RESEARCH ARTICLE
Against method: Exploding the boundary between
qualitative and quantitative studies of science
Donghyun Kang1
and James Evans1,2
1Sociology Department, Universität von Chicago
2Santa Fe Institute
Schlüsselwörter: content analysis, mixed methods, qualitative analysis, quantitative analysis, science studies,
word embedding
ABSTRAKT
Quantitative and qualitative studies of science have historically played radically different roles with
opposing epistemological commitments. Using large-scale text analysis, we see that qualitative
studies generate and value new theory, especially regarding the complex social and political
contexts of scientific action, while quantitative approaches confirm existing theory and evaluate the
performance of scientific institutions. Large-scale digital data and emerging computational methods
could allow us to refigure these positions, turning qualitative artifacts into quantitative patterns into
qualitative insights across many scales, heralding a new era of theory development, engagement,
and relevance for scientists, policy-makers, and society.
EINFÜHRUNG
1.
In Paul Feyerabend’s philosophical treatise Against Method (1975), he outlined an “anarchistic
theory of knowledge” that argued all major scientific advances eschew any generalizable notion
of scientific method. Methodological innovations and not tradition portend punctuated progress.
He held that prohibitions against ad hoc hypotheses and inconsistent findings, along with a focus
on theoretical falsification, decreased the potential for new discoveries and insights. Diese Bemühungen
to formalize and systematize science also drastically limit the potential for fields to learn from one
another. In diesem Artikel, we empirically examine the vast and growing divide between quantitative
and qualitative studies of science.
Here we show that quantitative and qualitative science studies represent not only distinctive
objects and approaches of study—distinctive ontologies and epistemologies—but that they
manifest diametrically opposed evaluations of the same objects and approaches. Recent research
on scientific review reveals that researchers further apart in networks of collaboration are more
likely to dispute the validity of those investigations (Teplitskiy, Acuna, et al., 2018). The divide
between quantitative and qualitative studies of science is deeper, and we will argue this divide
limits the potential for insight, advance and relevance that could come from greater collaboration
and an explosion of ontological and epistemic commitments.
British scientist and novelist C. P. Snow argued, in an influential 1959 Rede Lecture, Das
Western intellectual life was divided into “two cultures” (Snow, 1959)—sciences and humanities.
He memorably ridiculed the British educational establishment for overrewarding the humanities
at the expense of scientific literacy. “Once or twice I have been provoked [by snobbish literary
society] and have asked the company how many of them could describe the Second Law of
Thermodynamics. The response was cold: it was also negative. Yet I was asking … the scientific
Keine offenen Zugänge
Tagebuch
Zitat: Kang, D., & Evans, J. (2020).
Against method: Exploding the
boundary between qualitative and
quantitative studies of science.
Quantitative Science Studies, 1(3),
930–944. https://doi.org/10.1162/
qss_a_00056
DOI:
https://doi.org/10.1162/qss_a_00056
Korrespondierender Autor:
James Evans
jevans@uchicago.edu
Handling Editors:
Loet Leydesdorff, Ismael Rafols,
and Staša Milojevic(cid:1)
Urheberrechte ©: © 2020 Donghyun Kang and
James Evans. Published under a
Creative Commons Attribution 4.0
International (CC BY 4.0) Lizenz.
Die MIT-Presse
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Exploding the boundary between qualitative and quantitative studies of science
equivalent of: Have you read a work of Shakespeare’s?” The thrust of Snow’s argument is not that
science is better than humanistic insight, but that intelligent inquiry is divided and that this division
may represent the most prominent barrier to recognizing, grappling with, and ultimately solving
the world’s problems1. We will perform a 21st-century replication of Snow’s cocktail party exper-
iment on quantitative and qualitative students of science, and argue forcefully that a reformulated
treaty between quantitative and qualitative studies could portend a renaissance of engagement
with scientists about what they do—forward and defend supportable claims—at the level at which
they do it. It could also span scales of analysis, connecting scientific action to science policy and
civic action, leading to new discoveries and new relevance. For the prospect of more deeply un-
derstanding and engaging with science, we argue against method.
Prior research has demonstrated important differences between quantitative and qualitative
studies of science. A recent Journal of Informetrics analysis of qualitative versus quantitative sci-
ence studies handbooks produced between 1977 Und 2008 suggested that these areas tend to dis-
cuss different ideas, characterized by distinctive words, which cluster in very different areas of a
high-dimensional “word space” (Milojevic(cid:1), Sugimoto, et al., 2014). Darüber hinaus, when the same
authors analyzed articles from a sample of quantitative and qualitative journals similar to those
we explore below, they found a related clustering pattern. Leydesdorff and Van Den Besselaar
(1997) rendered articles from the same sets of journals into a comparable “citation space,” which
revealed comparable clustering and demonstrated the limited degree to which quantitative and
qualitative science studies drew from and referenced one another.
