ARTÍCULO DE INVESTIGACIÓN
Numbers or no numbers in science studies
Geoffrey C. Bowker
Department of Informatics, Donald Bren School of Information and Computer Sciences, Universidad de California,
Irvine, 5019 Donald Bren Hall Irvine, California 92697-3440
un acceso abierto
diario
Palabras clave: qualitative, quantitative, science studies
ABSTRACTO
This article analyzes my own research trajectory with respect to the qualitative/quantitative
divide in science studies and situates my work in the field.
I often recall my induction into American academia, lo these many years, at the University of
Illinois at Urbana Champaign. My partner, Susan Leigh Star, was in Sociology and I was
in Library and Information Science. The Sociology Department imploded about 2 years after
her arrival—and she was one of the precipitants. The battle was been the “quantheads”
(as we called them) and the ‘quals’ (not to mention the ‘two headed ethnomonster’ aka ethnometh-
odology). I couldn’t believe that I was being invited into theological battles of no real significance
(shall we count the number of angels on the head of a pin, or just describe them?). Then Leigh told
me about the Chicago School of Sociology, which had been expelled from Eden (aka the
University of Chicago) by the quants. This was a lasting trauma for the School—what had
happened, the story went, was that the quants had stormed in—and whereas no quant ever voted
a qual for tenure or hire, the quals judged cases on their merits. I always had doubts about this story
but it’s persuasive—sort of like U.S. politics right now: If only one side believes in fairness and the
other side believes in the one, true, bien, and only way, then the latter wins. I have witnessed a
number of analog (or should that be digital?) battles over the years, which confirm that we are
dealing with faith rather than philosophy—and that faith is a powerful political force.
I had a touching meeting with Loet Leydesdorff at the Social Studies of Science annual
meeting about 10 years ago. He and I were saddened by the exclusion of scientometrics from
social studies of science. From my background in science studies, actor-network theory was
integrally scientometric (the Leximappe program—see Callon, Law, and Rip [1986]) and onto-
logical/ethnographic. How could it be otherwise? He pointed out rightly that scientometrics
had been expelled from our rather shabby temple, and that even when they put on sessions, él
was only those who already had the faith who attended. And the true paradox was that the
qualitative and the quantitative had grown up together in science studies—they were richly
intertwined, were exploring the same questions with different methodologies, were learning
from each other. Then we witnessed the revenge of the quals. It didn’t matter that they hadn’t
read, thought about, or heeded scientometrics—our emergent discipline was to be partly defined
by the rigorous extirpation of statistical analysis.
So much was lost along the way. Prosopography—which was incredibly insightful, but barely
got started. The capture of emergent trends in science and technology through analysis of subtle
indicators. I loved the work that the Ecole des Mines folks (specifically Michel Callon and Bill
Tornero) had done on the traffic between scientific papers and subsequent patents. This could
not have been studied without large-scale statistical analysis. Of course this is not true of all
Citación: Bowker, GRAMO. C. (2020). Numbers
or no numbers in science studies.
Estudios de ciencias cuantitativas, 1(3),
927–929. https://doi.org/10.1162/
qss_a_00054
DOI:
https://doi.org/10.1162/qss_a_00054
Autor correspondiente:
Geoffrey C. Bowker
gbowker@uci.edu
Handling Editors:
Loet Leydesdorff, Ismael Rafols,
and Staša Milojević
Derechos de autor: © 2020 Geoffrey C. Bowker.
Publicado bajo Creative Commons
Atribución 4.0 Internacional (CC POR 4.0)
licencia.
La prensa del MIT
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Numbers or no numbers in science studies
“science studies”—especially in the policy realm (I know studies of environmental policy closest
aquí, and it is exemplary), there is still free and full movement between the spheres.
