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What can urban big data research tell us about cities? While studying cities as complex systems offers a new perspective on urban dynamics, we should dig deeper into the epistemological claims made by these studies and ask what it means to distance the urban researcher from the city. Big data research has the tendency to flatten our perspective: it shows us technology users and their interactions with digital systems but does so often at the expense of the informal and irregular aspects of city life. It also presents us with the city as optimisable system, offering up the chance to engineer it for particular forms of efficiency or productivity. Both optimisation itself, and the process of ordering of the city for optimisation, confer political and economic power and produce a hierarchy of interests. This commentary advocates that researchers connect systems research to questions of structure and power. To do this requires a critical approach to what is missing, what is implied by the choices about which data to collect and how to make them available, and an understanding of the ontologies that shape both the data sets and the urban spaces they describe.

Crisis narratives surrounding Europe's 2015 migration influx fuelled demands for new ways of tracking, mapping and predicting human mobility. We explore how market opportunities for technology firms and data analytics start-ups created by the EU's Global Approach to Migration led to solutionistic approaches to compiling and analysing migration statistics. We show that initiatives such as the rebranding of existing platforms and services as migration prediction systems are consolidating policy conceptualisations of migration as risk. Despite the promise of greater granularity, this "big data approach" cannot offer greater certainty about who is on the move and why. Instead such approaches are ill-suited to understanding the complex dynamics of migration and to offering protection to vulnerable people. The marketisation of migration statistics through big data offers a key case for advancing progressive approaches to both migration statistics and global data justice.Los relatos de crisis en torno a las corrientes migratorias hacia Europa del año 2015 han impulsado la demanda de nuevas formas de seguimiento, mapeo y predicción de la movilidad de personas. Exploramos cómo las oportunidades de mercado para las empresas de tecnología y las nuevas empresas de análisis de datos creadas por el Enfoque Global de la Migración de la UE han causado enfoques simplificadores al compilar y analizar las estadísticas migratorias. Mostramos que iniciativas tales como el cambio de nombre de ciertas plataformas y servicios existentes en los sistemas de predicción migratoria están transformando la conceptualización de las política migratoria como un riesgo. A pesar de la promesa de una mayor granularidad, este “enfoque big data” no logra ofrecer una mayor certeza sobre quién está en movimiento y por qué. Al contrario, estos enfoques no son adecuados para comprender las complejas dinámicas migratorias ni para ofrecer protección a las personas vulnerables. La comercialización de las estadísticas migratorias a través de Big Data ofrece un caso clave para avanzar sobre enfoques más progresivos tanto para las estadísticas migratorias y como para la justicia global de datos.

As data technologies become the medium of experiment for living labs, they become less a collaboration between citizen and researcher and more a test of how commercial actors can influence the public. Two new practices suggest we shouldapply research ethics rules: first, that the experimentation taking place does not aim to test technology using people, but to test people using technology; and second, that such experimentation is explicitly designed to understand how thepopulation outside the lab can be influenced and manipulated, and therefore has a political character that research ethics can give us some leverage over.

The scale and asymmetry of commercial technology firms’ power over people through data, combined with the increasing involvement of the private sector in public governance, means that increasingly, people do not have the ability to opt out of engaging with technology firms. At the same time, those firms are increasingly intervening on the population level in ways that have implications for social and political life. This creates the potential for power relations of domination, and demands that we decide what constitutes the legitimacy to act on the public. Business ethics and private law are not designed to answer these questions, which are primarily political. If people have lost the right to disengage with commercial technologies, we may need to hold the companies that offer them to the same standards to which we hold the public sector. This paper first defines the problem and demonstrates that it is significant and widespread, and then argues for the development of an overarching normative framework for what constitutes non-domination with regard to digital technologies. Such a framework must involve a nuanced idea of political power and accountability that can respond not only to the legality of corporate behaviour, but to its legitimacy.

A new research challenge for computer science.

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitlyconnected to a social justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: (in)visibility, (dis)engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract.

In this paper, we investigate people’s perception of datafication andsurveillance in Amsterdam Smart City. Based on a series of focusgroups, we show how people understand new forms of hypervisbi-lity, what strategies they use to navigate these experiences, and whatthe limitations of these strategies are. We show how people tried todiscern between public and private sector actors, to differentiate whothey trusted by building on the existing social contract. People alsotrusted the objectivity of data in relation to prior experiences of socialcontexts and discrimination. Lastly, we show how the experiences ofsome of the inhabitants in our study who were most vulnerable tohypervisibility highlight the limits to strategies based on the neutral-ity of data. By asking about perceived surveillance rather thanemphasising actual practices of surveilling, we show differentiatedcontexts and strategies, providing empirical grounds to question thedominant technical framing of smart cities.

