deutero [reflective/learning capacity]: How are people and organizations denoting and worrying about the phenomena you study?
People and organizers are responding to wild and built environment fires by addressing their root causes, creating policy around management and containment, and creating resources and information for public dissemination going forward.
meta [dominant discourses]: What discourses constitute and circulate around the phenomena you study? Where are there discursive risks and gaps?
Discourses: Indigenous environmental management systems, institutional fire ecology, climate change, climate change denial, infrastructure and population density, environmental policy perspectives on fire supression
Risks and gaps: Lack of integration between divergent and parallel discursive regimes
macro [law, political economy]: What laws and economies undergird and shape the power the phenomena you study?
This varies according to country and place, but colonial nations like US, Canada and Australia have practiced fire suppression for about 100 years.
meso [organizations]: What organizations are implicated in the phenomena you study? What geopolitics are in play?
Organizations: State and national environmental management groups, national and state parks, land management organizations, community organizations, housing organizations.
bio [bodies]: What are the bodily effects of the phenomena you study?
Smoke inhalation, destruction of housing, potential injuries and fatalies.
micro [practices]: What (labor, reproductive, communicative) practices constitute and are animated by the phenomena you study?
Farming, forestry, infrastructure, cultural management of lands
nano [language, subjectivity]: What kinds of subjects are produced by and imbricated in the phenomena you study?
Displaced individuals and communities, fire ecologists and scientists, policymakers
edxo [education and expertise]: What modes of expertise and education are imbricated in the phenomena you study?
Ecology, economics, chemistry, metereology
data [data infrastructure]: What data, infrastructure, analytic and visualization capabilities account for and animate the phenomena you study?
Maps (mostly that's it)
techno [roads, transport]: What technical conditions produce and delimit the phenomena you study?
For some, like in Italy, inability to get close to the source of the phenomenon
eco-atmo [ecology, climate]: What ecological and climatic conditions situate the phenomena you study?
Climate change, dry weather, "fire season" etc.
geo [earth systems]: What geological formations, contaminations, resources and scarcities ground the phenomena?
Wildfires, combustion etc.
deutero [reflective/learning capacity]: How are people and organizations denoting and worrying about the phenomena you study?
Individuals/citizens are concerned and spreading information about which masks to wear, what are masks effective in combating COVID-19, are masks even effective, why wear a mask, their rights in not wearing a mask, how masks are a ploy in kidnapping children, what information about masks is effective, and many more questions around the existence of masks during the pandemic. Within this broad category, this could be specified more by identifying different types of citizens (e.g. teachers, essential workers, immunity deficient individuals, etc.)
Local and federal government officials and agencies are concerned about the above but also about the implementation (or not) of mask wearing. How to share true facts about masks, and how to use masks in order to make a safer nation.
Researchers and scholars are similar to the above two concerns, but they can look at these mask issues in a more granular and/or conceptual lens such as how to actually scale the production of masks, proving the efficacies of specific mask types, and how masks have altered society’s way of life.
meta [dominant discourses]: What discourses constitute and circulate around the phenomena you study? Where are there discursive risks and gaps?
macro [law, political economy]: What laws and economies undergird and shape the power the phenomena you study?
Local and federal mandates to enforce mask-wearing or controlling the “article of clothing” that citizens are required to wear in order to conduct their daily personal
meso [organizations]: What organizations are implicated in the phenomena you study? What geopolitics are in play?
Local and federal government agencies, essential institutions (e.g. hospitals, food service, etc.), businesses/business owners and workers.
On a larger scale, this influences international affairs in travel and business, which affects the political relationships between citizens and government agencies between borders. Travel was ubiquitous prior to the pandemic and now that there is segregation due to the fear of virus spread, this shuts down opportunity, communication, and more that many people and organizations have been accustomed to in order to continue their standard expectations.
bio [bodies]: What are the bodily effects of the phenomena you study?
All parts of the human and animal biology that is affected by coronavirus directly or indirectly. That is, physical and mental.
micro [practices]: What (labor, reproductive, communicative) practices constitute and are animated by the phenomena you study?
All labor carried out by essential workers. This includes but is not limited to postal service, retail, food supply and service, medical care, the divide between those employed and having been able to build the skills to work remotely or not.
nano [language, subjectivity]: What kinds of subjects are produced by and imbricated in the phenomena you study?
Unsure about this one. Wil need to develop the research more in order to address this.
edxo [education and expertise]: What modes of expertise and education are imbricated in the phenomena you study?
data [data infrastructure]: What data, infrastructure, analytic and visualization capabilities account for and animate the phenomena you study?
techno [roads, transport]: What technical conditions produce and delimit the phenomena you study?
