Global South researchers succeeding against the odds: how are they different?

Understanding the Context

How are some global South researchers able to overcome contextual constraints and become highly cited?

There is a clear research divide between the global South and the global North[1] in terms of research investment and capabilities. The average national expenditure on research and development in Southern countries is 0.38% compared to 1.44% in Northern countries[2]. The number of researchers per million population in 2017 was 713 in the global South and 4,351 in the global North[3]. This had implications on the volume and impact of scientific outputs produced by the global South in comparison to the global North. Excluding China and India, in 2018 global North countries produced an average of more than 35,000 scientific and technical journal articles per country while global South countries produced 4,000 journal articles per country, out of which less than 2% made it to the top 1% most cited articles globally. This can be partially explained by the lower levels of investment and English proficiency, smaller relative populations of researchers, institutional exclusion factors and/or biases against Southern researchers when it comes to accepting their papers in top tier journals or awarding grants.

Despite all of the aforementioned challenges, there are a few Southern researchers who are able to achieve better outcomes than their peers. Such researchers could provide valuable insights and lessons that might help to better understand and even mitigate the current North–South divide in research outputs and citation. This blog post will highlight some of the valuable insights emerging from our recently published study that attempted to uncover publication-level and individual-level factors underlying the outperformance of information systems researchers in Egypt.

The Method

 This study employed the “data-powered positive deviance” (DPPD) methodology that uses digital datasets to identify positive deviants (those performing unexpectedly well in a specific outcome measure that is digitally recorded, mediated or observed) and potentially also to understand the characteristics and practices of those positive deviants (PDs) if digitally recorded.

Three main steps were conducted to identify and characterise PDs, as shown in Figure 1:

  • In the Define step, we defined our study population and the performance indicators that will be used to assign a score for each researcher. The study population comprised 203 information system researchers in Egyptian public universities. Six well-known citation metrics (h-index, g-index, hc-index, hi-index, aw-index and m-quotient) were calculated for each researcher using Publish or Perish and Google Scholar bibliometrics. Several citation metrics were used to avoid putting certain groups at a disadvantage due to factors such as the length of their research career, the size of their research departments, the age of their papers or their publication strategies.
  • The Determine step aims at identifying the PDs based on the scores calculated in the previous step. In this study, PDs or outliers were defined as researchers who significantly outperformed their peers in at least one of the six citation metrics. The interquartile (IQR) method was used to identify those outliers based on their deviation from the median, i.e. lying beyond the 1.5*IQR added to the third quartile in at least one of the six citation metrics.
  • The third step, Discover, consists of three main stages. In Stage 1, primary data was collected through in-depth interviews from a sample of PDs to explore practices, attitudes and attributes that might distinguish them from non-PDs. During Stage 2, the key findings from Stage 1 plus other predictors of research performance drawn from the literature were used to design a survey tool. That survey then targeted the whole population and tested if the proposed differentiators were significantly different between the two groups. Finally, in Stage 3, the Scopus database was used as the basis for analysis of researcher publications; extending and validating some of the findings identified in the previous stages.

Figure 1: Summary of the applied DPPD method

 What we found

 A combination of data sources (interviews, surveys, publications) and analytical techniques (PLS regression, topic modelling) were used to identify significant predictors of positively-deviant information system researchers. One of the key findings was that PDs contributed to the creation of roughly half (48%) of the publications and achieved nearly double (1.7x) the total number of citations of non-PDs despite representing roughly one-eighth (13%) of the study population. While there were significant predictors of outperformance that are structural (e.g. gender, academic rank and role, workplace perceptions), our focus in this post is on highlighting factors that are transferable i.e. practices and strategies that are to some extent within the control of the individual researchers. Table 1 provides a summary of such factors.

Individual-Level Predictors

 

Positive Deviants

Non-Positive Deviants

Travelling abroad to obtain their PhD degree

More PDs got their PhDs from global North countries 

Fewer non-PDs got their PhDs from global North countries

International research collaborations

Frequently part of multi-country research teams 

Seldom part of multi-country research teams

Co-authorship

Published more papers with foreign reputable authors

Published fewer papers with foreign reputable authors

Securing research grants and travel funds 

Secured more grants and travel funds

Secured fewer grants and travel funds

Research approach

Less inclined to do radical research

More inclined to do radical research

Student supervisions

Supervised a larger number of postgraduate students

Supervised a smaller number of postgraduate students

Capacity development  

More PDs took scientific writing and English writing courses

Fewer non-PDs took scientific writing and English writing courses

Publication-Level Predictors

Length of paper

Longer papers

Shorter papers

Length of abstract

Longer abstracts

Shorter abstracts

Length of title

Longer titles

Shorter titles

Number of authors and affiliations

More authors and affiliations

Fewer authors and affiliations

Number of references

More references

Fewer references 

Publication type

More journal articles and fewer conference papers

More conference papers and fewer journal articles

Quality of journals

Higher SJR journals

Lower SJR journals

Publishers

Published more in Elsevier Journals

Published less in Elsevier Journals

Topics

PDs publish fewer papers covering business process management and neural networks and published more papers in wireless sensor networks and hardware systems

Non-PDs publish more papers covering business process management and neural networks and published fewer papers in wireless sensor networks and hardware systems

 Table 1: Significant transferable predictors of outperformance

The analysis also included a visualization of topic prevalence over time for the PD corpus and non-PD corpus as presented in Figure 2. It shows topics, such as Classification Models, where PDs were early movers and then they were followed by NPDs. There is a greater prevalence of Expert Systems and GIS-related topics in the PD corpus in comparison to the NPD corpus. Conversely, there is lower prevalence of Neural Networks and Business Process Management & Process Mining. There are also topics that had very similar proportions over time for both groups, such as Social Network Mining.

Figure 2: Topic proportions of PD corpus (left) and non-PD corpus (right) over time

 Implications for practice and policy

This analysis cannot, of course, guarantee that applying these factors more broadly would lead to the same outcomes achieved by PDs. Nonetheless, there would be value in individual Southern researchers reflecting on the research- and paper-related behaviours that have been shown associated with positively-deviant research profiles. For instance, Southern researchers work in contexts of resource limitation, hence, research grants and travel funds are of outmost importance. Including partners from Northern universities (as PDs do) increases the chances of securing the funds as those partners are more familiar with grant procurement processes and more experienced in writing proposals. Studying abroad also seems to put Southern researchers at an advantage as it does not just equip them with the technical know-how and the degree needed to pursue their academic careers, but also helps them establish channels of collaboration with their supervisors and their PhD granting universities, long after they returned to their home countries. Those long standing relationships provide further access to research grants either directly or via joint grant applications.