This Campbell systematic review assesses the effects of business support services in low‐ and middle‐income countries on firm performance and economic development. The review summarizes findings from 40 studies. Included studies examine interventions targeted at SMEs (two to 250 employees) involving tax simplification, exports and access to external markets; support for innovation policies; support to local production systems; training and technical assistance, and SME financing and credit guarantee programmes. Findings from 40 studies are summarised in the review. These studies present evidence from 18 low‐ and middle‐income countries, with 26 studies analysing programmes in Latin America, six from Asia and five from Africa. On average, business support to SMEs improves their performance, their ability to create jobs, their labour productivity and their ability to invest. The effects on innovation are unclear. Matching grants, technical assistance and tax simplification programmes improve firms' performance and job creation; with technical assistance also improving labour productivity. Export promotion and innovation programmes positively affect exports and innovation, but there is no evidence that they improve performance or job creation. However, the effects of the programmes studied are not very large. Most studies do not include the required data to assess if the programmes are cost effective. Plain language summary BUSINESS SUPPORT SERVICES TO SMALL AND MEDIUM ENTERPRISES SEEM TO IMPROVE FIRM PERFORMANCE The Campbell review in brief Support to small and medium enterprises (SMEs) can improve their revenue and profits, their ability to create jobs, labour productivity and their ability to invest. But these effects are not large, and the cost effectiveness of the interventions not known. The effects on innovation are unclear. What is this review about? Large amounts of funding are going towards programmes to support small and medium enterprises (SMEs) in low‐ and middle‐income countries in order to increase revenue and profits, generate employment, and, so, create economic growth and reduce poverty. The Campbell review summarizes evidence of the impact of these programmes on measures of SME performance including revenues, profits, and productivity, as well as the firms' ability to generate employment and their labour productivity. What are the main findings of this review? What studies are included? Included studies examine interventions targeted at SMEs (two to 250 employees) involving tax simplification, exports and access to external markets; support for innovation policies; support to local production systems; training and technical assistance, and SME financing and credit guarantee programmes. What is the aim of this review? This Campbell systematic review assesses the effects of business support services in low‐ and middle‐income countries on firm performance and economic development. The review summarizes findings from 40 studies. Findings from 40 studies are summarised in the review. These studies present evidence from 18 low‐ and middle‐income countries, with 26 studies analysing programmes in Latin America, six from Asia and five from Africa. Do business support services work? On average, business support to SMEs improves their performance, their ability to create jobs, their labour productivity and their ability to invest. The effects on innovation are unclear. Matching grants, technical assistance and tax simplification programmes improve firms' performance and job creation; with technical assistance also improving labour productivity. Export promotion and innovation programmes positively affect exports and innovation, but there is no evidence that they improve performance or job creation. However, the effects of the programmes studied are not very large. Most studies do not include the required data to assess if the programmes are cost effective. What do the results mean? Overall SME support has a positive impact on various measures of firm performance, but with some caveats. Results for all the interventions studied could not be provided due to a lack of evidence. And the evidence available was mainly about programmes in Latin American countries. There is a likelihood of bias in many studies. Most did not report programme implementation costs, so it is not possible to weigh costs against benefits. Research on these programmes in sub‐Saharan Africa in particular should be prioritised, as this would contribute to the understanding of the role that support to small businesses may play in development processes there. Abstract BACKGROUND AND OBJECTIVES OF THE REVIEW Business support interventions in low and middle‐income countries (LMICs) direct a large amount of resources to SMEs, with the assumption that institutional constraints impede small and medium‐sized enterprises (SMEs) from generating profits and employment at the firm level, which in turn is thought to impede economic growth and poverty reduction. Yet despite this abundance of resources, very little is known about the impact of such interventions. To address this gap, this systematic review analyses evaluations of SME support services in LMICs to help inform policy debates pertaining to SMEs and business support services. This review examines the available evidence on the effects of SME support services in LMICs on firm‐level performance indicators (such as revenues, profits, and productivity), employment generation, and labour productivity. METHODS We systematically searched for available literature. To identify relevant papers for this review, we conducted electronic searches on key platforms; snowball sampling of references from relevant papers and book chapters, and suggestions from recognized experts in the field. We focused on LMICs as defined by the World Bank classifications, and on evidence published since the year 2000, so as to include more sophisticated evaluation techniques. The references retrieved for this review are up‐to‐date as of December 2014. We included studies that evaluated the effectiveness of business support services on firm level outcomes of SMEs in low‐ and middle‐income countries. We defined SMEs as firms with between two and 250 employees, but also included studies that used annual revenue to classify firms as SMEs instead of employee count. We examined interventions involving tax simplification, exports and access to external markets; support for innovation policies; support to local production systems; training and technical assistance, and SME financing and credit guarantee programmes. We looked at studies documenting the impact of any business support service on SMEs when compared with business as usual. We included studies that report at least one final outcome of interest (such as higher profits, employment generation, and productivity). We incorporated studies that use experimental and quasi‐experimental methods, and other studies purporting to control for selection bias and endogeneity in selection into the programme. The search results were screened by two review researchers, and the included studies were similarly coded by two researchers. This double‐review process was designed to make the selection procedure and coding more rigorous and to screen for mistakes. We coded the data according to the impacts and characteristics of the studies selected. Standardised mean difference was used to code continuous variable outcomes and risk ratios to code binary variables outcomes. Effect sizes were synthesised and summarised to one effect size per outcome per study. Given the heterogeneity of true effects, we used analyses of random effects models to estimate overall average standardised effects. Moderator analysis was conducted with four additional variables. RESULTS The initial search returned 9,475 studies, which after dropping duplicates and applying the selection criteria were reduced to a final sample of 40 studies. These consisted of 37 papers (23 peer reviewed and 20 working papers), and 6 book chapters. All were produced between 2003 and 2014. Four of these studies could not be included in the meta‐analysis as incomplete information prevented us from computing standardised measures. The review reports 242 effect sizes (ES), and the meta‐analysis is based on 72 ES; 64 continuous and eight binary outcomes. Overall, our findings indicate that: Business support to SMEs improves firms' performance (average ES of 0.13 standard deviations (SD) and confidence interval (CI) (0.06, 0.20)), helps create jobs (average ES of 0.15 SD and CI (0.08, 0.22)), has a positive effect on labour productivity (average ES of 0.11 SD and CI (0.08, 0.15)), on exports (average ES of 0.04 and CI (0.01, 0.06)) and on firms' investment (average ES of 0.13 SD and CI (0.02, 0.24)). Evidence on their effects on innovation by SMEs is less clear (average ES of 0.05 SD and CI (‐0.01, 0.12). When the analysis is disaggregated by type of intervention, we find that matching grants continue to show a positive impact on firms' performance and employment of similar magnitude and precision once we exclude some outliers. Excluding the outliers, the average ES for these two outcomes are 0.15 SD (with CI (0.08, 0.22)) and 0.14 SD (with CI (0.03, 0.24)) respectively. Even though they are based on only few studies, results from meta‐regression indicate that technical assistance programmes have some positive effects on firms' performance, jobs creation and labour productivity, whereas tax simplification programmes seem to improve firm performance and generate jobs. Export promotion and innovation programmes seem to positively affect exports and innovation respectively, but do not seem to have an effect firm performance and employment creation outcomes. The average ES are extremely low and very imprecisely estimated. IMPLICATIONS FOR POLICY AND RESEARCH Our findings suggest that, overall, SME support has a positive impact on firm performance indicators. The results of our review should not be interpreted as clear evidence of SME support effectiveness, however, as the meta‐analysis was unable to provide results for all types of interventions or for specific countries. There was also significant risk of bias in many studies. Most of the studies found relate to Latin America, and thus cannot be interpreted as being applicable to other regions, including Africa. We recommend further analysis of cost‐effectiveness, as most studies do not indicate the cost of implementation. There remains a paucity of rigorous evaluation studies on SME support programmes in Africa, and Sub‐Saharan Africa in particular. Therefore, the generation of more evidence for the African context is paramount to the improved understanding of the role SME support programmes might play in the development process.