The reliance of proprietary social media platforms in order to spread information about the use, efficacy, need, or fight against masks in order to address the pandemic.
eco-atmo [ecology, climate]: What ecological and climatic conditions situate the phenomena you study?
Besides the virus as a biological condition, there are unproven theories that the cause of the virus and the spread are due to ecological and climatic areas of understanding.
geo [earth systems]: What geological formations, contaminations, resources and scarcities ground the phenomena?
The basic resources that construct masks, not just the mask object itself, but all other resources that are included in the supply and demand life cycle of masks.
Just realized my responses weren't saved in the artifact, very sorry! Please see them now...
I study scientific (and otherwise) knowledge production around sugarcane-based bioproducts in Brazil. Renewable fuels and materials are concerns for many environmental groups as well as oil and gas companies; the former are wary of sustainability issues and the latter are concerned with making renewables a profitable element of their product portfolio. However, sugar-based petrochemical replacements are often invisible to lay consumers--they don't know that their plastic bottle was made of sugar instead of oil, for example--and this invisibility is sometimes a goal of manufacturers who want a seamless sugar substitute.
One dominant discourse is that climate change and a growing world population require sustainable methods of producing more of the materials that our lives depend on. My interlocutors frame sugarcane--engineered to be even more productive than normal and require less land for cultivation--as a fitting solution to this. However, this assumes, among other things, a continuation of certain consumption practices, rather than any kind of reform of these.
There is a new national biofuels policy in Brazil called RenovaBio that includes national biofuels targets, certification processes for sugar/biofuels manfucaturing plants, and a carbon credit system. This policy unfolds in part through spreadsheets that sugarcane growers fill out with the characteristics of their land and crop, in order to calculate life cycle analyses and other metrics. I came across a university online course that growers could take to learn the ins and outs of this bureaucratic process; thus access to certain knowledges and skills seems to shape how RenovaBio plays out for certain sugarcane growers. In terms of economics, the global markets for refined sugar and ethanol are very important for the Brazilian sucro-energy sector, because producers can choose to make sugar or biofuels throughout the harvest based on the market prices. Sugarcane consulting companies send out daily newsletters with the New York sugar prices at the very top. Scientists seem less concerned with NY sugar prices, but are wrapped up in their own scientific knowledge economies of circulating publications, access to reagents, etc.
There are several large and powerful union-like groups/cooperatives in the sugarcane industry in Brazil that represent growers' interests; many sugarcane consulting companies hired by growers to improve their production and bottom line; scientific organizations for sugarcane and bioenergy in Brazil specifically; the universities and private and semi-public research institutions that scientists work at; various ministries of the Brazilian government that regulate sugarcane/agriculture, biofuels/energy, and science/technology; and biotech companies trying to translate scientific knowledge about sugarcane into profitable technologies (both Brazilian and foreign). I think about geopolitics when interlocutors describe how foreign biotech companies open operations in Brazil to be close to the "cheap feedstock" but import their "own" knowledge/research from abroad.
Petrochemicals can cause many kinds of toxity to human and nonhuman bodies; some sugar-based petrochemical replacements try to change this, but others only aim to be seamless substitutes and thus carry forward these toxic relations (e.g., non-biodegradable sugar-based plastic that's chemically identical to petro-based plastic). There are effects on the sugarcane plant "body" to be considered--scientists are engineering it in unprecedented ways that contest conventional understandings of growth and productivity (see my 4s presentation). There are also the bodies of the sugarcane growers and industry actors, who are affected by shifting labor practices around sugarcane harvesting. And relevant as well are the bodies of people who live near cane fields; with mechanized harvesting today this is less an explicit issue, but historically cane fields were burned before harvest and the resulting volatile organic compounds caused health issues for nearby dwellers.
The phenomena I study is driven in part by the practices of scientists--their labor in the lab, their (re)production of scientific knowledge, their involvement in coordinating and reviewing government research grants. It's also driven by the labor of sugarcane growers and industry actors--driving the machines that harvest the cane, driving the trucks to transport the cane, operating the processing plant. There are also practices within and around sugarcane consulting, academic and industry conferences, and government policies/regulations that seem to animate new social worlds of sugar(cane).
Often scientists, sugarcane growers, and workers in the processing plant are treated as if they exist in completely separate realms; for example, one sugarcane company had plant workers do a virtual reality simulation of driving a harvesting machine so they'd better understand that part of the production chain. I'd like to think more about how this separation between science, agriculture, and industrial processing is (re)produced and the effect this has on subjectivities.