In terms of paper-related strategies, Southern researchers could avoid low-visibility local conferences and can select journals instead as they are more likely to deliver citations. Publishing with more authors (domestic and international) could also help pay for journal publication fees, with fees split across more authors or paid from overseas sources. Publishing with foreign authors could also help Southern researchers overcome the institutional biases[4] among editors, reviewers in single-blind or open review systems, and readers. PDs’ preference for working on established research areas rather than on radical research topics may also help in relation to institutional barriers, with research that builds incrementally on existing ideas and literature being more likely to be accepted for publication by referees, and cited by others working in the established area. Hence, Southern researchers seeking more citations could consider contributing to mainstream topics that build on existing work. Along the same lines, having multiple authors and affiliations increases the likelihood of citations, as each author has their own network and bringing those networks together can increase readership. Similarly, publishing papers with a larger number of references increases paper visibility through citation-based search in databases that allow it, such as Google Scholar, and through the “tit-for-tat” hypothesis i.e. authors tend to cite those who cite them.[5]

Higher education institutions and higher education policy makers may also reflect on the findings, and consider strategic implications for training, resource provision, collaborations, etc. For example, English and scientific/formal writing courses were associated with PD performance; such courses could be prerequisites for starting a PhD research. There could be more academic training designed around research grant writing and providing guidance on funding bodies that researchers can apply to. International research collaborations appeared as an important predictor of PDs; so, university senior managers and policy makers can explore ways to reduce barriers and increase opportunities for overseas PhD study, post-PhD return, and ongoing joint research projects with global North universities.

Citation rates are, of course, not the “be all and end all” of research: there are and should be other motivations and indicators of research. However, we hope the findings presented here can provide valuable “food for thought” for global South researchers.

 ________ 

[1] The terms “South” and “Southern” will be used to refer to countries classified as upper-middle income, lower-middle income, and low income. Accordingly, the terms “North” and “Northern” will be used to refer to countries that are members of the OECD (Organisation for Economic Co-operation and Development) or are classified as high-income economies by the World Bank based on estimates of gross national income per capita.

[2] Blicharska, M., Smithers, R. J., Kuchler, M., Agrawal, G. K., Gutiérrez, J. M., Hassanali, A., Huq, S., Koller, S. H., Marjit, S., Mshinda, H. M., & Masjuki, H. (2017). Steps to overcome the North-South divide in research relevant to climate change policy and practice. Nature Climate Change, 7(1), 21–27.

[3] World Bank. (2020). Science & Technology Indicators. World Bank.

[4] Karlsson, S., Srebotnjak, T., & Gonzales, P. (2007). Understanding the North-South knowledge divide and its implications for policy: A quantitative analysis of the generation of scientific knowledge in the environmental sciences. Environmental Science and Policy, 10(7–8), 668–684.; Gibbs, W. W. (1995). Lost science in the third world. Scientific American, 273(2), 92–99.; Leimu, R., & Koricheva, J. (2005). What determines the citation frequency of ecological papers? Trends in Ecology & Evolution, 20(1), 28–32.

[5] Webster, G. D., Jonason, P. K., & Schember, T. O. (2009). Hot topics and popular papers in evolutionary psychology: Analyses of title words and citation counts in evolution and human behavior, 1979–2008. Evolutionary Psychology, 7(3), 147470490900700300.

 

Sharing Data between Humanitarian Actors and Donor Governments

Across the humanitarian sector, data play an increasingly important role in response efforts. To implement water, shelter, protection, or food assistance programmes, humanitarian actors collect information, such as the gender or age of individual recipients of assistance. As a result, humanitarians need to manage large amounts of data, and recognise the importance of ensuring responsible use and protection of these data (see here and here).

In its recent guidance, the Inter-Agency Standing Committee (IASC) defines data responsibility in humanitarian operations as ‘the safe, ethical and effective management of personal and non-personal data for operational response, in accordance with established frameworks for personal data protection’ (IASC 2021, 7). Managing data responsibly requires comprehensive consideration, encompassing data collection, processing, analysis, use, storage, sharing, retention, and destruction. In a recent report, I investigate one component of data responsibility: data sharing between humanitarian actors and donor governments. The research draws on interviews with donors and humanitarians about data sharing practices and an examination of formal documents. While the report goes into more detail, here I focus on two issues – data and definitions, and expectations and standards – and why they matter for more effective humanitarian response.

Data and definitions

My research finds that references to ‘data’ in the context of humanitarian operations are usually generic and lack a consistent definition or even a shared terminology. Thus, among other definitions, data could refer to quantitative or qualitative, numbers or narratives, personal or non-personal data as well as financial, audit or compliance data, situational reporting, and aggregated or disaggregated indicator data (see table below).

Type of DataExample
Quantitative/numbersNumbers of beneficiaries/aid recipients
Qualitative/narrativeDescriptions of workshops or programme activities
PersonalDemographic data (names or contact information of aid recipients, group information, such as ethnicity)
Non-personalData about groups affected by the humanitarian situation, including needs or the threats they face
GroupData about groups of aid recipients (women, children, disabled), such as location
IndividualAge, sex or gender data about individual aid recipients
FinancialBudget reports
Audit/complianceReporting against legal or regulatory requirements, such as for safeguarding or counter-terrorism or sanctions
SituationalAnalysis of security situation
OrganisationalContact information for project officer
Disaggregated indicatorLocation-specific data for those assisted in a project
Aggregated indicatorTotal number of people assisted in a project
Types of Data

Varying expectations and standards

My research also found that donors have varying expectations of their partners – both among and within donor governments (e.g., at the country or headquarter levels) – and humanitarian actors have differing experiences of data sharing with donors. Expectations are informed by factors such as the complex regulatory frameworks for data (eg. host or donor government law, particularly in the context of privileges and immunities), the type of agreement (eg. grants or contracts), and funding allocations (eg. project-specific vs non-earmarked funding).

Donors varied in terms of the level of detail and the type of markers or indicators they requested. For example, United Nations agencies and Red Cross movement actors often have overarching agreements with donors that cover a range of activities in a country rather than project specific funding, as is often the case for non-government organisations (NGOs). The formal reporting for these overarching agreements is less specific, often requiring less formal sharing of disaggregated programme-related data even if it does not preclude or prevent informal requests for such data.