While plans to develop “smart cities” are gathering pace across the world, we know little about the ways in which the discourses of datafication, smartness, and big data play out in material contexts of urban development, including utility and resource management. In this paper, we explore this intersection in the case of Bangalore’s water supply, where IBM in alliance with the Bangalore Water Supply and Sewerage Board (BWSSB) is implementing a water-flow sensor network and geographic database system under the label of “big data for water supply.” We illustrate how the BWSSB-IBM approach narrows down the complex field of water provision to a question of water in- and out-flow measurements and the monitoring of BWSSB ground personnel. In theoretical terms, we discuss the ways in which these processes constitute both particular claims to knowledge, and the redefinition of citizenship as consumption.

This chapter draws together the conclusions of the book as a whole, namely that the available typologies of group privacy, such as collective action lawsuits or the articulation of the rights of political or activist groups, are intuitively insufficient to address the landscape emerging from the new data analytic technologies. The book has demonstrated that there are multiple and often divergent perspectives on what a group is and how it should be addressed with regard to privacy, but this divergence is also an important tool for understanding which elements of the problem can be addressed using current legal and conceptual tools and which will require new approaches. The chapter outlines the authors’ contributions to the typology of group privacy concerns, then identifies the gaps and limitations that arise from a group perspective on privacy, and the conceptual and practical implications of taking the group level into account. Finally, it suggests ways forward for future research, discussion and action.