Our goal here is to revisit the comparison of quantitative and qualitative science studies with
emerging tools from machine learning that allow us to survey not only the distinctive semantic
focus and approach in these areas but also their distinctive evaluation of the same. As we reveal
below, this demonstrates not only difference but direct opposition between quantitative and qual-
itative ontologies and epistemologies that suggest the potential for radical complementarity. Der
strengths of one are the weaknesses of the other, posing powerful opportunities for synthesis.
We begin by detailing our empirical investigation of qualitative and quantitative studies of
Wissenschaft, articulating and interpreting distinctions between these approaches. Then we detail how
new data on science and computational methods open up new pathways for collaboration and
mutual learning that could provoke advance in our understanding and engagement with science.
2.
INTERROGATING QUALITATIVE AND QUANTITATIVE SCIENCE STUDIES
We collected titles and abstracts from five journals. From the qualitative side, we selected Social
Studies of Science (SSS), Wissenschaft, Technologie, & Human Values (STHV ), and Minerva; von dem
quantitative side, we considered Scientometrics (SCI ) and Research Policy (RP). We note that
Milojevic(cid:1) et al. (2014) initially considered articles from RP indeterminately quantitative or qualita-
tiv, but their analysis demonstrated that more than 80% of those articles could be classified as
quantitative studies. The same logic could be applied for SSS, as approximately 10% of its articles
were quantitative, according to their classification. We anchored our categories with classifications
of the journals themselves, as even quantitative studies in SSS and qualitative studies in RP reference
other work from within the same journal and typically reflect epistemological commitments there.
We first collected sets of digital object identifiers (DOI) from each journal. We used the Springer
API for SCI and Minerva, and the Elsevier API for RP, to retrieve all DOIs affiliated with the journals.
Subsequently, titles and abstracts were also retrieved using the APIs. For SSS and STHV, DOIs were
1 Snow argued that because scientists felt humanistic inquiry irrelevant, “their imaginative understanding is
less than it could be. They are self-impoverished.”
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
Tisch 1. Data involved in analysis
Zeitschrift
Journal start Collection start DOIs Documents with abstracts DOIs/Abstracts
Category
Qualitative
Wissenschaft, Technology and
Human Values (STHV)
Social Studies of Science (SSS)
Minerva
Subtotal
Quantitative
Scientometrics (SCI)
Research Policy (RP)
Subtotal
Total
1988A
1988
1,180
1971
1962
1978
1971
1978
1965
1978
1971
1,916
1,899
4,995
5,953
4,031
9,984
838
1,151
396
2,385
5,383
3,234
8,617
14,979
11,002
71%
60%
21%B
48%
90%
80%
86%
73%
a We decided to only take into account articles published STHV from 1988, considering its institutional shift to 4S, when the journal started to rise, wenngleich
the journal was originally founded in 1967.
b Early Minerva articles did not feature an abstract.
All collection ended in October 2019.
first identified with the journals’ ISSN, using Crossref API, and HTML code from Sage web pages
associated with articles was collected. We developed a parser to extract titles and abstracts for all
articles2. Zusätzlich, we used the Web of Science to collect reference sections from SSS and SCI
to identify the citation pattern between the two journals. We employed the Python “Gensim”
package to lemmatize word forms, remove stop-words, and classify part-of-speech. Tisch 1 zeigt an
the number of DOIs and articles included in our analyses from each journal. Zusätzlich, Figur 1
displays the number of articles for each year.
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3. CHARACTERIZING THE DIVIDE
With lemmatized texts from each article, we counted Boolean term frequency for each word to
capture divergent word usages from the “two cultures” (Snow, 2012). We first computed the rel-
ative frequency for each word in both quantitative and qualitative articles. Zum Beispiel, 23.32% von
articles from the quantitatively oriented journals used “impact” in the title or abstract, but only
5.85% of those from qualitative journals. Based on the relative frequency of words, we computed
and arrayed the 10 most extreme ratios, separated by part of speech, in Abbildung 2. Zum Beispiel, Die
ratio of relative frequencies for “impact” from quantitative and qualitative journals, 3.99 (≈23.32/
5.85), is shown in the first column, using the qualitative frequency as denominator. For words
dominant in the qualitative side, numerators and denominators were switched. Note that we
only considered words appearing more than 5% from both sides for Nouns, Verbs, Adjectives,
Und 3% for Adverbs or Prepositions for Figure 2. We provide an extended version with a 1%
threshold for each part of speech in Figure A1 in the Appendix, and we will refer to word ratios
as we discuss the divergence, even if they do not appear in the figures.