Both donors and humanitarians agreed that informal requests also occur, more often for context-specific information or aggregated data, and in some cases, for sensitive or personal data. The most common type that interviewees named was requests for data related to monitoring programme delivery, such as disaggregated or aggregated indicators. Even so, donors and humanitarians confirmed that standards varied for partners, indicating that in general NGOs were required to provide the most detailed information. By contrast, donors more often accepted annual reporting statements for the UN and Red Cross, often because of the nature of the funding allocations or agreements.

Why does this matter for more effective humanitarian response?

First, not clearly defining what ‘data’ means makes it possible to have inconsistencies in the logic of handling data, to request data that should not be shared, and to compromise the principle of ‘do no harm.’ To mitigate against this, both donors and humanitarian actors must clearly define the type(s) of data that will be shared in the course of a partnership or contractual relationship. Without clarity on the type of data under discussion, it will be difficult to increase data literacy in the humanitarian sector, or to advance conversations and practice to more responsibly manage and protect data.

Second, an indirect yet mutually-reinforcing relationship exists between requests to share data and the need to collect data. Although my research focused on data sharing as opposed to data collection, the interviews and documentation point to an indirect relationship between the two: data are collected in part because they are meant to be shared. Meaning, humanitarians collect data partly because donors ask them to share data. Requests for data sharing, in turn, are driven by differing needs, which leads to collecting more data than strictly needed, with potentially higher risks to those whose data are collected.

Finally, while humanitarians have the ability to push back against donor requests to share data, this ability is greatly influenced by power dynamics and trust. This in itself is a further dilemma, given that this level of trust is more likely to exist between donors and established humanitarian actors, creating another, largely invisible barrier for newer, less established, usually national or local humanitarian actors – a barrier that undermines efforts to ‘localise’ humanitarian response

Building the practice of responsible data sharing therefore requires a sector-wide effort to increase data literacy across humanitarian actors and donors, and ultimately to protect those who should be at the centre of humanitarian response – those affected by conflict, violence, or disaster.

Latest Digital Development Outputs (Data, Labour, Platforms, Society, Ed Tech, MSc) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Labour; Digital Platforms; Digital Society; Ed Tech; MSc Programme – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Data Powered Positive Deviance: Combining Traditional and Non-Traditional Data to Identify and Characterise Development-Related Outperformers” (open access) by Basma Albanna, Richard Heeks, Julia Handl and colleagues from the DPPD project, presents a new methodology through which datasets can be used to identify “positive deviants” – those who outperform their peers in development – and to identify and scale the factors behind their outperformance.

Publication Outperformance among Global South Researchers: An Analysis of Individual-Level and Publication-Level Predictors of Positive Deviance” (open access) by Basma Albanna, Julia Handl & Richard Heeks, uses interviews, a survey and analysis of online datasets to identify those among a group of global South researchers who outperform their peers.  It identifies characteristics of both the high-performing researchers and their publications.

DIGITAL LABOUR

Systematic Evaluation of Gig Work Against Decent Work Standards: The Development and Application of the Fairwork Framework” (open access) by Richard Heeks, Mark Graham, Paul Mungai, Jean-Paul Van Belle & Jamie Woodcock, explains the development and application of the Fairwork framework, which is used worldwide to rate gig economy platforms against decent work standards.

Stripping Back the Mask: Working Conditions on Digital Labour Platforms during the COVID-19 Pandemic” (open access) by Kelle Howson, Funda Ustek-Spilda, Alessio Bertolini, Richard Heeks and other colleagues from the Fairwork project, analyses the Covid policies of 191 platforms in 43 countries. It finds some positive worker protections but also entrenchment of precarious work as platforms leverage the opportunities arising from the crisis.

DIGITAL PLATFORMS

Digital Platforms for Development” (open access) by Brian Nicholson, Petter Nielsen & Johan Saebo, provides an editorial introduction to a special issue of Information Systems Journal on the link between digital platforms and development processes.

Driving the Digital Value Network: Economic Geographies of Global Platform Capitalism” (open access) by Kelle Howson, Fabian Ferrari, Funda Ustek-Spilda, Richard Heeks and other colleagues from the Fairwork project, uses insights from global value chain and global production network frameworks to analyse power imbalances and value extraction across territories by gig economy platforms.

DIGITAL SOCIETY

“Toolkit for Measuring Digital Skills and Digital Literacy“ (open access) by authors at CSIS Indonesia, supported by Matthew Sharp, offers a comprehensive and original framework for measuring digital skills in Indonesia and other G20 countries. The toolkit incorporates insights from pilot individual and firm-level surveys on digital skills undertaken by CSIS in the Greater Jakarta area.

How can Smart City Shape a Happier Life? The Mechanism for Developing a Happiness Driven Smart City” by Huiying Zhu, Liyin Shen & Yitian Ren, introduces a Happiness Driven Smart City (HDSC) mechanism, composed of a three-layer structure and underpinned by a set of strategic measures. A case study shows the HDSC mechanism’s effectiveness in helping decision makers understand the status quo, strengths and weaknesses of smart city development in their context, so that their SC blueprint can be better aligned towards a happiness-driven direction.

ED TECH

The Effectiveness of Technology‐Supported Personalised Learning in Low‐and Middle‐Income Countries” (open access) by Louis Major, Gill Francis & Maria Tsapali, provides a meta-analysis examining the impact of students’ use of technology that personalises and adapts to learning level.

Evaluating Digital Personalised Learning Tools in Kenya: A New Research Study” (blog) by Becky Daltry, Louis Major and others, reports on a new research study to rigorously evaluate the integration of digital personalised learninginto Kenyan classrooms for young children, aged between 4-8 years old.

MSc PROGRAMME

Centre for Digital Development staff provide the core directorship and teaching for the University’s new MSc programme in Digital Development, which will launch in Sept 2022.

The risks of knowing from a distance: remote sensing, social-ecological relationships and ecosystem restoration

Rose Pritchard, Charis Enns, Tim Foster and Laura A. Sauls

Remote sensing data are valuable inputs to decision-making in ecosystem restoration. But as it becomes easier to collect detailed data from afar, is this lengthening other distances too – between restoration advocates and the lands they seek to restore, and between local people and centres of decision-making power?

A savanna restoration project in KwaZulu-Natal, South Africa. Photo: Rose Pritchard

Ecosystem restoration is having a moment in the political sun. The United Nations have designated 2021 to 2030 the Decade on Ecosystem Restoration. And restoration emerged as a priority in the recent COP26, with world leaders pledging to ‘Conserve forests and other terrestrial ecosystems and accelerate their restoration’ as part of the Glasgow Leaders’ Declaration on Forests and Land Use.