There is the expertise of academic scientific knowledge about sugarcane, which is sometimes framed in contrast to more practical understandings of how growing sugarcane "really works" by those out in the fields. There is also a lot of consulting knowledge produced around sugarcane, which focuses on maximizing production costs and managing volatile markets.
There is scientific data that doesn't seem to leave the lab, biotech data that is more explicitly linked to certain technologies, anecdotal or individual data from cane growers and producers that often draws on very technical and scientific metrics and understandings, and so much market data about sugar and ethanol. Scientists, growers, and industry actors have different ways of conceptually breaking down cane (see the other exercise I did for this workshop on Found Visualizations). In that other exercise I also talk about some of the visualization practices relevant to my research.
Transportation infrastructure like roads is key to how the sugarcane sector works (sugarcane has to be processed very soon after harvesting, so it's immediately transported to a nearby mill). Today sugarcane is mostly harvested with machines, and a lot of work goes into maintaining the fleet. Within the lab, research depends on instruments working, protocols going as planned, reagents arriving on time (having to go through customs, etc.), grant money coming through, global pandemics not shutting down in-person activities.
Rainfall has a large influence on sugarcane production, and rain patterns are changing with climate change. Some of my interlocutors have done research on the effects of increased CO2 concentration in the atmosphere on sugarcane growth too (it actually accelerates it). In the lab, air conditioning is necessary to maintain a proper/consistent temperature for experiments. Refrigeration and freezing is necessary for some reagents and samples, or to slow down protocols/processes. In the new sugarcane harvesting machines, the cabs are air conditioned to make it more comfortable for the operators. And again, the discourse of climate change is what positions sugar-based renewable materials as important and urgent.
Oil/petro-cultures and general extractivism seems to shape approaches to extracting sugar from sugarcane in new ways with biotechnology.
deutero [reflective/learning capacity]: How are people and organizations denoting and worrying about the phenomena you study?
The phenomenon, open science, has incited varied reactions. Advocates discuss open science as important for scientific progress on a number of grounds (e.g. quality, speed, fiscal efficiency, inclusivity of research). Some organizations (e.g. journals, funders, research communities) build technical systems that they believe to (at least appear to) enable open science. Some advocates act with zeal that estranges other researchers. Thus, some individual researchers have distaste for open science advocates that they see as police. Unclear how university departments react.
meta [dominant discourses]: What discourses constitute and circulate around the phenomena you study? Where are there discursive risks and gaps?
Open science advocates have created (?) a social discourse among scientists (e.g. by publishing perspectives pieces or on twitter) about open science--they often invoke Mertonian norms when justifying open science. The empirical research on open science often catalogs progress toward open science goals (e.g. surveys on data sharing behavior), though some has also been revealing individual scientists' concerns that the movement requests work of them that goes unrewarded. Open science is often discussed in relation to the knowledge commons.
macro [law, political economy]: What laws and economies undergird and shape the power the phenomena you study?
Open science relates to global and local funding priorities as well as the economics of science communication and publishing. A political turn toward open government also seems to be ongoing; open government and open science are intertwined. Furthermore, open science relates to the commodification of knowledge and the labor structures and organizations that produce and share knowledge—often for career benefits. Open science assumes knowledge to be a public good used and produced by scientists.
meso [organizations]: What organizations are implicated in the phenomena you study? What geopolitics are in play?
Government and private funders often hold an open science policy of some sort. Some funders have created coalitions for international or inflated influence over scientists' behavior. Disciplinary organizations, research labs, and coalitions take stances or attempt to influence the success of open science or they may simply demonstrate open practices (or not). Academic science as a whole may be viewed as an organization.
bio [bodies]: What are the bodily effects of the phenomena you study?
Though not the focus of my current research, changing research practices could change the bodily state of anyone involved in the research process. Also, open science might share data about research participants—often, this is health data, which raises questions of privacy and security.
micro [practices]: What (labor, reproductive, communicative) practices constitute and are animated by the phenomena you study?
Open science fundamentally about changing labor and communication practives. All research work could be perceived as interacting with open science as the actor has, in theory, a choice about what to share and with whom and how. Interacting with technology to conduct research is of particular interest to me—How do scientists' use of tools enable or discourage open practices?
nano [language, subjectivity]: What kinds of subjects are produced by and imbricated in the phenomena you study?