To capture nuanced semantic differences in language usage between the two cultures, we con-
structed two word embedding spaces using the titles and abstracts from each. Recent work in compu-
tational linguistics and natural language processing have powerfully represented entire systems of
2 In some cases abstracts could not be extracted because they did not exist: DOIs sometimes indexed book
Bewertungen, editorial statements, short discussions, or obituaries, which we excluded from further analysis.
Quantitative Science Studies
932
Exploding the boundary between qualitative and quantitative studies of science
Figur 1. Aggregated number of articles for each category by year.
meaning by embedding words and sentences as vectors in dense, continuous, high-dimensional
Räume (Le & Mikolov, 2014; Mikolov, Sutskever, et al., 2013; Pennington, Socher, & Manning,
2014). These vector space models, known collectively as word embeddings, have attracted wide-
spread interest among computer scientists (Bolukbasi, Chang, et al., 2016; Levy and Goldberg, 2014),
computational linguists (Garg, Schiebinger, et al., 2018; Hamilton, Leskovec, & Jurafsky, 2016), Und
social and behavioral scientists (Caliskan, Bryson, & Narayanan, 2017; Kozlowski, Taddy, & Evans,
2019) due to their ability to capture and represent complex semantic relations—including stereotypes,
prejudice, and cultural association—inscribed within a discursive culture as present in a corpus of text.
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Figur 2. Ratio between probabilities of given tokens observed in abstracts and titles.
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Exploding the boundary between qualitative and quantitative studies of science
In a word embedding model, each word is represented as a vector in shared vector space with
words sharing the similar surrounding words positioned nearby in the space. If science and
Und
scientist both appear near the word laboratory, then the vectors
Wille
be located near each other in the embedding, even if they never appear together in the text.
We use Google’s word2vec (Mikolov et al., 2013), the most widely used word embedding
Algorithmus, to construct two independent embeddings inscribing the cultures of quantitative and
qualitative science studies. Each deployed the continuous-bag-of-words (CBOW) Algorithmus, gebraucht
50 dimensions, had a word window size of seven, and required each modeled word to appear in
each corpus at least five times. Endlich, to achieve and present robust results, we averaged projec-
tions from 100 trained models as word2vec incorporates a stochastic element that causes weight
matrices to differ slightly with the same hyperparameters and corpus.
Within quantitative and qualitative epistemological cultures, we sought to identify the evalu-
ative dimension along which those cultures array ideas and approaches as better or worse
(Osgood, 1964; Osgood, Suci, & Tannenbaum, 1964). Word2vec initially received substantial
attention based on its capacity to solve analogy problems, such as “man is to woman as king is
to _____” (Mikolov et al., 2013). This can be solved by performing
+
−
, welche
will return a vector closest to the vector
on a sufficient embedding space. This suggests
−
Das
projects negatively. Building on this capacity, Kozlowski et al. (2019) proposed a method of
constructing cultural dimensions such as class by taking the arithmetic mean of word vectors
inscribes a gender vector on which
projects positively and
,
representing class antonyms (z.B.,
). This approach
has been widely validated and adapted (Ahn, 2020; Ein, Kwak, & Ahn, 2018; Bodell,
Arvidsson, & Magnusson, 2019), and we employ it here to construct and compare quantitative
and qualitative analysts’ evaluation of different phenomena, concepts, and approaches to the
study of science.
Und
,
We anchored the evaluative dimensions in quantitative and qualitative science studies using
the following word pairs: good-bad, better-worse, right-wrong, satisfactory-unsatisfactory,
positive-negative, sufficient-insufficient, effective-ineffective, excellent-failed, success-failure.
Zum Beispiel, to compute the association between theory in the quantitative worldview, we com-
puted its orthogonal projection through calculation of the cosine similarity between the normal-
ized word vector
and the vector calculated by summing up (
−
) + (
−
−
) + … + (
) from the trained word2vec model based on the corpus from
SCI and RP. A resulting value can fall between 1.0, signifying extreme positive evaluation, Und
−1.0, suggesting extreme negative evaluation. The value for theory on this evaluative projection
in the quantitative embedding is −.17. We repeated the same procedures with the model based
on the corpus from SSS, STHV, and Minerva to examine how the same words project in the qual-
itative world view, where theory projects much more positively at .04.