Remote sensing data from satellite or near-earth sensors can contribute to many aspects of ecosystem restoration. These technologies make it possible to collect information on land cover change and other key parameters at scales that would be impossible using ground-based survey methods alone. These data may be used to help prioritise restoration locations, plan restoration activities, and monitor the impacts of restoration efforts.

But data and the information products derived from earth observation are political objects. Data in and of themselves are neither good nor bad. The nature and use of data are shaped by people, each of whom will have different values and interests. Thus, remote sensing data can serve emancipatory, empowering purposes in landscapes undergoing restoration; for example, when local people use these data to advocate for their interests. Or the same kind of data could be used to reinforce asymmetrical power relationships and drive exclusionary, inequitable restoration approaches. This latter concern is particularly intense in contexts where restoration activities have already caused harm to marginalised people, such as by restricting access of poorer households to land or leading to displacement of communities.

In our seedcorn project, funded by the Centre for Digital Trust and Society at the University of Manchester, we aim to lay the groundwork needed to address three critical questions on the use and impacts of remote sensing data in restoration, focusing specifically on satellite remote sensing data. First, how are satellite remote sensing data being used by restoration practitioners? Research papers on applications of remote sensing data are multiplying rapidly, from global-scale analyses down to landscape-level studies. But how are these data actually shaping restoration interventions? And how, if at all, is knowledge derived from remote sensing data being combined with other forms of knowledge (such as local ecological knowledge) in the day-to-day practices of ecosystem restoration?

Second, how does use of remote sensing data alter the relationships between restoration practitioners and the lands they seek to restore? That distant imagery can change the way we think about the world is demonstrated by the example of the Earthrise photo, which made clear the fragility of our place in the galaxy and led to a step-change in western environmentalism. However, some things, such as local environmental values, can never be represented through remote sensing imagery. Additionally, satellite remote sensing data are imperfect, and even the highest resolution data still contain uncertainties that can lead to misinterpretation of landscapes. We seek to understand the consequences of the fact that some restoration practitioners and researchers are relating to their target landscapes increasingly – or in some cases exclusively – from a distance.

And third, what does use of remote sensing data mean for relationships of trust and power, both within and beyond landscapes undergoing restoration? A fear raised for conservation more generally is that digital data sources and ‘conservation by algorithm’ will exclude local people from decision-making processes. There are also concerns around privacy, as more aspects of peoples’ livelihoods are being observed without their knowledge or consent. But so far, few studies have drawn out these social and procedural implications of satellite remote sensing data use, in general but particularly in ecosystem restoration.

The potential of remote sensing data to help address the challenge of environmental degradation is undeniable, as demonstrated by the proliferation of increasingly detailed and sophisticated remote sensing applications and tools for environmental purposes over the last two decades. However, the use of such data is not without risks or challenges. Questioning the consequences of how satellite remote sensing data are used, by whom, and with what impacts is an important step towards ensuring that these data support just and lasting approaches to ecosystem restoration – not approaches associated with exclusion and harm.

If you are interested in this project and would like to be part of these conversations, please contact rose.pritchard@manchester.ac.uk

Latest Digital Development Outputs (Data, Economy, Health, Platforms, Water) from CDD, Manchester

Using SmartphoneRecent outputs – on Data-for-Development; Digital Economy; Digital Health; Digital Platforms; Digital Water – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

Strengthening the Skills Pipeline for Statistical Capacity Development to Meet the Demands of Sustainable Development: Implementing a Data Fellowship Model in Colombia” (open access) by Pete Jones, Jackie Carter, Jaco Renken & Magdalena Arbeláez Tobón, considers the importance of quantitative data skills development implied by the UN Sustainable Development Goals. The success of a partnership programme in the UK is used to explore how ‘data fellowships’ can fulfil some of the unmet capacity needs of the SDGs in a developing country context, Colombia.

Building Information Modelling Diffusion Research in Developing Countries” (open access) by Samuel Adeniyi Adekunle, Obuks Ejohwomu & Clinton Ohis Aigbavboa undertakes a literature review – including current and future research trends – on the adoption of building information modelling in developing countries.

DIGITAL ECONOMY / PLATFORMS

Conceptualising Digital Platforms in Developing Countries as Socio-Technical Transitions” (open read access) by Juan Erasmo Gomez-Morantes, Richard Heeks & Richard Duncombe demonstrates how the multi-level perspective approach can be used to analyse the lifecycle of digital platforms: the process of innovation, rapidity of scaling, and development impacts relating to resource endowments, institutional formalisation, and shifts in power.

Digital Platforms and Institutional Voids in Developing Countries” (open access) by Richard Heeks, Juan Erasmo Gomez-Morantes, Brian Nicholson and colleagues from the Fairwork project, analyses how digital platforms change markets through their institutional actions.  Using the example of ride-hailing, it finds platforms have formed a market that is more efficient, effective, complete and formalised.  At the same time, though, they have institutionalised problematic behaviours and significant inequalities.

Navigating a New Digital Era Means Changing the World Economic Order” (open access) by Shamel Azmeh, discusses the implications of digital shifts for global economic governance.

DIGITAL HEALTH

Cost-Effectiveness of a Mobile Technology-Enabled Primary Care Intervention for Cardiovascular Disease Risk Management in Rural Indonesia” by Gindo Tampubolon and colleagues demonstrates how to determine the economic impact of m-health.  It calculates the cost-effectiveness of a mobile-based health intervention at c.US$4,300 per disability-adjusted life year averted and US$3,700 per cardiovascular disease event avoided.

Delivering Eye Health Education to Deprived Communities in India through a Social Media-Based Innovation” by Chandrani Maitra & Jenny Rowley aims to develop understanding of the benefits of, and the challenges associated with the use of social media to disseminate eye health information in deprived communities in India.

Using a Social Media Based Intervention to Enhance Eye Health Awareness of Members of a Deprived Community in India” (open access) by Chandrani Maitra & Jennifer Rowley reports on a WhatsApp-based intervention to promote eye health communication in deprived settings. This research highlights the potential benefits of WhatsApp in increasing awareness on eye problems, amongst deprived communities where the disease burden remains very high.

DIGITAL WATER

Digital Innovations and Water Services in Cities of the Global South: A Systematic Literature Review” (open access) by Godfred Amankwaa, Richard Heeks & Alison Browne reviews the literature on digital and water in Southern cities.  It summarises findings to date on implementation and impact and sets out the future research agenda.