I am honestly not sure how to answer this question and no one else's responses to it have clarified it for me.
edxo [education and expertise]: What modes of expertise and education are imbricated in the phenomena you study?
Some open science advocates seem to believe it can upset the traditional path to legitimacy that influential researchers take, though how unorthodox a person can be and still be recognized as a valid contributor to scholarly communication is debatable (consider, for instance, citizen science). However, mertonian norms are often invoked by open science advocates as the backbone of (open) science philosophy, and this assumption of normativity could be construed as also reifying the ivory tower.
data [data infrastructure]: What data, infrastructure, analytic and visualization capabilities account for and animate the phenomena you study?
I work with qualitative data gathered online or through interview and plan to use observation, survey, and trace data in my dissertation. I will structure my analysis using thematic analysis techniques, sensitized by structuration theory and value sensitive design.
techno [roads, transport]: What technical conditions produce and delimit the phenomena you study?
Scientists gather and share data through complex pipelines of information and personal transportation. Throughout my career I hope to consider the many different junctures these pipelines contain. For my dissertation I plan to examine the use of a web app meant to be used as a cross between a personal homepage and a lab notebook—the ways that this app produces and delimits users' research work is exactly what I hope to study.
eco-atmo [ecology, climate]: What ecological and climatic conditions situate the phenomena you study?
Though not of particular interest to me at this time, the public and political distrust surrounding climate research is presumed by some stakeholders to be driving open science campaigns. The idea seems to be that by being transparent in research, climate change will be made undeniable.
geo [earth systems]: What geological formations, contaminations, resources and scarcities ground the phenomena?
There are physical resources that supply or enable our digital communication; there are physical resources and geographical sites that supply and house our research institutions. Some work on open science focuses on the nature of digital communication and how this enables asynchronous and non-collocated work; early writing on this focused especially on the move from print to digital journals.
In my dissertation, I am interested in how biotechnology is being ‘democratized’ in community science labs in the San Francisco Bay Area. Community science lab members (professional scientists, university students, DIY biologists, ‘biohackers’, interested amateurs) are generally thoughtful about what ‘democratization’ means within their spaces and communities, and how they can undertake initiatives to ‘democratize’ in other ways. However, whilst they aspire to ‘democratize’ biotechnology in creative, engaged ways, they are perhaps less critical about the project of ‘democratizing’ biotechnology more broadly, and generally think that doing so is necessarily a useful, worthwhile endeavour.
Discourses around citizen science and techno-optimism are especially prominent (and both take on a specific flavour inflected by Silicon Valley’s regional culture around innovation and entrepreneurship). More critical discourses which question the particular imaginaries about the relationship between science and society implicit in these discourses are less pronounced.
With respect to laws, there are several regulations that relate what sorts of projects and experiments can, and should, be conducted within community science labs. Additionally, the FBI has been a ‘partner’ of sorts with community science labs in the United States for about a decade. With respect to economies, these labs are heavily dependent on regional economies for donations of lab materials (e.g. from research labs, or unsuccessful start-ups). Some community labs members themselves also want to establish their own start-ups or create materials, tools, or technologies which circulate in these regional, and broader, economies.
There are several organizations involved, mostly non-profits who either help organize donations of lab materials to community science labs, or organizations which bring together community science labs at the global level.
Although there are some associated with DIY biology/biohacking who are eager to self-experiment, most community science lab members are wary or explicitly against this. Instead, most of the ‘bodily effects’ involved are related to the experiments which are conducted on micro-organisms (e.g. bacteria, yeast).
Community science lab members are generally engaged in conducting and communicating science and related project efforts to other group members and the public. They do so during community meetings, on social media, and via email. For community projects, the desire to ‘democratize’ and be as open as possible means that communication is generally transparent (although some information is withheld, e.g. specific experimental approaches which might be used by others in patents).
Efforts to ‘democratize’ biotechnology in these labs are generally aimed at producing scientifically literate subjects, although often this can manifest and implicitly depend on a ‘linear’ model of scientific communication, in which community science lab members are tasked with ‘informing’ newcomers of the benefits of these labs, and biotechnology more generally, in relatively straightforward, uncritical ways.
Community science labs are generally welcome to everyone interested in learning more about biology and science. As such, these communities are comprised of everyone from professional scientists eager to pursue their own projects outside of work, to eager people with no education backgrounds in biology. Many of the lab members have engineering backgrounds, and are interested in synthetic biology (i.e. the attempt to apply engineering principles to biological systems).
Community science lab members often rely on programs and platforms to help organize and collaborate on experiments together. There are also platforms (e.g. Mattermost) where updates on progress are shared with others.