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4. HOW QUANTITATIVE AND QUALITATIVE WORLD VIEWS DIVERGE
We first explore the relationship between quantitative and qualitative science studies by interpret-
ing the nouns and adjectives they disproportionately use to capture the ontology of their world-
views—what is real in science to them. Then we investigate the verbs, Adverbien, and prepositions they
deploy to capture their investigative epistemology as they study science—reflecting how the two
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
worlds differ in how they know what they know (Cetina, 2009). Endlich, we explore the projections of
words associated with divergent ontologies and epistemologies to examine how ontological and
epistemological salience relates to evaluation. Natürlich, these three investigations are not mutually
exclusive, so our mapping and projection of parts of speech involves some redundancy; aber wir
believe that this allows us to understand, describe and reinforce what they know and what they value.
4.1. Qualitative Ontology
The first two columns of Figure 2 and Figure A1 reveal two contrasting ontologies or worldviews:
They answer the question of what exists and is differentially worthy of consideration within the two
cultures of science studies. Consider how the words practice (5.09), scientist (2.26), arbeiten (2.07),
actor (3.57), Körper (4.50), and material (3.89) are hundreds of percent more likely to appear in qual-
itative than quantitative science studies. Practice appears 5.09 more times—509% more—in qual-
itative journals, reflecting that the setting (2.76) or context (2.01) of research and medical practice,
such as the laboratory (3.62), have been a major context of qualitative studies, but remain invisible
to quantitative researchers who only have access to statistics derived from the public record. Für
Beispiel, the practices of theoretical physicists have been studied by ethnographic observation
(Merz & Cetina, 1997) and analysts have recently examined the shift in neuroscientific practice
surrounding the advent of neuroinformatics (Beaulieu, 2001). Qualitative researchers have also
lavished attention on cognitive practices and states deeply bound up with scientific and medical
arbeiten, including uncertainty (2.73), expectation (2.75), Entscheidung (1.72), and risk (3.85). Diese
practices are qualified with adjectives such as experimental (1.83), professional (3.98), (bio)
medical (2.67), and clinical (2.58), suggesting a commitment to institutional (1.24) aspects of
techno-scientific work that remain nevertheless particular (1.62) to distinctive domains of practice.
Qualitative science studies have also focused on the qualitative character or form (3.12) von
persons3 (3.05) inside scientific and medical contexts, such as experts (3.03). Beyond fixed charac-
teristics, they pay special attention to processes—the way (3.01) events unfold—such as transfor-
mation (3.30)—and whether such processes are social (2.47), cultural (6.22), conventional (1.80),
politisch (6.18), or environmental (2.76). They also consider large and amorphous social classes
such as publics (3.21) and society (3.89) that exert influence within scientific settings, or forces that
resist quantitative identification, such as power (2.80), regulation (3.85), and governance (3.20).
Qualitative analysts also undertake research in ways distinctive from those publishing quanti-
tative journals. They interview (5.53) Teilnehmer (3.57) and invoke sociology (5.22). They raise
issues (1.77), Fragen (1.84), and concerns (3.24), make arguments (3.09), and engage in debate
(5.32). They cultivate notions (4.72) and constructions (5.25) that mature into concepts (2.21) Und
constitute theory (1.81). Qualitative researchers also manifest different rhetorics of argumentation
from their quantitative fellows. They forward positions central (2.08), key (1.23), crucial (1.78),
and fundamental (1.83), but they have affection for perspectives at the periphery that are critical
(1.89), alternative (1.78), and distinct (1.88).
4.2. Quantitative Ontology
The quantitative landscape of scientific subjects and objects appears dramatically different. Eher
than examining the qualities of scientific practice, they focus on quantitative (2.72) and economic
(2.87) outputs of scientific organizations including performance (3.98), impact (3.99), investment
(3.15), and innovation (2.96). Rather than materiality and practice, they attend to legal and
3 We do not distinguish between plural and singular words in our analysis, which considered lemmatized
Wörter, but vary them in text to facilitate exposition.
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
corporate entities including firms (12.14) and countries (3.12), which are identifiable from pub-
lished scientific metadata, and describe things with respect to the boundaries of these legal
objects—as foreign (3.77), regional (3.57), or international (1.96). They discuss corporate scientific
entities in terms of whether they are better (2.56) or worse, productive (2.49) or inert, and collab-
orative (2.34) or independent. They also consider measurable traces of the publication process,
such as publication (7.08), journal (6.74), and citation (12.36) using the web (7.84) of science,
but also patents (10.83). Quantitative science studies researchers view these facets of scientific ac-
tion as measures (4.45) or indicators (12.12) of higher level phenomena such as collaboration
(2.89). Darüber hinaus, they describe them not in the context of a case or example, but rather as a
sample (3.99) of an underlying distribution (3.26).
Quantitative science studies researchers see their data in terms of number (3.11), analyzed by
some method (1.77) or methodology (2.79) to evaluate a hypothesis (3.08). This allows them to
discover a result (3.10) in the form of an increase (5.53), trend (4.83), Ebene (2.59), share (2.99), oder
combination (2.75).