The introduction outlines the origin of the book in the concerns arising from new data analytical technologies with regard to collectives. Although their effects have so far been addressed only on the individual level, in fact profiling and machine learning technologies are directed at the group level and are used to formulate types, not tokens – they work to scale, and enable their users to target the collective as much as the individual. This means that our legal, philosophical and analytic attention to the individual may need to be adjusted, and possibly extended, in order to pay attention to the actual technological landscape unfolding before us. This book represents such an adjustment: it may be seen as an exploration of the territory that lies between ‘their privacy’ and ‘its privacy’, with regard to a given group. In this book we push the boundary towards ‘its’, and begin to think about the implications of that shift, first by identifying who must be involved in the discussion. This chapter outlines the philosophical and legal concerns that arise from considering group privacy, and sets out the challenge that this volume aims to answer.

Zero-rating has become a perfect storm incorporating politics, profit, and technical complexity. The claim that everyone has a right to Internet connectivity is coming into conflict with the argument for net neutrality: will the provision of free data services via mobile phones worldwide privilege a small minority of content providers such as Facebook and Google, or will it provide valuable access to information and services for low-income people worldwide? This dichotomy, however, is based on a reductivist perspective that doesn't take into account the possible disadvantages of a reliance on mobile connectivity for a majority of the world's citizens. If we add geographical and economic context into the debate, it becomes apparent that zero-rating could benefit some, but could also impoverish and further disadvantage many. Who are the winners and losers from a global perspective, and what are their chances against the prevailing wisdom of Silicon Valley?

Questions of data and algorithmic analysis saturated the conference at which Dalton and Thatcher first called for a Critical Data Studies (Dalton and Thatcher, 2014a). Soon thereafter, Taylor published a response to their original call (Dalton and Thatcher, 2014b) that focused on the spatial nature of data and the need to critically contextualize it (Taylor, 2015a). This was one of multiple beginnings of Critical Data Studies, as it will always be three words cobbled together imperfectly signifying diverse sets of work around data’s recursive relationship to society (c.f. Graham 2014; Kitchin 2014, and elsewhere). We highlight that moment not to claim the concept, but to contextualize the focus of this co-interview. When Taylor published their response to Dalton and Thatcher, she focused on the specifically spatial nature of much data, not only in the content of said data, but also in the need to contextualize from where it originates and is put to use. Data varies across space in a variety of ways: from those included or excluded to those who access said data for analysis, manipulation and (re)presentation.Critical Data Studies calls attention to subject formation within these data regimes, for a critical examination of where the interpellation of the individual emerges in algorithmic culture (Striphas, 2015) and, through that, where the cracks and seams, the spaces for resistance and alternatives, might be found. When you append “critical” to a field of study, you run the risk of both offending other researchers, who rightly point out that all research is broadly critical and of bifurcating those who use critical theory from those who engage in rigorous empirical research. Kate Crawford recently observed that the meaning of Critical Data Studies is as political as the data it engages (Crawford, 2015). To extend that, it must remain contestable in order to contest the creation, commodification, analysis, and application of data. Ultimately, Critical Data Studies must make space for the recursive dialogue between the deeply theoretical and the robustly empiric and, in so doing, avoid the hubris of pseudopositivism and technological determinism, in favor of the nuanced and contingent.This is a dialogue between Dalton, Thatcher, and Taylor on the spatial nature of data with respect to Critical Data Studies. It highlights the historical variability of the processes of data production and accumulation and how this, in turn, has resulted in the uneven development of data. We ask both what is missing and what must be brought to the analysis of data in order to respond to the existence of particular dataspheres governed by the kinds of technology available in different locations. In form, Dalton and Thatcher ask the first question and Taylor responds before asking the second, alternating from there. As such, this dialog has many entry and exit points and is not meant as a definitive statement of what Critical Data Studies is or who can speak for it. Rather, we choose to live in the unresolved tensions between researcher and subject, technology and society, space and time (Haraway, 1991), and we encourage our readers to do the same.

Around the world, regimes of identification regulate people’s interactions with state and commercial institutions. These regimes promise access to resources and entitlements, while also facilitating people’s visibility to states and therefore their governability. For many, proving one’s identity presents no challenge; however, it is estimated that a billion people have no official proof of identity. Meanwhile, the humanitarian sector is undergoing a transformation in which digital identity, mobile connectivity and digital finance are central features. Through a data justice lens, this paper explores customer identification regimes in two country contexts in which large displaced populations are present: Uganda and Bangladesh. The two cases reveal divergent approaches to regulating refugee identification: while Uganda's policy environment has recently become more inclusive, Bangladesh's proves to be particularly restrictive. We reflect on what these cases mean for the future development of digital identity systems by the humanitarian sector and the implications for data justice.

In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.