Community science labs provide the ‘technical conditions’ which make biotechnology accessible to a broader range of people. This includes lab benches, devices, and materials to conduct biotechnological experiments. These labs also depend on technical infrastructures and services beyond these labs (e.g. DNA sequencing).
The ‘ecological’ conditions in my research relate to lab-based ecologies of micro-organisms used in experiments, and the potential for environmental contamination beyond the labs.
There are some possible geo-scale impacts related to community science labs (e.g. some community lab projects are environmentally focused).
deutero [reflective/learning capacity]: How are people and organizations denoting and worrying about the phenomena you study?
In 2013 we accumulated a total universe of digital data of 4.4 zetabytes, in 2020 this number has increased by 44 zetabytes, a tenfold increase. For many, it is clear that digital data and its manipulation and analysis through Big Data are key pieces in the future development of our society and our research. Thus, the Big Data is having implications in multiple orders, such as the individual, social, ethical-moral, scientific or economic.
In the last years multiple investigations have been developed in the field of critical studies of algorithms and also in relation to the experience and decision making of the users and their data. An example of this is the work that focuses on self-tracking devices. However, I have had more trouble finding bibliography that focus on the technicians who manipulate, care for and analyze these data. Likewise, in the social sciences the discourse of the epistemological revolution that Big Data represents is gaining ground, despite initial reticence.
Thus, it could be said in general that the subject is one that is valued and respected, that awakens the interest of many people. But it is also an uncomfortable subject, which puts in check the Big Data discourse of objectivity, neutrality and precision and tends to be ignored.
meta [dominant discourses]: What discourses constitute and circulate around the phenomena you study? Where are there discursive risks and gaps?
Considering the magnitude of the phenomenon, it seems reasonable to find multiple discourses that fight for hegemony in this field. On one hand, we could speak of the discourse of Big Data as an epistemological revolution, which is linked to thinking of Big Data as a cognitive device. This discourse defends that Big Data is going to suppose, in the medium-long term, a transformation in our way of thinking.
On the other hand, we find the discourse of Big Data as a product. The assertion that Big Data is 'the new oil' is famous, referring to the billionaire market that exists around it.
The discourse of Big Data as a transforming element of the social order is also relevant. The different imaginaries that exist about it already affect multiple elements of our lives, such as the way we socialize on the Internet, the use of self-tracking devices, personal safety, or social and citizen responsibility. An interesting example of this is the use of self-tracking applications that have emerged in relation to COVID-19.
This can also lead us to think about the legalistic discourse of digital personal data and the Big Data, a discourse that evaluates the ethical-moral character of the use of personal data, and which revolves around the concepts of property, privacy or consent.
Linked to this, we find discourses that approach the Big Data and the use of digital personal data from the concept of extractivism, and attend to the danger of being over-supervised, to a lack of personal freedom, to ignorance and lack of knowledge about the functioning of the Big Data and to the biases of the algorithms and, in general, the digital infrastructure of the Big Data.
macro [law, political economy]: What laws and economies undergird and shape the power the phenomena you study?
In the field of Big Data and personal digital data, it is not difficult to observe an extractivist mechanics. Big Data is a tool that requires many resources: large amounts of data, storage space, large amounts of energy, large numbers of technical workers, etc. The access to the Big Data product, as well as its management, generally falls on power figures: governments, big companies or multinationals, etc. This imbalance characterizes the economy of Big Data, where users are producers of raw information that do not know or are not aware of the mechanics of the process. A process that is left in the hands of a few and whose result benefits directly only a few. It is characteristic (and necessary for this situation to be maintained) a great lack of knowledge about the operation, location or characteristics of the process.
On the other hand, in this same field, data protection and privacy laws play a quite significant role, which tend to refer to the data as an object owned by an individual person.
meso [organizations]: What organizations are implicated in the phenomena you study? What geopolitics are in play?
Big Data involves organizations of all kinds, in which data circulates and acquires different values or meanings. Having the necessary resources to be able to handle and analyze Big Data, and even to have the results of those analyses, places us on a rather tense game board. We might say that the main organizations and actors are: companies, governments and civilian organizations. All these organization vary greatly in size, power or resources.
As organizations that see the data as a product we can find: companies that own data but do not have the resources to do something with it; companies that offer services of maintenance and analysis of data; companies that have the resources to both own data and analyze it; companies that sell their data to third parties. We can also find how most governments and administrations use their data to create new public policies, analyze the economical distribution, or design surveillance systems, for example. But we can also find research groups that use Big Data tools to keep, maintain, take care of and analyze their research data.
