The rise of globalization and industrialization has driven the demand for rare earth metals (REMs). These metals are widely used in various sectors of the global economy with various applications in medicine, renewable energy, electronics, agriculture, and the military. REMs are likely to remain an important part of our global future, and, as production increases, areas contaminated by REMs are expected to expand over the coming decades. Thus, triggering significant adverse environmental, animal, and human health impacts. Despite increased attention on REMs outside China in recent years, there are limited studies exploring REM production, deposits, and associated health impacts in the African context. Proper mine management, adequate safety protocols, sustainable processing methods, and waste handling systems have been identified and proposed globally; however, the nature and scale of implementing these management protocols on the African continent have been less clear. Therefore, planetary health-centered solutions are urgently needed to be undertaken by researchers, policy makers, and non-governmental actors in Africa and across the globe. This is with the overarching aim of ensuring eco-friendly alternatives and public health consciousness on REM exploitations and hazards for future generations to come.
Category: 3/ Type
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Google news initiative’s influence on technological media innovation in Africa and the Middle East
The Google News Initiative (GNI) aims to collaborate closely with the news industry and financially support the creation of quality journalism in the digital age. It also aims to bring technological advancements and innovation into newsrooms’ operations. Drawing on journalism innovation and responsible innovation theories, this study examines GNI beneficiaries in Africa and the Middle East. To address this, we analysed GNI projects’ descriptions combined with thirteen (n= 13) in-depth interviews with leading actors and beneficiary news organisations to answer two main questions:(a) What are the main characteristics of the technological innovations proposed by GNI Innovation Challenge grantees in Africa and the Middle East? and (b) How are these news media organisations becoming increasingly dependent on these platforms’ technological and financial aspects? Anchored in journalism innovation, responsible innovation, and platformisation theories, our findings show that funded organisations heavily depend on Google’s technological and financial infrastructure to innovate. Furthermore, we note that some projects do not offer a clear path for sustainability in the future. We further argue that this initiative builds an infrastructure of power and dependency that poses risks to responsible innovation in journalism. Our study contributes to extant scholarship on digital platforms and their role in the infrastructure of news organisations, creating power asymmetries between those who serve as the backbone for data flows and technological processes and those dependent on these institutions.
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Diving into data: Pitfalls and promises of data journalism in semi-authoritarian contexts
This paper calls for greater scrutiny of data journalism as a practice in the semi authoritarian context of Zimbabwe. Based on in-depth interviews with practising journalists in Zimbabwe, this paper answers two main questions: In what ways is data journalism practised in the Zimbabwean context? To what extent are newsrooms in Zimbabwe “tooled” and capacitated for data journalism practices? We note that data journalism is widely understood by individual journalists in this country but paradoxically less practised due to many challenges. By answering these two questions, we sustain an argument that data journalism appropriation in semi-authoritarian contexts can be instrumental in promoting monitorial democracy and reversing media decadence.
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Leveraging responsible, explainable, and local artificial intelligence solutions for clinical public health in the Global South
In the present paper, we will explore how artificial intelligence (AI) and big data analytics (BDA) can help address clinical public and global health needs in the Global South, leveraging and capitalizing on our experience with the “Africa-Canada Artificial Intelligence and Data Innovation Consortium” (ACADIC) Project in the Global South, and focusing on the ethical and regulatory challenges we had to face. “Clinical public health” can be defined as an interdisciplinary field, at the intersection of clinical medicine and public health, whilst “clinical global health” is the practice of clinical public health with a special focus on health issue management in resource-limited settings and contexts, including the Global South. As such, clinical public and global health represent vital approaches, instrumental in (i) applying a community/population perspective to clinical practice as well as a clinical lens to community/population health, (ii) identifying health needs both at the individual and community/population levels, (iii) systematically addressing the determinants of health, including the social and structural ones, (iv) reaching the goals of population’s health and well-being, especially of socially vulnerable, underserved communities, (v) better coordinating and integrating the delivery of healthcare provisions, (vi) strengthening health promotion, health protection, and health equity, and (vii) closing gender inequality and other (ethnic and socio-economic) disparities and gaps. Clinical public and global health are called to respond to the more pressing healthcare needs and challenges of our contemporary society, for which AI and BDA can help unlock new options and perspectives. In the aftermath of the still ongoing COVID-19 pandemic, the future trend of AI and BDA in the healthcare field will be devoted to building a more healthy, resilient society, able to face several challenges arising from globally networked hyper-risks, including ageing, multimorbidity, chronic disease accumulation, and climate change.
