevalent in Indian society. This is one of the first kinds of such study in India that captures the dark-hued women's recurrent phenomenon of discrimination in their daily lives. It further shows that skin-tone bias and discrimination are widely prevalent and practised despite the claims that Indian society is free from skin-tone biasedness and subsequent discrimination.

This study explores the crucial role of competitive intelligence (CI) in the tourism sector’s strategic decision-making. CI has significantly transformed the tourism sector through new insights and sophistication in data analysis and strategic planning. The rise in tourism-related competition, due to new destinations, varied tourist preferences and sustainability emphasis, makes competitive intelligence essential for understanding future market trends and making informed strategic choices.

Utilising PRISMA techniques for bibliometric analysis, the study examines literature from 1998 to 2023 (WoS), focusing on service innovation, customer experience management and sustainable strategies. It presents an analysis of the evolution of CI in tourism, its impact, influential works and future research directions.

Findings show that the multidisciplinary nature of CI in tourism is further evidenced by studies on quality cues, travellers’ information needs and the utilisation of big data. Future studies need to understand both global trends and regional specifics, as shown in investigations of spatial-temporal tourism dynamics.

This study represents a novel contribution to the field of tourism research by offering a comprehensive bibliometric analysis of CI literature from 1998 to 2023. It uniquely integrates service innovation, customer experience management and sustainable strategies within the context of CI, highlighting its multidisciplinary impacts and evolution. These insights collectively emphasise the need for future innovation and a comprehensive understanding of the global-local nexus to inform future tourism research and practice.

Competitiveness is an important personality trait that has been studied in various disciplines and has been shown to predict critical work outcomes at the individual level. Despite this, the role of competitiveness in groups and teams has received scant attention amongst organizational researchers. Aiming to promote future research on the role of competitiveness as both an adaptive and maladaptive trait – particularly in the context of work – the authors review competitiveness and its effects on individual and team stress and Well-Being, giving special attention to the processes of cohesion and conflict and situational moderators. The authors illustrate a dynamic multilevel model of individual and team difference factors, competitive processes, and individual and team outcomes to highlight competitiveness as a consequential occupational stressor. Furthermore, the authors discuss the feedback loops that inform the different factors, highlight important avenues for future research, and offer practical solutions for managers to reduce unhealthy competition.

The purpose of this study is to contribute to previous research on customer relationships by quantitatively examining differences in the monetary benefits obtained by consumers using social and commercial relationships to make purchases from small and medium-sized enterprises (SMEs).

Customer transaction and relationship data from an SME in the USA is used to quantitatively assess the value of different marketplace relationships in an entrepreneurial context. Tobit regression is used to empirically model and test the impact of specific relationship characteristics on customer discounts.

Customers using social connections to make purchases obtain significantly larger discounts than customers using commercial connections; customers using direct connections attain significantly larger discounts than consumers using indirect connections (referrals). Interestingly, when examined by connection type, direct and indirect connections do not produce significant differences for social connections, yet they yield notable differences for commercial connections. The findings provide valuable insights to entrepreneurs for understanding and managing customer relationships.

This study empirically demonstrates that social relationships can be both prevalent and influential in the marketplace. The methodology used to quantitatively assess the monetary value associated with different methods of engaging with SMEs allows objective comparisons among different types of customer relationships. Quantification also allows important relationship characteristics to be empirically examined, including how the relationships compare to one another and to nonpersonal marketing activities. Ultimately, these novel contributions generate important insights to help marketers and entrepreneurs better understand customer relationships.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.