Table Of ContentLimits of the Numerical
̇․̇․̇
Limits of the
Numerical
․̇․̇․
The Abuses and Uses
of Quantification
Edited by
Christopher Newfield,
Anna Alexandrova,
and Stephen John
The University of Chicago Press
Chicago and London
The University of Chicago Press, Chicago 60637
The University of Chicago Press, Ltd., London
© 2022 by The University of Chicago
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Published 2022
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ISBN- 13: 978- 0- 226- 81713- 2 (cloth)
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ISBN- 13: 978- 0- 226- 81716- 3 (e- book)
DOI: https://doi.org/10.7208/chicago/9780226817163.001.0001
Library of Congress Cataloging-in-Publication Data
Names: Newfield, Christopher, editor. | Alexandrova, Anna,
1977– editor. | John, Stephen, editor.
Title: Limits of the numerical : the abuses and uses of quantification /
edited by Christopher Newfield, Anna Alexandrova, and Stephen John.
Description: Chicago ; London : The University of Chicago Press,
2022. | Includes bibliographical references and index.
Identifiers: LCCN 2021050921 | ISBN 9780226817132 (cloth) |
ISBN 9780226817156 (paperback) | ISBN 9780226817163 (ebook)
Subjects: LCSH: Quantitative research. | Quantitative research—Case
studies. | Quantitative research—Social aspects. | Education, Higher—
Research—Case studies. | Health—Research—Case studies. |
Climatology—Research—Case studies.
Classification: LCC Q180.55.Q36 L56 2022 | DDC 001.4/2—dc23/
eng/20211204
LC record available at https://lccn.loc.gov/2021050921
The University of Chicago Press gratefully acknowledges the generous
support of the Independent Social Research Foundation toward the
publication of this book.
This paper meets the requirements of ANSI/NISO Z39.48- 1992
(Permanence of Paper).
Contents
Introduction: The Changing Fates of the Numerical · 1
Christopher Newfield, Anna Alexandrova, and Stephen John
Part I
Expert Sources of the Revolt against Experts
1. Numbers without Experts: The Populist Politics of Quantification · 23
Elizabeth Chatterjee
2. The Role of the Numerical in the Decline of Expertise · 47
Christopher Newfield
Part II
Can Narrative Fix Numbers?
3. Audit Narratives: Making Higher Education Manageable in
Learning Assessment Discourse · 71
Heather Steffen
4. The Limits of “The Limits of the Numerical”: Rare Diseases
and the Seductions of Qualification · 93
Trenholme Junghans
5. Reading Numbers: Literature, Case Histories,
and Quantitative Analysis · 117
Laura Mandell
Part III
When Bad Numbers Have Good Social Effects
6. Why Five Fruit and Veg a Day? Communicating, Deceiving, and
Manipulating with Numbers · 143
Stephen John
7. Are Numbers Really as Bad as They Seem?
A Political Philosophy Perspective · 161
Gabriele Badano
Part IV
The Uses of the Numerical for Qualitative Ends
8. When Well- Being Becomes a Number · 181
Anna Alexandrova and Ramandeep Singh
9. Aligning Social Goals and Scientific Numbers: An Ethical- Epistemic
Analysis of Extreme Weather Attribution · 201
Greg Lusk
10. The Purposes and Provisioning of Higher Education: Can Economics
and Humanities Perspectives Be Reconciled? · 219
Aashish Mehta and Christopher Newfield
Acknowledgments · 257
References · 259
Contributors · 289
Index · 293
[Introduction]
The Changing Fates of the Numerical
Christopher Newfield, Anna Alexandrova,
and Stephen John
Both private and commercial aircraft have a variety of navigational tools
they can use. One is very high frequency omnidirectional range and dis-
tance measuring equipment (VOR/DME), which allows users to combine
measures of their bearing and their slant distance from an object like an
airport runway. It uses highly detailed quantitative information to offer
precise measures of the kind that allow pilots to execute safe landings in
bad weather without good visual contact with the ground. As such, VOR/
DME offers a classic example of the powers of the numerical as they im-
prove daily life by building quantification into systems.
Not long after midnight on August 6, 1997, Korean Air Lines (KAL)
flight 801 was using a VOR/DME beacon operated by the airport on the
island of Guam. The plane and the VOR/DME system were both working
perfectly. The captain was a highly experienced pilot with 8,900 hours of
flying time, having also flown for the Republic of Korea Air Force. He had
landed at Guam at least eight times, as recently as the previous month.
The other members of the flight crew were also experienced and well
trained. It was raining on approach, but not dangerously so. This was a
fairly routine landing conducted by highly qualified professionals. And
yet this captain and crew used the VOR/DME beacon to crash KAL 801
into the side of a hill (Gladwell 2008, 178).
This flight crew had no deficiency of up- to- date technical training. They
were extremely skilled at all aspects of the navigational system and with
the wide variety of circumstances in which it was used. But their crash
was part of a pattern for Korean Airlines, so much so that in 1999, South
Korea’s president declared KAL’s accident rate to be a national i ssue, and
switched his presidential flight program to the country’s other airline.