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Artificial intelligence, law, and vulnerabilities
Social vulnerability is a measurement of the ability of communities to adequately respond to external stresses (Blaikie et al., 1994), such as the ongoing “SARS-CoV-2” – Severe Acute Respiratory Syndrome Coronavirus 2 (Bankoff & Hilhorst, 2004). During these periods of upheaval, people with disabilities, racial, ethnic, and religious minorities, children from low-income families, the elderly, migrants and refugees, the immunocompromised and those with chronic health conditions, and the homeless among others are considered to be at greater risk from the adverse effects, and potential losses incurred by these external stressors. They are also the slowest to recover from such emergencies. For example, recent data from the COVID-19 pandemic shows that vulnerable populations were much more likely to contract the virus, were less likely to receive the vaccine because of hesitancy and distrust of “Big Pharma”, yet they were more in need of social assistance compared to other segments of society (Cheong et al., 2021; Kazemi et al., 2022; St‐Denis, 2020). Classified as “socially vulnerable” by the United Nations (n.d.), these populations are almost always economically marginalized, politically under-represented, and socially underserved. (Un)surprisingly, they are predominantly racialized (Black and other people of color), and have a long history of enduring violations of their civil rights and freedoms, even during disaster response and recovery efforts. The factors and/or characteristics that determine the social vulnerability of a group differ from country to country, however, there are some universal similarities. Risk factors that contribute to the vulnerability of these groups include poverty, unemployment, and lack of access to resources (e.g., adequate healthcare, education, housing, safe drinking water, transportation, and other social services) (Cutter et al., 2003). Socially vulnerable populations are also stigmatized and discriminated against by the wider society, and even criminalized in law, policy, and practice. Forced to live in environments of severe inequality, they are unable to thrive, feel safe, and actively participate in all aspects of society (UNDP, n.d.). When compared to the general population, the capacity of socially vulnerable groups to cope with, respond to, and recover from the adverse impacts of crises is hindered by the inordinate obstacles they encounter in their daily lives (Wisner et al., 2004). These obstacles are indicators of structural inequities and barriers that hamper fair and equitable access (for all) to the resources needed to satisfy one’s basic needs. Social vulnerability is then a combination of the risk factors and socio-cultural markers listed above, which hinder full participation in economic, social, political, and cultural life (UN DESA, 2016). The amplification of existing inequities during crises like the COVID-19 pandemic has re-ignited discussions about global inequities and the challenges they present to socially vulnerable populations.
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From the classroom to the newsroom: A critical route to introduce AI in journalism education
From a computer vision application to monitor elections transparency in Argentina to automated real estate texts in Norway, and everything in between, Artificial Intelligence powered tools are changing journalism. Scholars have taken note, and the academic production of AI in journalism has gained considerable ground in the last five years. However, research on how journalism education deals with AI influence in the industry is scarce. Based on a self-training method using available online free courses for journalists and a review of university teaching initiatives, this article proposes key elements to trace teaching trajectories to introduce AI into journalism curriculum. Included are recommendations for drawing a path to teaching journalism students to think critically about AI and, at the same time, to understand the available tools for reporting and investigating in a complex context where journalism lives in a profound state of crisis.
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The militarization of digital surveillance in post-coup Zimbabwe:‘Just don’t tell them what we do’
While a large body of research has documented and theorized digital surveillance practices in various political contexts, little has been done to investigate the growing trend of military-driven digital surveillance practices in semi-authoritarian regimes. In this article, I use the case of the surveillance practices of the Zimbabwe Defence Forces to argue that scholarship needs to (re)evaluate this emerging trend. The article has three aims: first, it explores military-driven surveillance capabilities, the circulation of such capabilities and the surveillance tactics emerging in the semi-authoritarian context of Zimbabwe. Second, it examines the interface of factionalism and politics within the Zimbabwe Defence Forces and how this influences quotidian military-driven digital surveillance practices.