An analogy. When I talk with students about functionalism, they often shudder and give a
metaphorical sign of the cross. This is odd—they’ve been taught that it’s bad, but not what it
is—only a tired caricature. When I see students repeating this nonsense, I refer them to Mary
Douglas—an avowed functionalist and a brilliant social theorist. These very antifunctionalist
students frequently parrot the phrase: “What work does … do?” without any awareness that
this formulation is the same as: “What function does … have?.” It is an unthinking, knee-jerk
ideological response, but it’s part of the vulgate, so why fight it?
Another analogy. One of the most promising developments of the past 20 years in the
humanities has been distance reading. Franco Moretti developed (sin, as far as I can tell,
reference to scientometrics) a wonderful theory that enabled the study of vast text corpora (para
all 18th- or 19th-century literature). According to him—and I agree—we could find new genres
of literature that do not yet have a name. And there is a very good qualitative reason for this—as
Cliff Siskin (2016), demonstrates in System, it was the winners—for example, Wordsworth
defining romantic poetry for the generations—who won the day. It is only through distance
reading that you can cut through the rhetoric and recover stories not yet told. Yet this is precisely
what we were doing in the 1980s with very large text corpora. We were finding scientific move-
ments that did not yet have a name and were projecting their future development. The statistical
analysis allowed us to read against the grain of texts. Deconstrucción, at the same epoch, prom-
ised the same: to read askance. Y todavía, unfortunately, this small contribution is possibly alone
in making this obvious connection: The schools are too far apart.
My own background was Annalist history, which completely mixed the qualitative and the
quantitative. Si, following Braudel, you want to understand Mediterranean culture by escaping
the boundaries of nationalist historiography, then of course you needed not just a subtle anal-
ysis of the nature of “peoples”; you also needed good figures on the circulation of goods and
food to develop your claim. They tried to piece together all the available information and turn
it into a story. Segundo, the folks who crunched the numbers equally did the qualitative work—
Francois Furet (1981, 1982) wrote one of the most perceptive discussions of the French rev-
olution (Thinking the French Revolution) and with Marcel Ozouf performed one of the best
discussions of literacy based on careful statistical analysis.
A lot of my work has been about classification systems: Quantification is key. If we are
using numbers, we are enumerating over a set of categories—which poses the question of
where these categories came from. In my current work on machine learning, the shoe is some-
what on the other foot. I consistently have to justify saying that classification is occurring
(Bechman & Bowker, 2019) when number crunchers say that it’s all about computers
crunching raw data—as if such a thing existed! Classification occurs at many levels in com-
plex algorithms: It doesn’t precede numbers, but nor does it succeed them—the two are folded
into each other. Any mode of understanding the world of science and information today needs
to integrally consider the two at each point along the analytic path.
As is clear, I fully support the program of this journal. The world is too complex and the
decisions we must make too difficult to allow us to ignore any set of useful tools. Our field will
be so much the richer when the two are reunited.
CONFLICTO DE INTERESES
The author has no competing interests.
Estudios de ciencias cuantitativas
928
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Numbers or no numbers in science studies
INFORMACIÓN DE FINANCIACIÓN
No funding was received for this research.
REFERENCIAS
Bechmann, A., & Bowker, GRAMO. C., (2019). Unsupervised by any other
name: Hidden layers of knowledge production in artificial intelli-
gence on social media. Big Data and Society. https://doi.org/
10.1177/2053951718819569
Callon, METRO., Law, J., & Rip, A. (Editores.). (1986). Mapping the dynamics of sci-
ence and technology: Sociology of science in the real world. Londres:
Palgrave Macmillan. https://doi.org/10.1007/978-1-349-07408-2
Furet, F., (1981). Interpreting the French Revolution (trans. mi. Forster).
Cambridge: Prensa de la Universidad de Cambridge.
Furet, F., & Ozouf, j. (1982). Reading and writing: Literacy in
France from Calvin to Jules Ferry. Cambridge [Cambridgeshire]:
Prensa de la Universidad de Cambridge.
Siskin, C., (2016). Sistema: The shaping of modern knowledge.
Cambridge, MAMÁ: CON prensa.
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