Another ontological divergence is found in the way the two cultures predicate their subjects
of study. Referring to a statistical model, quantitative analyses tend to report significant (1.99)
Erkenntnisse. Quantitative science studies (offensichtlich) evaluate these subjects quantitatively, In
terms of whether they are the same (1.39) or different (1.32), represent a simple (2.11) or full
(2.15) model specification, or score high (2.53) or low (3.81), groß (1.95) or small (2.33), mehr
(1.50) or less (1.25), and positive (4.11) or negative (2.73) on some metric. Discursively, quan-
titative articles hierarchically rank their findings, easily summarized by main (2.59), overall
(3.24), and general (1.29) points. They qualify these findings as being consistent (2.35) oder
relative (2.88) to one another, and isolate outcomes due (2.22) to the same underlying causes.
In striking contrast, quantitative versus qualitative science studies tend to draw upon economics
(2.87) versus sociology (5.22); focus on objects international (1.96) versus institutional (1.24),
allgemein (1.29) versus particular (1.62), empirical (1.69) versus theoretical (1.39), and simple
(2.11) versus complex (1.51); and finally frame their arguments in terms of hypotheses (3.08) versus
Fragen (1.84) and findings (2.18) versus issues (1.77).
The relative prevalence of verbs and adverbs in the two cultures of science studies provides
powerful insight to the research behaviors that distinguish qualitative versus quantitative
epistemologies.
4.3. Qualitative Epistemology
Qualitative researchers disproportionately explore (1.78) scientific phenomena, looking (2.06) für
and seeking (2.71) to recognize (2.15) and understand (2.65) insights they subsequently describe
(1.53) to their audiences. They articulate their tasks and those of the agents they observe with a
physicality uncharacteristic of quantitative work. They engage (2.60) and call (2.13), bring (2.86)
and take (1.42), kommen (2.00) and go (2.07), and move (2.81) and turn (2.64). This may be unsur-
prising as qualitative research methods inherently involve the body as both instrument and object
of study. Qualitative researchers discursively unfold their exposition through active, even muscu-
lar, rhetoric, suggesting explicit acts of construction. They claim (4.63), argue (4.27), Herausforderung
(3.10), maintain (2.43), and emphasize (2.48) ideas in building up a system that engages (2.60)
other concepts, arguments, and interlocutors.
Insofar as social constructivism implies that human action is socially situated and knowledge is
constructed through interaction, qualitative science studies scholars perform this approach
(Collins, 1981). They draw (2.70), shape (4.23), embed (2.34), constitute (3.06), and explicitly
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
construct (2.40) theories that not only reflect their propensity to view (2.10), sehen (2.89), and look
(2.06) at scientific phenomena in situated context (2.01) but also produce knowledge in the
situated context of the article form itself. Als solche, they are not certain of their claims, but attempt
(1.91) ihnen.
Darüber hinaus, their mode of research activities resists strict quantification. Observations are made
closely (1.63), and the phenomena they observe occur always (1.49), often (1.61), usually (1.37),
and largely (1.51). Spatially and temporally, things happen here (1.17), Jetzt (2.02), currently
(1.30), or increasingly (1.76). Qualitative students of science muse on the cosmic space of possi-
bilities by considering things that could even occur potentially (1.72). This reflects how subjects of
qualitative research are themselves engaged in complex transformations that resist quantification:
They arise (2.69), become (1.88), and change (1.89), but then continue (2.10) in the paths they
begin (2.47). Violating the canonical iid assumption behind most econometric models (that cases
are identically and independently distributed), most things happen together (2.01). Defying the
causal ordering of events, many of those things occur simultaneously (2.68). This complexity
requires contingent argumentation, and the creation and subtle resolution of paradox, reflected
by intensive use of prepositions rather (1.73), Also (1.99), yet (2.06), and instead (1.86).
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4.4. Quantitative Epistemology
Quantitative researchers follow the central dogma of empiricism: They observe (2.15), collect
(2.21) Information (2.61), and measure (4.63) Daten (2.58), then apply (2.26), use (1.60), or utilize
(1.72) methods to identify (1.97) and find (2.51) things about the world. They do not explore, Sie
investigate (1.94). Rather than constructing new theories, they assess (2.55) and compare (2.53)
existing ones, proposing (1.57) hypotheses, then evaluating (2.54), and testing (2.83) ihnen. Das
poises quantitative students of science to use their validated models (1.35) to predict (2.37) Die
future and confirm (5.22) what they expect (2.72).