There are also organizations whose objective is not to use the data, but to protect it: Here, the role of national and supranational governments in determining what is legal or illegal in the use and acquisition of data should be taken into account. An example of this is the role of the European Union and its GDPR. We should also keep in mind the —mostly local— organizations of digital activists who fight against the extractivist logics of Big Data and defend personal integrity against the abuse of data mining.
There are also organizations that think of Big data not as a product but as a tool to solve social and environmental problematics, such as ‘Data for Good’ or ‘So Good Data’.
To all this we could add certain tensions that have recently appeared in the media and which have to do with the use and abuse of Big Data by large companies. A clear example of this is the case of Cambridge Analytica, but also the efforts of the United States to keep some companies of Chinese origin out of the country.
bio [bodies]: What are the bodily effects of the phenomena you study?
According to multiple works, this phenomenon is having several consequences on our bodies and the way we live. The most prevalent example is that of the self tracking devices, which has altered a good part of our habits and the way we attend to patterns or vital signs. For example, the appearance of intelligent watches has been a determining factor in our idea of 'a healthy life' and has altered our perception of the functioning of our body by allowing its visualization in graphs, or the accumulation, comparison and analysis with other data. It also allows us to share and make a social event of habits that previously went unnoticed or were part of our private life, such as ovulation cycles, sleep patterns or diets.
It is also a phenomenon that is clearly impacting the medical and research fields, allowing correlation relationships to be more easily established.
An example of both points is the COVID-19 apps.
micro [practices]: What (labor, reproductive, communicative) practices constitute and are animated by the phenomena you study?
This is a rather large and difficult question to answer in a short space. The phenomenon of Big Data is big enough to be constituted by or animate very diverse practices. Therefore, I am going to use this space to emphasize the practices that seem most relevant to my research, empirically located in a data laboratory of an international company (Telefonica); the first in Europe and the fifth worldwide.
The manipulation of digital data by technicians requires a series of work practices (they are salaried workers in a company), but it also allows us to talk about care practices. According to some authors, a large percentage of data scientists' working time is spent on tasks involving cleaning, storage and sorting data. That is, making sure that the data have the necessary characteristics to be grouped in a table and crossed later for analysis (i.e. that there are no erroneous or false data), but also worrying that, with use, the data remain useful and functional, that they continue to be accessible, that they can be repurposed, etc. These tasks show that data enters into cycles of repair, decay, construction, reconstruction or growth.
Also, as Pink et al. (2018) have said before, “[a] focus on how data is made and broken highlights the collaboration between people and infrastructures: people’s lives and work become entangled with data production (Berson, 2015). Such entanglements reveal themselves when we familiarize ourselves with data worlds. This familiarization can involve collaborations with the custodians of data, ‘geeks and quants’ (Bell, 2015: 25), or following how data becomes appropriated into the everyday, is valued and converted into forms of value (Fiore-Gartland and Neff, 2015; Ruckenstein, 2014).”.
nano [language, subjectivity]: What kinds of subjects are produced by and imbricated in the phenomena you study?
Subjects of this phenomenon are, broadly speaking: users (data producers), digital activists, NGOs, technicians (work force) and owners (of data, means of production and final product). But all of these categories are quite big. We should keep in mind the diversity and heterogeneity of these subjects.
When we talk about users, we talk about the general public that use or access digital platforms and, by doing so, produce digital data. I believe it is worth spending a line or two in pointing out how the legalist discourse has produced an idea of this public of users as separate individuals who own their data. This also indicates data as something that can be owned, and something that can be private and individual. This, I believe, is one of the keys for thinking of data. Some authors and activists have pointed out as naïve this way of thinking of data as something intrapersonal when most of our lives and our experiences in digital platforms consists on interpersonal relationships.
Also, I would like to point out to the invisibilization of most of the labor of technicians, which I believe I can state is blackboxed. As much as we have an algorithmic imaginary and are able to talk freely about algorithms, technicians tend to be ignored. Thus, it is ignored the part their role in categorizing, organizing, keeping, cleaning, etc. our data.
edxo [education and expertise]: What modes of expertise and education are imbricated in the phenomena you study?
I believe it wouldn’t be wrong to say that Big Data is quite an interdisciplinary phenomenon. This is even though most of the imaginaries of Big Data, and media’s discourse tend to link it to hardcore computation, coding and mathematics/statistics. It has been stated that, due to the interdisciplinarity that characterizes Big Data, it is quite difficult to form or educate experts in the field.
