AO: Green produces a particularly strong meta analysis characterizing the discourse related to indigenous knowledge and science in South Africa.
AO: Foster’s analysis is strong at the eco, macro, and nano scales (see her outline and arguments on page 11). She is particularly strong at revealing how the unpredictability of the hoodia plant complicated different claims for knowing and cultivating it (e.g. difficulty of growing it by cultivators, the fact that it did not actually reduce weight as it was originally thought to for commercial prospects, etc.) She also focuses on questions of belonging, looking at growers, scientists, and San peoples negotiations over hoodia and how these are structured by inequality. For example, subjects can be both empowered and disempowered (where scientists acknowledge Hoodia plants and San peoples on their websites but continue to present them as mere sources of raw materials) (101). Foster also dedicates more time than most reflecting on her own research practices and ethical responsibilities.
Lesley Green's concept of “relational ontology” (2012: 6) points out a discursive risk in Foster's work, namely a reliance on stable occupational and identity categories to describe human actors (the categories of "San peoples," "Hoodia growers," and "CSIR researchers" appear to hold stable throughout).
AO: Breckenridge has a strong meta analysis and frames his own project in the gaps between the existing dominant explanations about South African history. He draws on historical material to argue that South Africa is the strongest example of a centralized biometric state, surprising considering its place in the “global South.” He has little in way of explanation of his own data practices and does not use an ecological perspective in his analysis.
AO: Interestingly, von Schnitzler merges a macro and techno analysis to argue that technology itself becomes a political terrain for the negotiation of moral-political questions about limits, entitlements and obligations of citizenship in SA (671). She does not touch on data or data infrastructures per say nor heavily looks at ecological conditions.
AO: (August 28, 2018): During Natasha Vally's keynote panel at the 4S pre-conference workshop on STS in Africa, she cautioned against romaticizing "breaking" and not to celebrate the breaking. I think this highlights perhaps a discursive risk in this piece.
AO: Coban has a strong analysis of the meta discourses, especially focusing on how “Made in Africa, for Africa” narratives have characterized the tech space in Nairobi. She is missing a strong macro analysis and without historicizing “hacking” (by another name), falls into the same discourse touted by the sector (that “Engine” was the start of hardware hacking in Kenya; this is not true and fails to account for the public universities, e.g. Technical University of Kenya).
AO: The authors think about “capacity” in terms of relational, “arising not only in negotiations and trans- actions between African and non-African experts, but also in materiality, in time, in technopolitics and geopolitics, in the cohesion and dissolution of collectives, in points of contact between labor and dreams.”
AO: They argue that the capacity to dream is an important aspect of “capacity” which anticipates change and moves towards different, improved futures. They hold that form of capacity must be “appreciated, maintained ‒ and built in order to energize medical and scientific activity – as political and social action – for improved health.” (354). I appreciate this perspective of capacity but also note that concepts of “techutopias” are already heavily used within tech circles in Africa (which are heavily caught up in “Africa Rising” narratives that perhaps the health and care sector is not. See a lot of the work related to “dreaming up the future” of Africa (via tech). I think there needs to also be further nuance to these “dreams” to denote where many of these imaginaries are stemming from (e.g. Wakanda??).
AO: The authors are strong in their macro and nano descriptions of the notions of capacity and historicize the concept.
AO: Okeke’s work contrasts with some of the other work on STS in Africa because of her knowledge as a practicing pharmacist and microbiologist in Nigeria. She notes her research interest started when a paper she wrote about bacteria causing diarrhea was critiqued for its outdated and imprecise “standard” methods. In searching for a response, Okeke realized the sheer and overwhelming scope of the diagnostic gaps across Africa (xi). She is careful to write: “I hope I do not convey the impression that there are no diagnostic facilities in Africa.” She notes: “It is important to emphasize that the scale and scope of the problem described in this book is heterogeneous. … The argument I try to make here is that every African patient should have appropriate diagnostic access and most currently do not.” (xii). Based on her subject position and experiences, she is thus able to put together a nuanced but compelling call for greater intervention in the development of lab capacity on the continent. She is especially focused on techno and macro levels of analysis.
AO: Tichenor illustrates how the practices of certainty and exact “data” do not align with the differential meanings and spaces of understanding “malaria.” Despite the genre of the journal article, she is able to touch on nearly all levels of analysis. Her techno analysis and macro analysis are especially strong and she shows the interlinking between the growing global political will and focus on eradicating the “health problem of malaria” (with success being tracked via quantitative metrics) with the need for performativity of data at the local level.
AO: Tichenor is explicit in saying that malaria and all health problems in Senegal are in fact labor and infrastructural problems. She notes how difficult it is to disrupt the vision that global health problems can be addressed by the enumeration of short-term technological fixes.
Analysts underlying assumption seems to be that research data and work *should* be connected (for better science?) but because of these barriers (named in paper), they are not being connected or used as much as they should or could be. (“These costs, however small in comparison to research budgets, inhibited many of the research participants from engaging with these different platforms – thus missing many opportunities to profile and connect their research to that of others” (42).
Bezuidenhout et al. are strongest on their techno and macro levels of analysis but their nano and micro levels are wanting as it is hard to get a sense of who they were engaging (students? employees? faculty? administrators?) and also what exactly was involved in the “data engagement” that was being discussed. The concept of “data” was used in different valances (by them as well as the interlocutors) and that was not noted or analyzed. I think their use of “data” is largely to mean research outputs? But there is a conflation between open access and open data which are not the same thing. Authors also are conflating Open Data with Open Science which are also not the same thing. I would argue that this is more the “Open Data” movement rather than the maxim of OS (“OS understands democratizing science as increasing the amount of data available” p. 45).
There is also a missing aspect which is the contribution BACK into the online data platforms. Access appears to be a one-sided “use” of outputs of data but the paper doesn’t discuss the importance of circulation (with creation by / from African scholars as well).
There seems to be an underlying assumption that data sharing should be designed to be at a global scale: “clearly what is necessary is a global, coordinated, inter-disciplinary and multi-focused discussion on how to pull these diverse aspects together into a coherent approach.” (45) My own opinion is that data sharing can be valuable even within a very micro-research community, esp. if there is the ability to gain access to it or if it is archived and managed in a way that has the potential for it to be available to others in the future. I do not think data sharing needs to be automatically at a global scale. In fact, that is what is currently being done and it is problematically centralized by private Western players so I don’t think that is the solution.
I also question the assumption that because there are more people on the continent using their mobile phone that data sharing capabilities need to be built for the mobile phone (“The dominance of cellular phones on the African continent – in comparison to other ICTs – and their increasingly effective use in a wide range of financial and health-related applications should provide a focal point for OS initiatives on the continent – too many websites critical for data engagement are highly cumbersome for use on mobile devices”, pg 45). I think the question is *who* the data is for. Is it for lab based researchers? Are lab based researchers all using laptops at work? What functionalities are available on the laptop vs the mobile phone? These are the very “user-specific” contextual factors that should be considered. Who/when/where/why are people using their phones and who/when/where/why are African scientists using data sharing platforms?
Mavhunga is particularly strong in his techno and eco level of analysis, focusing on the historical materiality of poaching and hunting technologies. He focuses less on the meso (organizational) level.
Mavhunga pushes back against the humanitarian desire to help Africa (leveraging postcolonial scholarship) to highlight African agency and the creative capacity of Africans innovating everyday. By doing so, he seeks to bring about a change in perceptions about the continent as helpless and in need of assistance and rather as creative and desiring of partnership/business (11).