A key epistemological move in the quantitative habitus is to identify inexpensive quantities
that indicate (3.06) concepts of theoretical interest, and then analyze (1.65) the structure of
indicators assuming a conserved map (2.28) or homomorphism with the pattern of underlying
concepts. Unlike qualitative analysts of science, they assume an attitude of objectivity, welche
leads to greater certainty: They do not question, they determine (2.45). They do not construct,
they obtain (4.34).
Insofar as quantitative science studies research innovation (2.96), productivity (5.13), perfor-
Mance (3.98) and outputs (10.78), they naturally observe these states increase (2.48) improve
(2.44), enhance (1.78) and perform (1.76). Their adverbs illuminate how quantitative science
analysts do what they do. They make their arguments empirically (1.60) and their models reveal
findings significantly (3.22). They also reveal quantities underlying their claims. They observe phe-
nomena more (1.18), frequently (1.74), highly (1.48), and recently (1.37). Quantification enables
them to discuss scientific and technological agents that are most (2.06) and very (1.37). The struc-
ture of their statistical models allow them to compare (2.53) and account for things relatively (2.28).
Quantitative analysts of science make claims declaratively, like a high-level programming
Sprache. Their findings may be hierarchically summarized mainly (2.18) or generally (1.26),
and listed serially, Außerdem (2.74), Auch (1.21), and therefore (1.56), concluding finally (1.37)
in contrast with the complex contingencies in qualitative exposition. To summarize, acolytes of
empirical epistemologies test (2.83), while interpretive explorers argue (4.27). Quantitative ana-
lysts objectively observe (2.15) and find (2.51), while qualitative scholars intuitively see (2.89) Und
verstehen (2.65). These differences in creative agency burrow down to the atoms of quantitative
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Exploding the boundary between qualitative and quantitative studies of science
and qualitative analysis: Quantitative findings consist (1.73) of discovered facts while qualitative
ones are constituted (3.06) by them.
5. EVALUATION
To capture how quantitative and qualitative science studies evaluate their own most common
ontological observations and epistemological moves, but also those of the other, we embedded
all of their words into two separate high-dimensional Euclidean spaces, enforcing internal seman-
tic consistency to quantitative and qualitative articles, jeweils. We view quantitative and
qualitative embedding models as representations of the worldviews distinct to the two cultures.
Then we induced an evaluative dimension that distinguished how those worldviews judged re-
search approaches (verbs and adverbs), subjects of study (nouns and adjectives), and discursive
moves (prepositions, conjunctions, usw.) We initially selected 10 words prior to computation to
illustrate their difference between qualitative and quantitative worldviews, as shown in
Figur 3. The evaluative projection of these words within quantitative and qualitative worldviews
manifest strong negative correlations—Spearman correlation of –.71 and Pearson correlation of
–.50.—suggesting that quantitative and qualitative science studies reflect not independent but
opposing epistemological worlds. They know different things, but they also value that knowledge
in opposing ways. Darüber hinaus, when we take all the words from Figure 2 and project them on the
evaluative dimension in each of the two worlds, we continue to find strong negative correlations of
−.53 (Spearman), and −.50 (Pearson)—see Figure A2 in the Appendix.
This relationship between frequency of use and positive evaluation suggests a strong relation-
ship between what we know and what we prefer. By observing an equally potent relationship
between negative evaluation and infrequency of use in one’s worldview, we see that researchers
not only study but also laud what they believe can be studied and denigrate what they believe
kann nicht. Darüber hinaus, it appears that what one culture of science studies examines infrequently—but
their neighbor examines intensively—are things they believe neither can nor should be studied.
This represents bad work. Our findings delineate not only ontological and epistemological but
also moral boundaries between quantitative and qualitative studies of science, where what can
be studied maps onto what should be studied.
The citation pattern between journals reflects this division. Figur 4 displays the citation pattern
between SCI and SSS from 2000 Zu 2019 based on a rolling average of the proportion of papers in
each journal citing the other. Zum Beispiel, the value for 2000 was calculated as the nonweighted
mean of the citing ratio from 1996 Zu 2000. The figure demonstrates persistent division. But the
division is curiously asymmetric. SCI papers have been more likely to cite SSS papers than the
Figur 3. Projection of words onto the evaluative (“good-bad”) dimension in each culture.
Quantitative Science Studies
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Figur 4. Five-year rolling average of the proportion of papers with cross-citation between SCI and
SSS from 2000 Zu 2019.
converse, suggesting that quantitative science studies sometimes employ qualitative studies to
propose theories and provide framing. But even this has shrunk in recent years.
