How are Data used to Create Change? The Case of Violent Attacks on Health Care

On 28 March 2021 Myanmar security forces shot protesters in Yangon city. Some sought refuge in the hospital where soldiers and police followed them and opened fire. Unfortunately, this kind of violence on health care is all too common in contexts affected by armed conflict. Since the full-scale invasion of Ukraine in 2022, the World Health Organization (WHO) Surveillance System for Attacks on Health Care (SSA) has verified over 715 attacks on health. A Safeguarding Health in Conflict Coalition (SHCC) and Insecurity Insight review of five years of data on attacks found ‘more than 4,000 unique incidents of violence against health care in situations of armed conflict—on average more than two incidents a day.’ These attacks threaten health workers, individual health outcomes for patients and conflict-affected populations, and jeopardise access to health services. 

The question in the title may seem relatively straightforward. Scholarly and policy analysis about attacks on health care call for more data and better monitoring to document and understand the issue and to ensure accountability (see here and here). These numbers have gotten headlines about Ukraine and COVID-19, helping to raise awareness about these attacks. 

But do numbers do more than raise awareness? What do we know about their influence? Put another way, what is the relationship between data, often numbers, and changes in policy or behaviour? Scholars have examined the efficacy of transnational advocacy and decision-making but few have examined the specific role of data in these processes. 

In a recent publication, my colleague Róisín Read and I consider the relationship between data and change, as part of a broader effort to research the impact of attacks on health care. In our open-access article, we argue that data about attacks on healthcare are indeed necessary for understanding the scope of the problem and for raising awareness. But the continued occurrence of attacks demonstrates that data are insufficient in creating normative, policy, or behavioural change. To investigate the complex and potential role of data in these processes, we focus on two pathways for change. We call the first pathway ‘operational change,’ designed to prevent or mitigate the impact of attacks on health. The second refers to normative change, often pursued via transnational advocacy aiming to achieve a reduction in the frequency of attacks. The former operates at the level of those affected by attacks, while the latter works at the level of those perpetrating attacks.

Our investigation highlights the institutional, political, and social contexts in which data are produced and used, and how these contexts can be as significant as the evidence they provide for decision-making and advocacy efforts. We find that many issues impact on the role of data related to policy or programmatic change, from the technical (eg related to standards and terminology) to issues of bias and the social or institutional networks that shape data collection and use. To be useful, data should be collected with a clear purpose that is meaningful for those collecting, analysing, and using the data. Moreover, the political context impacts on the framing of data and the incentives to under- or overreport, whether about harms or disease. Even the terminology used in collecting data can be a point of political contention. As we write, ‘Data are never neutral; they privilege particular, subjective realities that are especially contested in fractious political contexts.’ 

Additionally, at the levels of operational and normative change, the role of personal and institutional relationships are crucial. For instance, individuals and organizations bring existing biases and frames of reference to bear on the data they encounter. As a result, data that challenge preconceptions are likely to require a higher burden of proof to become credible. Yet personal and institutional connections also strengthen trust in and interpretation of data. This highlights the crucial role of broad-based networks, which can help to build trust in the underlying data. In doing so, these connections enhance the potential for data to influence change.As academics do, we conclude with a call for more research to investigate the often positive but non-linear role of data in change processes. While our specific focus was on the relationship between data attacks on health, we hope these insights assist other efforts to affect decision-making or create behavioural, policy, or normative change.

Advertisement

Latest Digital Development Outputs (China, Data, Economy/Platforms, Inclusion, Water, Rights, Sustainability) from CDD, Manchester

Recent outputs – on China Digital; Data-for-Development; Digital Economy / Platforms; Digital Inclusion; Digital Water; Rights; and Sustainability – from Centre for Digital Development researchers, University of Manchester:

CHINA DIGITAL

“China’s digital expansion in the global South” presents recordings of nine presentations at a CDD international workshop that discusses the implications for the global South of China’s emergence as a digital superpower.

“Understanding the evolution of China’s standardization policy system” (open access) by You-hong Yang, Ping Gao & Haimei Zhou, investigates the evolution of China’s technology standardization policy system in the period from 1978 to 2021.  

DATA-FOR-DEVELOPMENT

A DC State of Mind? A Review of the World Development Report 2021: Data for Better Lives by Hellen Mukiri-Smith, Laura Mann & Shamel Azmeh, reviews the World Development Report (2021) on data governance.