Latest Digital Development Outputs (Data, Humanitarianism, Labour, Platforms) from CDD, Manchester

Recent outputs – on Data-for-Development; Digital Humanitarianism; Digital Labour; Digital Platforms – from Centre for Digital Development researchers, University of Manchester:

DATA-FOR-DEVELOPMENT

The Rise of the Data Economy and Policy Strategies for Digital Development” (open access) by Shamel Azmeh, Christopher Foster & Ahmad Abd Rabuh, expands on policy debates around digital development.  It examines the emergence of the data economy and potentials of strategic policy and/or industrial policy in the global South.  Based on a global policy analysis, it identifies four key “policy pathways” by which countries can look to strategically capture value in the data economy.

DIGITAL HUMANITARIANISM

Digital Innovation by Displaced Populations: A Critical Realist Study of Rohingya Refugees in Bangladesh” by Faheem Hussain, P.J. Wall & Richard Heeks, uses a critical realist approach to understand the three mechanisms the underpin digital innovation by Rohingya refugees.

Lessons On The Digital World From The Charity Sector: The Corporate World Has A Lot To Learn” (open access) by Brian Nicholson, Lisa Kidston, Cris Sachikoyne & Dane Anderton, argues that African charitable organisations and those like the national Citizens Advice in England and Wales are leading the way when it comes to demonstrating exemplary digital leadership.

DIGITAL LABOUR AND DEVELOPMENT

Competing Institutional Logics in Impact Sourcing” by Fareesa Malik & Brian Nicholson, draws on the concepts of institutional logics to  present a case study of a USA-based IT outsourcing vendor “AlphaCorp” practising impact sourcing in a Pakistan subsidiary. The findings show that in cases where actors are located in diverse institutional contexts, competing interests determine the respective priority given to the welfare and market logics.

Digital Labour Platforms in Pakistan: Institutional Voids and Solidarity Networks” by Fareesa Malik, Richard Heeks, Silvia Masiero & Brian Nicholson, conceptualises the theoretical link between labour platforms and socio-economic development drawing on the notion of institutional voids and empirical fieldwork in Pakistan.

Risks and Risk-Mitigation Strategies of Gig Economy Workers in the Global South” by Tatenda Mpofu, Pitso Tsibolane, Richard Heeks & Jean-Paul Van Belle, analyses three strategies (platform-, driver- and driver group-led) that seek to mitigate the risks of ride-hailing work in Cape Town.

The Fairwork Foundation: Strategies for Improving Platform Work in a Global Context” (open access) by Mark Graham, Jamie Woodcock, Richard Heeks, Paul Mungai, Jean-Paul Van Belle, Darcy du Toit, Sandra Fredman, Abigail Osiki, Anri van der Spuy & Six M. Silberman, introduces the work of the Fairwork Foundation to rank and compare gig work platforms against a set of five decent work principles.

DIGITAL PLATFORMS AND DEVELOPMENT

Analysing Urban Platforms and Inequality Through a ‘Platform Justice’ Lens” by Richard Heeks & Satyarupa Shekhar, introduces a model of “platform justice” through which to analyse the impact of urban digital platforms.

Competing Logics: Towards a Theory of Digital Platforms for Socio-economic Development” by Silvia Masiero & Brian Nicholson, seeks to contribute to the nascent literature on platforms in development, unpacking a human-centred development logic as an alternative to the market logic that animates most of the platforms discourse and relying on it to lay the foundations for an emerging theory of platforms for development.

Digital Platforms, Surveillance and Processes of Demoralization” by Sung Chai, Brian Nicholson, Robert Scapens & Chunlei Yang, conceptualises the theoretical link between platforms and morality drawing on an interpretive study of a hotel in Vietnam to examine surveillance.

Delivering Urban Data Justice for “Smart Cities 2.0”

What new institutions are needed to ensure smart cities are also data-just cities?

Smart City 1.0 “is primarily focused on diffusing smart technologies for corporate and economic interests”.  Smart City 2.0 is “a decentralised, people-centric approach where smart technologies are employed as tools to tackle social problems, address resident needs and foster collaborative participation”.[1]

Given their people-centrism, a foundation for Smart Cities 2.0 must therefore be delivery of urban data justice: fairness in the way people are made visible, represented and treated as a result of the production of urban digital data.[2]

We already know the constituent parts of urban data justice, as shown in the figure below.[3]

But a key argument of this model is that data justice is significantly shaped by urban social structures.  If those structures are unjust then data practices and outcomes will likely be unjust.  How, then, do we create urban social structures more likely to deliver the data justice that is part of Smart City 2.0?

Setting aside more radical restructuring of the urban polity, three more incremental forms can play a role:

1. Living Labs

“Living labs employ a user-focused design environment, a strategy of co-creation, and, increasingly, an institutionalized space wherein citizens, administrators, entrepreneurs and academics come together to develop smartness into concrete applications. They help identify and join localized expertise, real-life testing and prototyping with strategic networking of resources to address challenges that cannot be solved by single cities or departments.”[4]  Located at the upstream end of the innovation cycle, living labs are well-placed to come up with new, just ways of applying urban data.[5]

2. Urban Data Trusts

Data trusts are “a legal structure that provides independent stewardship of data … an approach to looking after and making decisions about data in a similar way that trusts have been used to look after and make decisions about other forms of asset in the past, such as land trusts that steward land on behalf of local communities.”[6]  These can form an institutional superstructure to ensure justice in the ownership, sharing and use of data; particularly data gathered about urban citizens.[7]

3. Community Data Intermediaries

Community data intermediaries are “organizations that gather data relevant for neighborhood-level analysis and make the information available to community groups and local institutions”.  Alongside their key role in gathering data – for example via community mapping – CDIs may also have features of both living labs (innovating application of that data) and data trusts (acting as stewards of the data for communities).[8]

The devil here will be in the detail: how exactly are these entities structured and run?  Simply attaching a label to an organisation does not make it just, with critiques in circulation of living labs[9], urban data trusts[10], and community data intermediaries[11].  Nonetheless, it is these types of urban institutional innovation that will underlie delivery of data justice in Smart Cities 2.0.  I look forward to further examples of these and similar innovations.