6. BREAKING THE BARRIER
The meager, asymmetric collaboration we observe between qualitative and quantitative science
studies—qualitative research discovers things worth counting, then quantitative research counts
them—suggests a fixed division of labor that systematically limits the one-way insights that can
pass between the cultures. Combined with the limited proclivity of quantitative sciences studies
to engage in theory-building, or qualitative investigations to evaluate hypotheses, the potential for
advance in either has become severely constrained. But one can imagine a new treaty between
them for the benefit of science about science (Fortunato, Bergstrom, et al., 2018).
Patterns above the level of ethnographic visibility, detectable only through surveying large-
scale quantitative patterns, could lead directly to new discoveries in the science of science. If quan-
titative science studies adopted a complex systems approach that not only tested preimagined
hypotheses but also documented emergent phenomena, then they might have insights to intrigue
and feed qualitative investigation that had been ignored, only anchored and obvious above
the level of observable scientific practice. Lee and Martin (2015) argue for this possibility of quan-
titative “cartography—the construction of question-independent, though theoretically organized,
reductions of information to make possible the answering of many questions.”
Regularities in quantitative traces of science demand complex causal investigation, which in
turn require qualitative assessment (Small, 2013; Tavory & Timmermans, 2013) in that it involves
new and unanticipated factors, which may be visible through the instrument of the body even
though never previously collected. Infolge, qualitative research facilitates the detection of com-
plex configurations of delicate signals, which could unearth chains of causal influence, or test and
compare quantitatively derived inferences. Zum Beispiel, in a recent paper we published (Wu,
Wang, & Evans, 2019), we identified a strong, negative relationship between the size of teams
producing science and technology, and their likelihood to disrupt the frontier in their domains,
Quantitative Science Studies
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controlling for authors and outputs visible across 65 million teams. We did not publish a causal
account for how this occurs, because the complex mixture of cognitive and social phenomena
underlying the effect, ranging from collaborative inhibition (Barber, Harris, & Rajaram, 2015) Zu
risk aversion (Christensen, 2013) to transaction costs (Williamson, 1985) to some other unima-
gined force, involve factors that have not been, and may never be, measured across the tens of
millions of teams we examined quantitatively.
In den vergangenen Jahren, large-scale digital repositories of complex, qualitative scientific artifacts have
become widely available through online services such as Figshare, the Open Syllabus project,
GitHub, and customized collections drawn from the web or through recording and digitization
efforts. The supply of these digital artifacts, including preprints, proposals, slide presentations,
meeting recordings, and online conversations, has driven demand for powerful machine learning
Werkzeuge, especially deep neural networks (Manning, 2015), that can encode them into quantitative
Daten. But these artifacts are fundamentally high-dimensional, containing many overlapping
qualities—each of which could potentially be enumerated. What to quantify? If we reduce these
artifacts into quantities previously theorized, we ignore the vast new views of science uniquely
available to us. Emergent patterns in complex data demand qualitative sensibilities to determine
a research focus and how to interpret, theorize, and qualify insights (Evans & Foster, 2019; Nelson,
2017). But these skills have been underexercised and undervalued among quantitative re-
searchers. Bringing together qualitative sensibility with quantitative literacy and computational
skill will require overcoming ontological, epistemological, and moral divides regarding what is
real, what is knowable, and what is good.
In an era of small data on science, quantitative approaches maximized our insight by testing
strong theories, buttressed by a myriad assumptions. In an era of big data, Jedoch, we maximize
discovery by reducing our assumptions, weakening our theories and growing new ones (Lee &
Martin, 2015). With big data, we can inductively discover grounded theory on some data
(Hannigan, Haans, et al., 2019; Nelson, 2017), then quantitatively test it on other data. But doing
this requires removing the misplaced moral taint associated with “data mining” embedded within
the contemporary quantitative culture of science studies. The importance of fixed, preset hypoth-
eses made sense in an era of small data, but does not in one of large data. We believe that new
computational methods and digital data could weaken the demarcation between quantitative and
qualitative science studies, if we can overcome the evaluative commitments that separate them.
New computational methods and digital data could also broach another divide.
Quantitative science studies, as published in outlets such as RP, often address the agents of
science policy, such as public funders or journal editors. By identifying bias, and pathways to
higher performance critical to the mission of these policy-makers, such studies offer direct insights
that could improve the institutions of science. Qualitative science studies, by contrast, often reflect
on the implications of closely examined scientific institutions and policies. Articles in serials such
as Minerva and Nautilus raise broader questions about how and why science is as it is, and what
alternatives might exist. But neither tends to speak to the scientists they study. Historically, Die
philosophy of science dealt with the making and defending of scientific claims (Quine, 1951)
and the public moves scientists undertake, which drive both scientific influence and impact. In
the mid-20th century, philosophers of physics would publish in physics journals, arguing over
the legitimate interpretation of observations and experiments.
