Paul N. Edwards is an STS specialist at the University of Michigan; he is a former student of Donna Haraway (who I’ve mentioned before and we will read later), and has directed Michigan’s STS program. He is currently in both the Department of History and the School of Information, “an interdisciplinary professional school focused on bringing people, information, and technology together in more valuable ways.” I am a big fan of one of his earlier books called The Closed World, which is essentially a history of the computer and the Cold War.
For Edwards, the history of meteorology is a shift from voluntary internationalism to infrastructural globalism – “the building of technical systems for gathering global data helped to create global institutions and ways of thinking globally” (Edwards 2010:xviii). While this book could be said to be about climate change, and that the ‘vast machine’ is a reference towards the earth, I think it may help you see the book as about the vast machine that is the ‘scientific-industrial complex’ or the vast machine that is the infrastructure behind the study of climate change. Edwards will also be looking at the basic question of the role of scientific models. From our discussion of ‘evidence-based politics,’ are scientific models evidence? Do they produce ‘facts’? Edwards point in this debate is that “without models, there are no data” (Edwards 2010:xii)
“In modern weather forecasting, for example, only about ten percent of the data used by global weather prediction models originate in actual instrument readings. The remaining ninety percent are synthesized by another computer model: the analysis or “4-dimensional data assimilation” model, which creates values for all the points on a high-resolution, three-dimensional global grid” (Edwards 2010:21).
I think that Edwards’ perspective of looking at climate science and meteorology as an infrastructure helps us better understand how scientific knowledge is produced. For Edwards, the history of meteorology is also “a history of systems, networks, and webs; of data, models, and knowledge flows” (2010:xxiv). What exactly is an infrastructure? A common definition of infrastructure is “the physical components of interrelated systems providing commodities and services essential to enable, sustain, or enhance societal living conditions” (Wikipedia). More importantly, I like to see infrastructure as the necessary parts of everyday life that only become visible when something is broken. (As an aside, this is also how I see culture – information/practices that are unspoken, taken for granted, by a society.) The levies that are part of the landscape, the background, only become visible when they are breached; but they are absolutely necessary for life in places like New Orleans. Or perhaps more closer to your own experiences, infrastructure is like campus wifi – it is necessary for your everyday life, but you only notice it when it is down. Edwards says:
“infrastructures reside in a naturalized background, as ordinary and unremarkable as trees, daylight, and dirt. Our civilizations fundamentally depend on them, yet we notice them mainly when they fail. They are the connective tissues and the circulatory systems of modernity. By linking macro, meso, and micro scales of time, space, and social organization, they form the stable foundation of modern social worlds” (Edwards 2010:8-9).
What is Climate?
Edwards distinguishes the study of weather from climate; studying climate is essentially a historical study: “Climate is the history of weather-the average state of the atmosphere over periods of years, decades, centuries, and more” (Edwards 2010:xiv). As the historical study of a physical system, it involves collecting records from the past using three different models: simulation models (based on physical theory); reanalysis models (from weather forecasting); and Data analysis models (based on adjustments to original instrument data). To understand climatology itself, Edwards recommends thinking of it as ‘infrastructural inversion’ – the reconstructing of history of the atmosphere from data. He is then focused on the “climate knowledge infrastructure,” especially the divergence of weather forecasting and climatology.
Think globally, act locally.
“It asserts an intimate relationship between two vastly different scales: macro, world-scale environmental and economic systems, on the one hand, and the micro sphere of individual choice and action, on the other. It extends an arrow of agency, comprehending macro effects as the results of vast aggregations of micro causes. Thus it locates the meaning of individual action in its relationship to the gigantic whole. Finally, it affirms that global change matters so deeply that it should occupy the intimate corners of everyday awareness and guide each person’s every choice” (Edwards 2010:1).
The question then becomes how did the world become a system (or how did meteorology become an infrastructure). Edwards has a good description of what constitutes an infrastructure, whether in terms of climatology or in terms of roads, energy, or water:
Infrastructures: production, communication, storage, and maintenance web with both social and technical dimensions (Edwards 2010:18).
- reach or scope
- learned as a part of membership
- links with conventions of practice
- embodiment of standards
- built on an installed base
- becomes visible upon breakdown
- is fixed in modular increments, not all at once or globally (Edwards 2010:8-9)
In terms of climatology and meteorology, before something becomes ‘infrastructure,’ it can be seen as first becoming a ‘large technical system.’
Large Technical System:
- development and innovation
- technology transfer, growth, and competition
- splintering or fragmentation
Now while it is intuitive to think of physical systems as infrastructure, knowledge can also be seen as an infrastructure. And if we think of what we learned from Latour, this ‘knowledge infrastructure’ is part and parcel of the physical infrastructure. The knowledge infrastructure: “comprise robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds” (Edwards 2010:17). Science itself is a knowledge infrastructure with the following characteristics: (Edwards 2010:17):
- enduring communities with shared standards, norms, and values
- enduring organizations and institutions, such as libraries, academic departments, national science foundations, and publishers
- specialized vocabularies
- conventions and laws regarding intellectual property
- theories, frameworks, and models
- physical facilities such as classrooms, laboratories, and offices
- “support” staff: computer operators, technicians, secretaries
Now here is the kicker, for you to think about as you read the rest of the book (and in particular for this class). We’ve talked a lot about what constitutes fact. Edwards suggests that a fact (or what he calls ‘an established fact’) is one supported by an infrastructure (Edwards 2010:22).