 

[1] Trencher, G. (2019) Towards the smart city 2.0: empirical evidence of using smartness as a tool for tackling social challenges, Technological Forecasting and Social Change, 142, 117-128

[2] Adapted slightly from Taylor, L. (2017) What is data justice? The case for connecting digital rights and freedoms globally, Big Data & Society, 4(2), 2053951717736335

[3] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

[4] Baykurt, B. (2020) Are “smart” cities living up to the hype?, University of Massachusetts Amherst News, 1 May

[5] For a data justice perspective on the activities of one Living Lab in Kathmandu plus related organisations, see: Mulder, F. (2020) Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal, Journal of Contingencies and Crisis Management, 28(4), 432-445

[6] Hardinges, J. (2020) Data trusts in 2020, Open Data Institute, 17 Mar

[7] For more on urban civic data trusts, see: Kariotis, T. (2020) Civic Data Trusts, Melbourne School of Government, University of Melbourne, Australia

[8] For a guide on creating community data intermedaries and examples, see: Hendey, L., Cowan, J., Kingsley, G.T. & Pettit, K.L. (2016) NNIP’s Guide to Starting a Local Data Intermediary, NNIP, Washington, DC

[9] Taylor, L. (2020) Exploitation as innovation: research ethics and the governance of experimentation in the urban living lab. Regional Studies, advance online publication.

[10] Artyushina, A. (2020) Is civic data governance the key to democratic smart cities? The role of the urban data trust in Sidewalk Toronto, Telematics and Informatics, 55, 101456

[11] Heeks, R. & Shekhar, S. (2019) Datafication, development and marginalised urban communities: An applied data justice framework, Information, Communication & Society, 22(7), 992-1011

Latest Digital Development Outputs (Agriculture, Data, Social Media) from CDD, Manchester

Recent outputs – on Agricultural Platforms; Data-for-Development; Social Media and Education – from the Centre for Digital Development, University of Manchester:

AGRICULTURAL PLATFORMS:

Ag-Platforms in East Africa: National and Regional Policy Gaps” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag identifies national and regional governance deficits (gaps) in the diffusion of digital agricultural platforms, and consequently how Ag-platforms bridge national and regional policy gaps.

Platforms in Agricultural Value Chains: Emergence of New Business Models” (pdf) by Aarti Krishnan, Karishma Banga & Joseph Feyertag explains the various models of digital agricultural platforms that exist, and provides policy-makers with a roadmap that supports the proliferation of sustainable Ag-platforms.

DATA-FOR-DEVELOPMENT:

Datafication, Value and Power in Developing Countries” by Richard Heeks, Vanya Rakesh, Ritam Sengupta, Sumandro Chattapadhyay & Christopher Foster analyses the implementation challenges and impact of big data on organisational value, sources of power, and wider politics.

Identifying Potential Positive Deviants Across Rice-Producing Areas in Indonesia: An Application of Big Data Analytics and Approaches” (open access) by Basma Albanna, Dharani Dhar Burra & Michael Dyer uses remote sensing and survey data to identify “positive deviant” rice-farming villages in Indonesia: those which outperform their peers in agricultural productivity.

The Urban Data Justice Case Study Collection” (open access) presents ten case studies analysing new urban data in Latin America, Africa and Asia from data justice/rights perspectives.  It also outlines a future research agenda on urban data justice in the global South.

SOCIAL MEDIA AND EDUCATIONAL DEVELOPMENT

WhatsApp-Supported Language Teacher Development: A Case Study in the Zataari Refugee Camp” (open access) by Gary Motteram, Susan Dawson & Nazmi Al-Masri through a thematic analysis of WhatsApp exchanges, explores how Syrian English Language teachers working in refugee camps in Jordan work collaboratively on teacher development.

Big data and development in India. The hype and the reality

Many around the world celebrated the agreement of the Sustainable Development Goals (SDGs) and a new agenda for transformative development by 2030. But, practitioners and policy makers were left scratching their heads as to how they were going to monitor the detailed 169 targets and ever more numerous indicators, never mind understanding and achieving these goals.

It is in this context that we’re seeing a growth of interest in using data to help solve development problems. Indeed, we can say that the infrastructures now being built to support data are likely to become central to how we make development decisions in the future.

How will such data infrastructures shape our thinking about development over the next decade? What types of limitations and biases might they embed? How should they best be designed and implemented? It is these questions that we looked to explore in a recent paper [1] analysing big data use for development in India.

In this paper we dug into two cases where big data was being used to support wider development over commercial goals – the Bengaluru Metropolitan Transport Corporation (BMTC) and big data transport upgrades in Bengaluru, India; and Stelcorp (name changed), a state initiative using big data for improving electricity systems.

Digging into big data

Digging into these cases, we found that both of these initiatives were connected into longer, often decades-old histories of data collection and decision making. This meant that new data innovations were being introduced in an attempt to understand long running development problems. Thus, the main focus of BMTC was on using vehicle tracking and big data innovations to improve the notoriously unreliable city bus services.

We found that big data innovation allowed improved integration of rich information flows, and led to centralisation of decision making. In StelCorp, previously manually-collected meter data was now digitally-collected and aggregated (see images below). The supporting infrastructure allowed a near real-time analysis of the status of the electricity network, and was more effective at monitoring around failures and blackouts. A new central data centre played a growing role in processing and analysing this data. In BTMC, new bus transportation data was aggregated and fed in real-time to large screens in a “control centre” where activity was monitored by administrators.

Digitalisation in Stelcorp: Meters such as those on the left supply real time data about network usage. Even manual meter reading data is now often transferred through automated reading devices (right) to later be input into the system.

Beyond day-to-day monitoring, we also saw signs that the new data was feeding into more strategic decisions. In the electricity sector, for example, upgrades have been plagued by poor and politicised decision making, but the state-wide data from Stelcorp is now being used in upgrading decisions.

More conceptually, there is evidence that these initiatives are playing a role in supporting new forms of state commitments, or citizen interaction. BTMC has been associated with a ‘Smart City’ initiative and citizens interacting with a set of efficient urban services. Indeed, BTMC introduced a citizen mobile app for tracking bus routes which has had over 50,000 downloads. In the Stelcorp initiative, state political visions about “24/7 electricity” have in part emerged from the better data that allows improved management of the electricity system.

Limitations

Whilst big data has led to these operational, strategic and visionary advances, there were a number of concerns in these projects. One key concern raised was the quality of data being used in these projects, which was often incomplete, short-term, or skewed.

Most problematic was that data from marginal groups was difficult to obtain, so in Stelcorp, automated electricity data was mainly coming from cities, where rural data was still manually collected, and in both cases there was often the need for “data wrangling” before the data had value.

These data limitations pose questions of how representative the data being used is of the population. If certain measures are skewed towards those more affluent, data coming from those more marginal might then be seen as “nonconforming” or even deviant. Moreover, the way that the data is selected, measured and transformed in such systems will be important in determining what processes are made visible by data and what might remain in the shadows.