We argue that discursive acts of claims-making are critical behaviors in science, and that with
computational methods and large-scale samples of this activity, science studies can begin to
address questions that directly address scientists, their efforts, and the scientific and reputational
consequences that follow. A reformulated treaty between quantitative and qualitative studies,
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
forged by computational methods and big scientific text data could portend a renaissance of
engagement with scientists about what they do—forward and defend scientific claims—at the
level at which they do it. Deeper collaboration between qualitative and quantitative studies of
science would span scales of analysis, connecting scientific action to science policy along one
dimension, and civic action and participation on the other (Durant, Evans, & Thomas, 1989), po-
tentially driving new discoveries and new relevance.
We believe that our own case, contrasting the relationship between qualitative and quantitative
science studies, demonstrates how computation can disturb the demarcation between quantity
and quality. The enumeration of word quantities requires in-depth interpretation, which involves
theorizing about distinct qualities. While exploiting computational representations emerging from
our models built from qualitative and quantitative research articles, we had to consider nuances of
meaning about words in context to make sense of their projections on the evaluative dimension
within each of the two cultures.
We note that our observed divisions between quantitative and qualitative science studies re-
flect broader patterns distinguishing quantitative and qualitative turns across social and natural
Wissenschaften. Qualitative and quantitative science studies were much more likely to reference sociol-
ogy and economics, jeweils. These index homologous differences in qualitative and quanti-
tative analysis of governments, Firmen, markets, Schulen, and other domains of social life.
Qualitative approaches, more prevalent in sociology, anthropology, and history, examine trans-
formations, conjunctions and other complex social processes resisting quantification. Quantitative
Methoden, central to economics, and increasingly political science, Statistiken, and computer and
information science, focus more frequently on defensively establishing increases, decreases, co-
Beziehungen, and causes. These patterns are not limited to the social sciences. Peter Galison has doc-
umented the mid-20th-century conflict between qualitative, image-based analyses in physics
using exposed film and cloud chambers to visually trace particles, versus logical tests of particle
presence with spark chambers (Galison, 1997). In Evelyn Fox Keller’s A Feeling for the Organism,
she details a stark distinction between Barbara McClintock’s qualitative, intuitive understanding of
maize and genetics, contrasting it with formal models and small-scale controlled experiments of
dismissive colleagues like Sewall Wright and Joshua Lederberg (Keller, 1984)4. In many historical
Fälle, Jedoch, the merger of qualitative insights and quantitative formalisms and large-scale
measurement have been associated with breakthrough advance, as in the construction of bubble
chambers and the formal characterization of genetic operons and transposons.
Auguste Comte argued that the practice of science in action should also be a subject of an
empirical investigation (Comte, 1988), but only a new pact between qualitative and quantitative
investigation can approach this aspiration. It leads us to sympathize with Feyerabend: “The idea of a
fixed method, or a fixed theory of rationality, rests on too naïve a view of man and his social
surroundings. To those who look at the rich material provided by history, and who are not intent
on impoverishing it in order to please their lower instincts, their craving for intellectual security in
the form of clarity, precision, ‘objectivity,’ ‘truth,’ it will become clear that there is one principle …
anything goes” (Feyerabend, 1975, P. 18). Feyerabend’s indeterminacy goes beyond current
evidence—stable, conservative institutions of science have ensured for centuries that not anything
goes—but he defensibly highlights that advance is typically associated with a violation of existing
methodological boundaries. With a wealth of new digital data on science and computational
methods to explore it, we could squander this opportunity to understand science in new ways,
4 Quantification is not always ascendant. Several contemporary biological publications are moving toward
visual abstracts, while biology-related equations may reduce the spread of their ideas in broader biological
Diskurs (Fawcett & Higginson, 2012).
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Exploding the boundary between qualitative and quantitative studies of science
or we could move against method—transgressing the boundary of epistemic cultures for a deeper
collective understanding of the scientific enterprise and our role within it, portending a Renaissance
in science studies.
COMPETING INTERESTS
The authors have no competing interests.
FUNDING INFORMATION
This research was supported by a grant from AFOSR FA9550-19-1-0354.
DATA AVAILABILITY
Data and code for analysis available at: https://github.com/KnowledgeLab/AgainstMethod/.
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Figure A1. Extended comparison of prevalent words using a 1% threshold: Words are reported if they appear more than 1% of the time from
the more frequent corpus in the less frequent one.
Quantitative Science Studies
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Exploding the boundary between qualitative and quantitative studies of science
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Figure A2. Rank order of all words in Figure 2, projected onto the evaluative (“good”/“bad”) dimensions inscribed by qualitative and quantitative
science studies articles.
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