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Powers, Interests and Actors 1: The Influence of China in Africa’s Digital Surveillance Practices
This chapter examines the influence of China in the growth of Africa’s digital surveillance 1 capabilities and practices. The growth of Chinese investments through its Belt and Road Initiative (BRI) in the global south is well documented. Yet, China’s digital infrastructure investments in Africa still present a research lacuna that needs to be filled. Equally under-researched is how it influences African governments’ clampdown on citizens. Utilising the international political economy approach, we analyse the intersection of power, actors and interests in Africa’s surveillance practices focusing on Zambia and Zimbabwe. We focus on Zimbabwe and Zambia because their ties with China are among the most enduring of all African countries.
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Hate speech and polarization in participatory society, 67-82
Since the advent of Twitter in 2006, South Africa has witnessed a spectacular explosion of racist discourse on social media platforms. According to the South Africa Human Rights Commission (SAHRC, 2016), the centres of explosive racist discourses are Twitter and Facebook. SAHRC (2016) further notes that recent racist outbursts on Twitter and Facebook threaten the creation of a non-racial society and general social cohesion in South Africa. The surge of racist rants in South Africa reflects a society that is increasingly becoming racially polarised (South African Institute of Race Relations, 2018). The SAHRC trend analysis report (2016) reveals that discrimination on the grounds of race remains the highest of equality complaints, with an annual increase in high-profile derogatory incidences frequently aimed at black Africans. Similarly, Daniels (2009), Rauch and Schantz (2013) and Shepherd et al.(2015) note that social media have become a major conveyer of hate crimes against racial minorities and a major reason for the failure of multiculturalism as an institutional practice at a global level. Social media discussions on the subject of race in South Africa have served to highlight how racism remains pervasive and toxic (SAHRC, 2016). Some studies, such as those by Mafoko (2017) and Stephens (2018), have argued that the ‘rainbow nation’that the first democratically elected president of the country, Nelson Mandela, envisaged is now history–not a lived reality.
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In the Dead End: The Decline of the Indigenous Language Press in Post-colonial Zimbabwe
The Zimbabwe Media commission (ZMC), the statutory body that regulates the press in Zimbabwe, has on several occasions urged media proprietors to prioritise newspapers, or even magazines, in Zimbabwe’s varied indigenous languages. At independence in 1980, the government of Zimbabwe, through its newly created media entity, New Ziana, made strenuous efforts to promote the indigenous language press. Yet, only a handful of these newspapers remain. The few that do are tottering under severe challenges, and they do not show signs of sustainability going forward. Though the government claimed it would intervene to serve the indigenous language press, they have not helped save them. The continued decline of the press – as evidenced by closures, downsizing, staff turnover and many other symptoms – is testimony to the fact that these efforts have been hugely unsuccessful.
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Data journalism in the age of big data: An exploration into the uptake of data journalism in leading south African Newspapers
The growing capacity to generate large quantities of data as a result of rapid technological change in the recent past has meant that increasingly, journalists everywhere are under pressure to find new ways of handling this information deluge, processing and packaging it in ways that allow ease of access for their readers. These data blizzards have been further intensified by the ever-expanding social media networks that keep churning out large volumes of information at unprecedented speed, making gathering, processing and packaging information in visualisable form all the more important. The rise of data journalism is in part a direct response to this, as it provides journalists the critical tools to manipulate data on complex issues such as national budgets, election manifestos and national census for innovative storytelling. This chapter explores the emergence of data journalism in South Africa and analyses its …
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Deep Learning based Automatic Image Caption Generation
The paper aims at generating automated captions by learning the contents of the image. At present images are annotated with human intervention and it becomes nearly impossible task for huge commercial databases. The image database is given as input to a deep neural network (Convolutional Neural Network (CNN)) encoder for generating “thought vector” which extracts the features and nuances out of our image and RNN (Recurrent Neural Network) decoder is used to translate the features and objects given by our image to obtain sequential, meaningful description of the image. In this paper, we systematically analyze different deep neural network-based image caption generation approaches and pretrained models to conclude on the most efficient model with fine-tuning. The analyzed models contain both with and without `attention’ concept to optimize the caption generating ability of the model.