The Smart Cities Challenge: Such visions can be seen to be made viable by the growth of big data. However in reality big data projects often tend to have a narrower focus. Source: http://www.smartcitieschallenge.in/

There were also more general questions about the focus of big data projects. These projects were marketed and discussed under lofty development goals, but in implementation they were often quite narrow projects. BTMC, for all its discussion of smart cities and citizens, was far more focussed on stamping out corruption among bus employees than making the city’s public transport smart.

Further, in all these projects there is scant sharing of the new data produced. These projects have not been about the public shining a light on opaque mechanisms of decision making. In fact, with a growing number of public and private actors involved, mechanisms of decision making are becoming even less transparent.

Big data for development

Big data projects are in their infancy in countries like India, but as these cases show they are becoming important to support decision making on key development issues, not only at an operational level, but in strategic decision making and in supporting new visions of developmental partnerships between citizens, private sector and the state.

However, these initiatives rarely follow the vision of big data driving transformative changes. They so-far tend to use problematic data to enhance decision making. They also tend to focus on quite narrow aspects of problems in implementation over the bigger development problems that might be more impactful.

We also need to make sure that big data does not solely lead to technocratic solutions, or underplay the importance of integrating with a wider set of social and political activities for development – data showing electricity pilferage will have limited impact without solving the complexities of local politics of electricity in rural and slum areas, and data on public vehicle movements cannot replace the underfunding of urban transport.

[1] Heeks, R., Rakesh, V., Sengupta, R., Chattapadhyay, S. & Foster, C. (In press) Datafication, Value and Power in Developing Countries: Big Data in Two Indian Public Service Organisations. Development Policy Review.


This is an adapted version of a blog originally posted on the Sheffield Institute of International Development (SIID) blog.

With thanks to Vanya Rakesh & Ritam Sengupta for their research in India and SIID and the University of Manchester for the small grant support for this work.

Positive Deviance: A Data-Powered Approach to the Covid-19 Response

Nations around the world are struggling with their response to the Covid-19 pandemic.  In particular, they seek guidance on what works best in terms of preventive measures, treatments, and public health, economic and other policies.  Can we use the novel approach of data-powered positive deviance to improve the guidance being offered?

Positive Deviance and Covid-19

Positive deviants are those in a population that significantly outperform their peers.  While the terminology of positive deviance is absent from public discourse on Covid-19, the concept is implicitly present at least at the level of nations.  In an evolving list, countries like New Zealand, Australia, Taiwan, South Korea and Germany regularly appear among those seen as most “successful” in terms of their relative infection or death rates so far.

Here we argue first that the ideas and techniques of positive deviance could usefully be called on more directly; second that application of PD is probably more useful at levels other than the nation-state.  In the table below, we summarise four levels at which PD could be applied, giving potential examples and also potential explanators: the factors that underpin the outperformance of positive deviants.

Level Potential positive deviants Potential PD explanators
Nation[i] Countries with very low relative infection or death rates
  • Early lockdown
  • Extensive testing
  • Use of contact-tracing incl. apps
  • Cultural acceptance of mask-wearing
  • Prior mandatory TB vaccination
  • Quality of leadership
Locality (Regions, Cities)[ii] Cities and regions with significantly slower spread of Covid-19 infection than peers
  • Extensive or innovative community education campaigns
  • Testing well in excess of national levels
  • Earlier-than-national lockdown
  • Extensive sanitisation of public transport
  • Quality and breadth of local healthcare
  • Quality of leadership
Facility (Hospitals, Health Centres)[iii] Health facilities with significantly higher recovery rates than peers
  • Innovative use of existing (scarce) healthcare technologies / materials
  • Innovative use of new healthcare technologies: AI, new treatments
  • Level of medical staff expertise and Covid-19-specific training
Health facilities with significantly lower staff infection rates than peers
  • Provision of high-quality personal protective equipment in sufficient quantity
  • Strict adherence to infection monitoring and control measures
  • Strict adherence to high-quality disinfection procedures
  • Innovative use of contact-free healthcare technologies: chat bots, robots, interactive voice response, etc
Individual[iv] Individuals in vulnerable groups who contract full-blown Covid-19 and survive
  • Psychological resilience
  • Physical fitness
  • Absence of underlying health conditions
  • Effective therapies
  • Genetics

 

At present, items in the table are hypothetical and/or illustrative but they show the significant value that could be derived from identification of positive deviants and their explanators.  Those explanators that are under social control – such as use of technological solutions or policy/managerial measures – can be rapidly scaled across populations.  Those explanators such as genetics or pre-existing levels of healthcare capacity which are not under social control can be built into policy responses; for example in customising responses to particular groups or locations.

Evidence from positive deviance analysis can help currently in designing policies and specific interventions to help stem infection and death rates.  Soon it will be able to help design more-effective lockdown exit strategies as these start to show differential results, and as post-lockdown positive deviants start to appear.

However, positive deviance consists of two elements; not just outperformance but outperformance of peers.  It is the “peers” element that confounds the value of positive deviance at the nation-state level.

Public discourse has focused mainly on supposedly outperforming nations [v]; yet countries are complex systems that make meaningful comparisons very difficult[vi]: dataset definitions are different (e.g. how countries count deaths); dataset accuracy is different (with some countries suspected of artificially suppressing death rates from Covid-19); population profiles and densities are different (countries with young, rural populations differing from those with old, urban populations); climates are different (which may or may not have an impact); health service capacities are different; pre-existing health condition profiles are different; testing methods are different; and so on.  Within all this, there is a great danger of apophenia: the mistaken identification of “patterns” in the data that are either not actually present or which are just random.

More valid and hence more useful will be application of positive deviance at lower levels.  Indeed, the lower the level, the more feasible it becomes to identify and control for dimensions of difference and to then cluster data into true peer groups within which positive deviants – and perhaps also some of their explanators – can then be identified.

Data-Powered Positive Deviance and Covid-19

The traditional approach to identifying positive deviants has been the field survey: going out into human populations (positive deviants have historically been understood only as individuals or families) and asking questions of hundreds or thousands of respondents.  Not only was this time-consuming and costly but it also becomes more risky or more difficult or even impractical during a pandemic.

Much better, then, is to look at analysis of large-scale datasets which may be big data[vii] and/or open data, since this offers many potential benefits compared to the traditional approach[viii].  Many such datasets already exist online[ix], while others may be accessed as they are created by national statistical or public health authorities.

Analytical techniques, such as those being developed by the Data-Powered Positive Deviance project, can then be applied: clustering the data into peer groups, defining the level of outperformance needed to be classified as a positive deviant, identifying the positive deviants, then interrogating the dataset further to see if any PD explanators can be extracted from it.