According to Malcolm Gladwell’s account of this story, what ultimately
downed KAL 801 was deference culture in the cockpit. He traces this to
1
2 ‹ Christopher Newfield, Anna Alexandrova, and Stephen John
hierarchies built into Korean language and cultural practice. Neither the
copilot nor the flight engineer felt that they could confront, correct, or
even speak directly to the captain, their superior officer. The flight engi-
neer likely realized their course was off and should have said, “Captain,
you remember that the VOR/DME beacon isn’t on the runway here. And
it’s really too cloudy to make a visual landing.” Instead, he said, “Captain,
the weather radar has helped us a lot” (Gladwell 2008, 216).
When KAL finally confronted the pattern of crashes, it brought in
someone who focused on changing the company’s culture, particularly
the modes of deference that had withstood normal crew resource man-
agement techniques. He changed the flying language from Korean to Eng-
lish, and one doesn’t have to buy the generalizations of Gladwell or some
crash investigators about Korea as a rigidly hierarchical culture to see that
language change would interrupt entrenched patterns and enable things
to be said that one would be reluctant to say in one’s mother tongue. Many
have credited a range of cultural changes with ending KAL’s run of acci-
dents: as of this writing, KAL has been crash- free since 1999.
We wish to use this story to highlight a different phenomenon. Numer-
ical information has meaning through the institutional and cultural sys-
tems in which it is created and used. The solution to KAL’s crash problem
was not to create proper respect and facility for numerical information,
which was already extremely high with its pilots, nor to identify individ-
ual acts of interpretive error, nor even to train officers to speak openly to
their captain. Rather, the solution was to enable the whole crew’s active
engagement in interpreting details and anomalies in quantitative data in
the qualitative context of their minute- by- minute experience of the flight.
This practice is surprisingly rare, and its rarity is one of our motives for
writing this book. Crises like KAL’s pattern of crashes can cause people
or organizations to correct flawed relations to quantitative data, such as
allowing them to overcome the false sense that numbers carry their own
meanings and can be handled passively. But in general, our societies have
not taken this step. Data of various kinds permeate our private, profes-
sional, and political lives. We need to make a range of personal choices
about our health, family relationships, education, and consumer practices.
We need to choose between political candidates, join or avoid advocacy
groups and social movements, and estimate the benefits of divergent so-
cial policies. We expect ourselves to have good reasons for making these
choices, and we expect the same for others. In all of these spheres we
regularly treat quantitative data as decisive. We underinvest in modes of
qualitative interpretation, though these are often difficult and complex.
We do not design institutions to put qualitative understanding on the
Introduction › 3
same plane as the quantitative. We do not create nonbinary attitudes that
can bring quantitative and qualitative knowledge together. And we do not
treat quantitative information as always embedded in cultural systems,
where the meanings of the numerical are finally decided.
These omissions are doubly dangerous in our allegedly post- truth era,
one permeated not only by internet- enabled “deep fakes” and psychologi-
cal manipulation but also by a supposedly general indifference to facts, or
a decline of reason as eclipsed by affect (Davies 2018).
This volume addresses the role of numerical information as an anchor
of factuality. In a stereotypical received model, qualitative arguments,
cast in language, are composites of fact and opinion, while quantitative
data are precise, value- free, and objective. In this popular framework, sci-
entific knowledge emerged centuries ago from the welter of discourse and
commentary that formed natural history through the continuing process
of mathematization of the relationships among data elements. One result
of this view is our venerable “two cultures” model (Snow [1959] 2001), now
evolved into a split between STEM disciplines (science, technology, engi-
neering, and mathematics) and all non- STEM, the “soft” human sciences,
whose conclusions and very status as knowledge are always contestable.
This binary model is, of course, incorrect, as all domains of knowledge
are a complex mix of the qualitative and the quantitative (six million is
a deceptively precise number from the discipline of history that every
European knows, if they know nothing else about twentieth- century his-
tory). And yet the binary model remains a cultural common sense: While
numbers can of course be falsified and manipulated, they rest on rigorous
methodologies that bring a precision that qualitative reasoning allegedly
lacks. Numbers are the foundation of scientific knowledge, while lan-
guage permeates the far less trustworthy worlds of politics and culture.
This dualistic stereotype affects every domain of social life. In higher edu-
cation, for example, undergraduates have been told to leave the subjective
and supposedly impractical arts and humanities fields for the objective
and efficacious STEM disciplines, whose only common feature is that they
are quantitative.
The two cultures quant- qual stereotype also has social and political con-
sequences. Valid personal decisions and policy arguments are obligated to
start with data like these, and to remain grounded in them:
• The average resident of a member country of the Organisation for
Economic Co- operation and Development (OECD) has a net adjusted
disposable income of just under US$31,000, lives in a household with