DIGITAL ECONOMY / PLATFORMS

“Examining ecosystems and infrastructure perspectives of platforms: the case of small tourism service providers in Indonesia and Rwanda” (open access version available) by Christopher Foster & Caitlin Bentley, analyses tourism platforms from the perspective of small and marginal service providers. It is useful to move away from ideas of platform leaders organising ecosystems from the top-down, towards more emergent behaviours of service providers in multi-platform environments.

Automation and industrialisation through global value chains: North Africa in the German automotive wiring harness industry by Shamel Azmeh, Huong Nguyen & Marlene Kuhn, examines the implications of automation on the global map of production and the position of developing countries in global value chains. Through the case of the German automotive wiring harness industry, we examine the implications of ongoing automation processes on production in North Africa.

“Digital public goods platforms for development: the challenge of scaling” (open access) by Brian Nicholson, Petter Nielsen, Sundeep Sahay & Johan Saebo.  We articulate the notion of digital global public goods and examine the development of DHIS2, a global health platform inspired by public goods, focusing on the paradoxes that arise in the scaling process. A presentation of the paper to the Pankhurst Institute, University of Manchester is available on YouTube.

DIGITAL INCLUSION

“Digital inequality beyond the digital divide: conceptualizing adverse digital incorporation in the global South” (open access) by Richard Heeks, presents a new model to understand how inclusion in – rather than exclusion from – digital systems leads to inequality.

“Revisiting digital inclusion: a survey of theory, measurement and recent research” (open access) by Matthew Sharp, sets out a framework of core components of digital inclusion, surveys current measures of digital inclusion, and makes suggestions for how future research could be more rigorous and useful.

DIGITAL WATER

“Water ATMs and access to water: digitalisation of off-grid water infrastructure in peri-urban Ghana” (open access) by Godfred Amankwaa, Richard Heeks & Alison L. Browne, finds water ATMs to be incremental infrastructures delivering relatively limited and operational-level value, but also producing new and contested socio-material realities.

RIGHTS AND DIGITAL

“RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour” (open access) by Erick Mendez Guzman, Viktor Schlegel & Riza Batista-Navarro, describes a dataset of news articles categorised according to forced labour indicators. The articles were annotated with rationales, i.e. human explanations for placing them under specific categories, to support the development of explainable AI systems.

“Hustling day in Silicon Savannah: datafication and digital rights in East Africa” (open access) by Gianluca Iazzolino, Michael Kimani & Maddo, is a cartoon on the winners and losers in Kenya’s booming tech scene. It translates, for a non-academic audience, the authors’ research on how digital technologies are reshaping the informal economy in the global South.

SUSTAINABILITY AND DIGITAL

“Exploring financing for green-tech SMEs in East Africa: current trends and risk appetite” (open access) by Aarti Krishnan, reviews the financing of green-tech SMEs in East Africa including different financing at different enterprise lifecycle stages, in different sectors, and across different countries.

“Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework” by Faris Elghaish, Sandra T. Matarneh, David John Edwards, Farzad Pour Rahimian, Hatem El-Gohary & Obuks Ejohwomu, investigates the interrelationships between emerging digital technologies and the circular economy, concluding with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI.

Follow @CDDManchester

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 Data	Example
Quantitative/numbers	Numbers of beneficiaries/aid recipients
Qualitative/narrative	Descriptions of workshops or programme activities
Personal	Demographic data (names or contact information of aid recipients, group information, such as ethnicity)
Non-personal	Data about groups affected by the humanitarian situation, including needs or the threats they face
Group	Data about groups of aid recipients (women, children, disabled), such as location
Individual	Age, sex or gender data about individual aid recipients
Financial	Budget reports
Audit/compliance	Reporting against legal or regulatory requirements, such as for safeguarding or counter-terrorism or sanctions
Situational	Analysis of security situation
Organisational	Contact information for project officer
Disaggregated indicator	Location-specific data for those assisted in a project
Aggregated indicator	Total 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.

Follow @CDDManchester

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

Recent 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.

Follow @CDDManchester

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.

Follow @CDDManchester

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.

Follow @CDDManchester

 

[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.

Follow @CDDManchester