An example already underway is clustering the 368 districts in Germany based on data from the country’s Landatlas dataset and identifying those which are outperforming in terms of spread of the virus.  Retrospective regression analysis is already suggesting structural factors that may be of importance in positive deviant districts: extent and nature of health infrastructure including family doctors and pharmacies, population density, and levels of higher education and of unemployment.

This can then be complemented in two directions – diving deeper into the data via machine learning to try to predict future spread of the disease; and complementing this large-scale open data with “thick data” using online survey and other methods to identify the non-structural factors that may underlie outperformance.  The latter particularly will look for factors under socio-political control such as policies on lockdown, testing, etc.

Of course, great care must be taken here.  Even setting aside deliberate under-reporting, accuracy of the most basic measures – cases of, and deaths from Covid-19 – has some inherent uncertainties[x].  Beyond accuracy are the broader issues of “data justice”[xi] as it applies to Covid-19-related analysis[xii], including:

  • Representation: the issue of who is and is not represented on datasets. Poorer countries, poorer populations, ethnic minority populations are often under-represented.  If not accounted for, data analysis may not only be inaccurate but also unjust.
  • Privacy: arguments about the benefits of analysing data are being used to push out the boundaries of what is seen as acceptable data privacy; opening the possibility of greater state surveillance of populations. As Privacy International notes, any boundary-pushing “must be temporary, necessary, and proportionate”[xiii].
  • Access and Ownership: best practice would seem to be datasets that are publicly-owned and open-access with analysis that is transparently explained. The danger is that private interests seek to sequester the value of Covid-19-related data or its analysis.
  • Inequality: the key systems of relevance to any Covid-19 response are the economic and public health systems. These contain structural inequalities that benefit some more than others.  Unless data-driven responses take this into account, those responses may further exacerbate existing social fracture lines.

However, if these challenges can be navigated, then the potential of data-powered positive deviance can be effectively harnessed in the fight against Covid-19.  By identifying Covid-19 positive deviants, we can spotlight the places, institutions and people who are dealing best with the pandemic.  By identifying PD explanators, we can understand what constitutes best practice in terms of prevention and treatment; from public health to direct healthcare.  By scaling out those PD explanators within peer groups, we can ensure a much-broader application of best practice which should reduce infections and save lives.  And using the power of digital datasets and data analytics, we can do this in a cost- and time-effective manner.

The “Data-Powered Positive Deviance” project will be working on this over coming months.  We welcome collaborations with colleagues around the world on this exciting initiative and encourage you to contact the GIZ Data Lab or the Centre for Digital Development (University of Manchester).

This blogpost was co-authored by Richard Heeks and Basma Albanna and was originally published on the Data-Powered Positive Deviance blog.

 

 

[i] https://interestingengineering.com/7-countries-keeping-covid-19-cases-in-check-so-far; https://www.forbes.com/sites/avivahwittenbergcox/2020/04/13/what-do-countries-with-the-best-coronavirus-reponses-have-in-common-women-leaders; https://www.maskssavelives.org/; https://www.bloomberg.com/news/articles/2020-04-02/fewer-coronavirus-deaths-seen-in-countries-that-mandate-tb-vaccine

[ii] https://www.weforum.org/agenda/2020/03/how-should-cities-prepare-for-coronavirus-pandemics/; https://www.wri.org/blog/2020/03/covid-19-could-affect-cities-years-here-are-4-ways-theyre-coping-now; https://www.fox9.com/news/experts-explain-why-minnesota-has-the-nations-lowest-per-capita-covid-19-infection-rate; https://www.bbc.co.uk/news/world-asia-52269607

[iii] https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19; https://www.cuimc.columbia.edu/news/columbia-develops-ventilator-sharing-protocol-covid-19-patients; https://www.esht.nhs.uk/2020/04/02/innovation-and-change-to-manage-covid-19-at-esht/; https://www.med-technews.com/topics/covid-19/; https://www.innovationsinhealthcare.org/covid-19-innovations-in-healthcare-responds/; https://www.cnbc.com/2020/03/23/video-hospital-in-china-where-covid-19-patients-treated-by-robots.html; https://www.researchprofessionalnews.com/rr-news-new-zealand-2020-4-high-quality-ppe-crucial-for-at-risk-healthcare-workers/; https://www.ecdc.europa.eu/sites/default/files/documents/Environmental-persistence-of-SARS_CoV_2-virus-Options-for-cleaning2020-03-26_0.pdf

[iv] https://www.sacbee.com/news/coronavirus/article241687336.html; https://www.thelocal.it/20200327/italian-101-year-old-leaves-hospital-after-recovering-from-coronavirus; https://www.vox.com/science-and-health/2020/4/8/21207269/covid-19-coronavirus-risk-factors; https://www.medrxiv.org/content/10.1101/2020.04.22.20072124v2; https://www.bloomberg.com/news/articles/2020-04-16/your-risk-of-getting-sick-from-covid-19-may-lie-in-your-genes

[v] Specifically, this refers to the positive discourse.  There is a significant “negative deviant” discourse (albeit, again, not using this specific terminology) that looks especially at countries and individuals which are under-performing the norm.

[vi] https://www.bbc.co.uk/news/52311014; https://www.theguardian.com/world/2020/apr/24/is-comparing-covid-19-death-rates-across-europe-helpful-

[vii] https://www.forbes.com/sites/ciocentral/2020/03/30/big-data-in-the-time-of-coronavirus-covid-19; https://healthitanalytics.com/news/understanding-the-covid-19-pandemic-as-a-big-data-analytics-issue

[viii] https://doi.org/10.1002/isd2.12063

[ix] E.g. via https://datasetsearch.research.google.com/search?query=coronavirus%20covid-19

[x] https://www.medicalnewstoday.com/articles/why-are-covid-19-death-rates-so-hard-to-calculate-experts-weigh-in; https://www.newsletter.co.uk/health/coronavirus/coronavirus-world-health-organisation-accepts-difficulties-teasing-out-true-death-rates-covid-19-2527689

[xi] https://doi.org/10.1080/1369118X.2019.1599039

[xii] https://www.opendemocracy.net/en/openmovements/widening-data-divide-covid-19-and-global-south/; https://www.wired.com/story/big-data-could-undermine-the-covid-19-response/; https://www.thenewhumanitarian.org/opinion/2020/03/30/coronavirus-apps-technology; https://botpopuli.net/covid19-coronavirus-technology-rights

[xiii] https://privacyinternational.org/examples/tracking-global-response-covid-19; see also https://globalprivacyassembly.org/covid19/