This kind of thought is linked to the idea that Big Data produces the most objective, neutral, precise information —as it has been stated of other new methods before, only to learn that no such a thing exists— and has to do with an emphasis on method. The whole technical process of dealing with digital data is full of decision-making, even when it seems like it is not. Categorization, cleaning, algorithm-design, algorithm-evaluation, data visualization and analysis, to name a few, are complex processes that are fully imbricated with conceptions and preconceptions of how the world/the society is and how it works. That is one of the main reasons why social sciences are absolutely relevant in this field.
data [data infrastructure]: What data, infrastructure, analytic and visualization capabilities account for and animate the phenomena you study?
I work primarily in qualitative observation and analysis, but I am currently learning some programming languages such as SQL or Python. Learning this programming languages allows me to better understand technicians and their work, as well as the logic behind their labor. I probably will have to work with data sets, documents and visualizations of data produced in the lab. Also, the group I want to study is quite active on social media, so I might be compelled to do virtual ethnography.
techno [roads, transport]: What technical conditions produce and delimit the phenomena you study?
As far as I know, the work of Big Data technicians is quite an individual job that is done always in the presence of one or multiple computers. Also, data is also usually located on the cloud. So, roads or transport don’t really limit Big Data—which can be considered one of its strengths.
But precisely because it needs powerful computers and supercomputers, internet usage, cloud access and storage, the interconnexion of thousands of devices, etc., energy consumption does definitely delimit and affect its existence. Data is captured, stored and analyzed digitally, and that usually means that it needs one or various servers that are not necessarily located next to the lab or the company. All this colossal infrastructure needs and consumes a lot of energy and resources. Big Data itself is a product of the development of computational capacities— which have been increasingly energy-consuming.
Also, because of the digital storage and access to data, one of the main concerns that limit this phenomenon is cybersecurity.
eco-atmo [ecology, climate]: What ecological and climatic conditions situate the phenomena you study?
I am not aware of any ecological or climatic conditions that situate the phenomenon of Big Data other than the stated in the next question.
Geo [earth systems]: What geological formations, contaminations, resources and scarcities ground the phenomena?
Big Data has a material infrastructure that is usually overlooked. It has been proved that this material infrastructure pollutes. As Pierson and Lefevre (2015) had pointed out before: “terminal equipment, networks, and data centers […] each consume similar amounts of electrical power, on the order of 40 gigawatts in 2013, which is equivalent of about forty nuclear units. This figure obviously has repercussions on the climate, even if this carbon footprint depends on the energy mix of the user country (34 grams of CO2 per kwh in France in February).”
deutero [reflective/learning capacity]: How are people and organizations denoting and worrying about the phenomena you study?
Academic researchers in IS, psychology: Generally I think online therapy ISN’T being framed or studied within the “platform labor” academic research community. I think that research about teletherapy more generally seems to be focused on the patient/client experience, and whether it changes the therapeutic effectiveness to do it online. It is also discussed as reducing stigma and increasing convenience for the patient, although this may be emphasized more by the teletherapy companies themselves rather than the research.
Public health organizations discuss telehealth as a tool for creating access to care where it otherwise isn’t available.
The therapy profession up until covid still seemed to think of teletherapy as an edge-case, as not clearly “legitimate” or “evidence-based”.
meta [dominant discourses]: What discourses constitute and circulate around the phenomena you study? Where are there discursive risks and gaps?
Discourses of “evidence based treatment”, of scalability of labor and access to mental health services, discourses of risk and prevention
Discursive gap: gig workers, platform laborers, women and mother workers
macro [law, political economy]: What laws and economies undergird and shape the power the phenomena you study?
Mental health regulations - state licensure is a huge barrier for mental health professionals to enter the practice and to maintain it as they move around the country
Managed care - Very difficult for therapists to get reimbursed by insurance companies, often reimbursements are quite low and have a high requirement in terms of justifying treatment. Means that many therapists don’t accept insurance if they can help it. Also means that accepting insurance guarantees patients/referral stream for the therapists since there are so few options for those looking for in-network providers.
ALSO, insurance-based treatment venues (e.g. platforms that work w/ insurance companies, community mental health centers getting medicare/medicaid reimbursements) fundamentally require different approaches, different work (more outcome-oriented), than non-insurance-based treatment (e.g. private practice without insurance, DTC platforms).
“Economies of scale” - therapy is a very human-labor intensive work (not very scalable) but is in very high demand and has a huge market, so there is reason to figure out scalability in order to tap into and profit off this huge market/demand
meso [organizations]: What organizations are implicated in the phenomena you study? What geopolitics are in play?
