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Scholars working in the field of the economics of education have long documented how investment in girls’ education delivers the greatest return in terms of societal wellbeing (see, e.g., Dollar & Gatti, 1999; Forbes, 2000; Klasen, 2002). Unfortunately, mainstream economists have quite a narrow interpretation of wellbeing, the rate of growth in gross domestic product (GDP) usually serves as the main barometer. The problem here is that higher rates of economic growth do not necessarily imply improved outcomes in terms of gender equality. Getting girls in school certainly represents progress. There are 129 million girls around the world who are not in school (UNICEF, 2022), and there are numerous obstacles to overcome if this problem is to be seriously addressed. The good news is that far more attention is being devoted to girls’ education and the empowerment of women today than in the past, as countless NGOs and charities have sprung up in recent years that focus on these issues. A key question, however, is whether it is sufficient just to get girls to attend school. Less attention seems to be paid to what they actually do when they get there. Admittedly, some education is better than none at all, but what if girls attended schools where the measure of success was less focussed on exam results, getting a good job, and growing GDP? In this chapter, we present the case for school education that aligns with the Sustainable Development Goals (SDGs). Green School for Girls (GS4G) – a pilot programme – seeks to implement a curriculum and pedagogy that will produce strong, independently minded, entrepreneurial young women, capable of earning a good living for themselves and their families in a fashion that is in harmony with the interests of the community and the natural environment.

This study aims to assess the sources of COVID-19 information respondents relied for COVID-19 pandemic information access and use, forms of misinformation and their influence on COVID-19 vaccine uptake hesitancy among frontline workers.

A Google Form developed questionnaire, distributed conveniently through link shared through WhatsApp groups was used to collect data from frontline workers from Dar es Salaam and Dodoma cities. Analysis was done using a binary regression analysis.

It was found that it was not the source that mattered for one to be hesitancy or not on COVID-19 vaccination, but the extent to which the information the source channels was manipulated or false.

Relying only Google Form questionnaire sent through a link on WhatsApp may have compromised the quality of information gathered and the quality of conclusion. Another study may have conducted through researcher administered questionnaire to a bigger sample to increase conclusion reliability and validity.

This study urges that to increase the COVID-19 vaccine uptake, it is important to ensure that the quality of information from the revealed dependable sources is checked to avoid possible consequential disquiet resulting from misinformation.

As the world is striving toward combating the COVID-19 or at least lessening its effects, this paper is of its own kind, using the theory of informative fictions to guide the assessment of the sources of information and the extent to which they influence misinformation on COVID-19 vaccine uptake hesitancy.

This study aims to explore the gendered nature of housing insecurity by investigating how gender affects women’s experience moving from transitional to market housing. By describing women’s pathways out of supportive or transitional housing support, the authors show how patriarchal forces in housing policies and practices affect women’s efforts to find secure housing. The authors argue that gender-neutral approaches to housing will fail to meet women’s needs.

This study explores the narratives from women accessing support services in Halifax, Canada. The first author conducted deep narrative interviews with women seeking to move from transition to market housing.

This research sheds light on the effects of gendered barriers to safe, suitable and affordable housing; how women’s experiences and expectations are shaped by these barriers; and, how housing-based supports must address the uniquely gendered experiences women face as they access market housing. In addition, this research reveals the importance of gender-responsive services that empower women facing a sexist housing market.

Little research has explored questions related to gender and housing among those seeking to move from transitional to marker housing, and existing research focuses on women’s housing insecurity as it relates to domestic violence. The sample of women included those having housing insecurity for a variety of reasons, including substance use and young motherhood.

By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.

The methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection.

The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score.

To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.