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While research on digital dangers has been growing, studies on their respective solutions and justice responses have not kept pace. The agathokakological nature of technology demands that we pay attention to not only harms associated with interconnectivity, but also the potential for technology to counter offenses and “do good.” This chapter discusses technology as both a weapon and a shield when it comes to violence against women and girls in public spaces and private places. First, we review the complex and varied manifestations of technological gender violence, ranging from the use of technology to exploit, harass, stalk, and otherwise harm women and girls in communal spaces, to offenses that occur behind closed doors. Second, we discuss justice-related responses, underscoring how women and girls have “flipped the script” when their needs are not met. By developing innovative ways to respond to the wrongs committed against them and creating alternate systems that offer a voice, victims/survivors have repurposed technology to redress harms and unite in solidarity with others in an ongoing quest for justice.

Using COVID-19 pandemic as a more immediate empirical reference, this paper aims to understand the biosecurity risks arising from tourist activities and, through a more prospective analysis, to consider the relevance of public health issues in the context of tourism-sustainability nexuses.

The text assumes a hybrid format, incorporating elements resulting from empirical research and essayistic viewpoints. The collection of empirical elements was based on documental research in several sources, such as newspapers, international institutions of an intergovernmental nature and the discussion forum of the travel platform TripAdvisor.

By assuming mobility and large agglomerations of people from different origins, mass tourism has fostered multiple outbreaks of COVID-19 and the rapid global spread of contagion chains. The pandemic clearly exemplified the responsibility of tourism in the dispersion of biotic agents with severe ecological, economic, social and public health repercussions. It is, therefore, urgent to rethink the tourism growth trajectory and more effectively consider the biosecurity risks associated with mobility in discussions on tourism and sustainability. At the same time, tourism must be delineated in terms of the great aims of sustainability, and this transversal purpose to which it contributes should be considered an intrinsic condition of its own sectorial sustainability as an economic activity.

The biosecurity challenges posed by mass tourism are a very topical issue, still little considered in sustainability policies and on which there is a marked deficit in scientific research.

This paper aims to demonstrate the approach taken in delivering the quality and impact elements of Knowledge for Healthcare, the strategic development framework for National Health Service (NHS) library and knowledge services in England. It examines the work undertaken to enhance quality and demonstrate the value and impact of health library and knowledge services. It describes the interventions developed and implemented over a five-year period 2015–2020 and the move towards an outcome rather than process approach to impact and quality.

The case study illustrates a range of interventions that have been developed, including the outcomes of implementation to date. The methodology behind each intervention is informed by the evidence base and includes professional engagement.

The outcomes approach to the development and implementation of quality and impact interventions and assets provides evidence to demonstrate the value of library and knowledge staff to the NHS in England to both high-level decision-makers and service users.

The interventions are original concepts developed within the NHS to demonstrate system-wide impacts and change. The Evaluation Framework has been developed based on the impact planning and assessment (IPA) methodology. The interventions can be applied to other healthcare systems, and the generic learning is transferable to other library and knowledge sectors, such as higher education.

Family businesses feature prominently in economies, including the South African wine industry, using websites to convey their family identity. This research paper aims to explore the family identity elements that family wineries use on their websites, their alignment and how these are communicated online.

Based on Gioia’s methodology, a two-pronged approach was used to analyze 113 wineries’ websites’ text using Atlas. ti from an interpretivist perspective.

South African wineries use corporate identity, corporate personality and corporate expression to illustrate their familiness on their websites. It is portrayed through their family name and heritage, supported by their direction, purpose and aspirations, which emerge from the family identity and personality. These are dynamic and expressed through verbal and visual elements. Wineries described their behaviour, relevant competencies and passion as personality traits. Sustainability was considered an integral part of their brand promise, closely related to their family identity and personality, reflecting their family-oriented philosophy. These findings highlight the integration that exists among these components.

Theoretically, this study proposes a family business brand identity framework emphasising the centrality of familiness to its identity, personality and expression. Using websites to illustrate this familiness is emphasised with the recommendation that family businesses leverage this unique attribute in their identity to communicate their authenticity.

This study contributes to understanding what family wineries communicate on their websites, specifically by examining the elements necessary to create a family business brand based on the interrelationship between family identity, personality and expression with familiness at its core, resulting in a proposed family business brand identity framework.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.