Platform companies & startup tech culture (very US-based)
Massive insurance companies, the economies and incentives of a private, profit-driven insurance landscape
Employers looking to provide premium mental health services to employees
The US state that minimally funds public mental health services
bio [bodies]: What are the bodily effects of the phenomena you study?
The stress and fatigue of emotional labor of therapists
The stress and fatigue of patients suffering from mental health issues while also trying to navigate an inaccessible mental health system
Therapists providing care for young children from their home while also providing teletherapy (see: therapist who described nursing her child below the camera while doing teletherapy)
Separation from the clinic, the office, from colleagues when doing teletherapy
Ability for therapists to see into their patient’s homes
The difficulty of helping patients in distress remotely (E.g. can’t walk them to an inpatient clinic, can’t hand them a box of tissues)
micro [practices]: What (labor, reproductive, communicative) practices constitute and are animated by the phenomena you study?
Practices of time-management and time-autonomy: managing time across multiple teletherapy platforms (using a master calendar, using one platform to fill last minute cancellations from another platform); managing caseload on platforms (different settings and options with each platform); setting time-boundaries with clients verbally on platforms that promise on-demandness; balancing childcare and reproductive labor responsibilities; giving time for emotional processing, self-care time in between sessions. Contrast with the lack of time-autonomy in community mental health clinics, in private-practice when you need to drive to a physical office to see local patients.
Practices of virtual community-building - using facebook groups and forums as a way to figure out how to build your career and your practice as a therapist.
Emotional labor; extreme burnout with high-risk, high-needs clients who are seen in low-resourced settings, although also comes with high degrees of satisfaction in terms of “making an impact”; emotional labor associated with ongoing connection and contact in-between sessions that some platforms offer; going to therapy themselves or reaching out to supervisors as a way to help manage and process emotions.
nano [language, subjectivity]: What kinds of subjects are produced by and imbricated in the phenomena you study?
The “working well” or the “worried well” being serviced by platforms and private practice, in contrast to “high-risk” “high-needs” populations being serviced at agencies/community mental health centers
The “Risky” patient constructed by platform screening surveys and questionnaires, those who are Not appropriate for telehealth (contested, grey areas that differ depending on the therapist’s risk tolerance)
Therapists as “entrepreneurs”, creating their own private practices in order to escape the pressures of community mental health. (Versus...therapists as wage laborers, “trading hours for dollars”, as gig workers for platforms)
edxo [education and expertise]: What modes of expertise and education are imbricated in the phenomena you study?
Entrepreneurial expertise (self-promotion, creating a workable business model across multiple streams of income)
Platform expertise
Insurance and reimbursement expertise
Mental health expertise (different niches/working with different populations)
Technical expertise
data [data infrastructure]: What data, infrastructure, analytic and visualization capabilities account for and animate the phenomena you study?
Managed care a very data-driven tightly run machine, in which insurance companies are always working the margins to reduce costs, reduce utilization, while maximizing outcomes (e.g. maximizing “healthiness”). In mental health, particularly tricky to measure “healthiness”.
Therapy entrepreneurs as bookkeepers to make sure they are maximizing their revenue, minimizing costs.
techno [roads, transport]: What technical conditions produce and delimit the phenomena you study?
Teletherapy platforms presume robust internet infrastructure, presume that both therapist and client have access to devices and technical expertise. Often, those clients who are high-risk or high-needs are those clients who do NOT have internet or device access or expertise.
Complex technical infrastructures required to do mental health remotely, requires therapists to gain expertise in HIPPA compliant video platforms, in EHRs, in online billing.
eco-atmo [ecology, climate]: What ecological and climatic conditions situate the phenomena you study?
I’m not entirely sure how to answer this one. One maybe relevant point is that a teletherapy world requires a lot less of people driving cars. A lot of current community mental health these days has therapists or social workers drive TO clients, heard from interviewees that they were spending a lot of time in their cars and in traffic.
geo [earth systems]: What geological formations, contaminations, resources and scarcities ground the phenomena?
Again I’m not sure how to answer this exactly, although rural populations (e.g. farming communities, mining communities, communities that may be more focused on extracting resources from land) notoriously have difficulty accessing the human services infrastructure of mental health?
Researchers living and working in Nairobi are worried that their research practices are experienced as extractive by those they study. They are worried for multiple reasons - because it could taint their data if only a small subset continue to participate, they might be viewed as unethical by funders, the broader communities, etc. etc.
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