1

 

Formal  Methods  of  Cultural  Analysis  

Draft  9/9/14

John W. Mohr and Craig Rawlings

(Word Count = 11,715)

(Forthcoming) International Encyclopedia of the Social

and Behavioral Sciences, 2nd edition. James D. Wright (ed.) Elsevier

Abstract: Our focus is on methods of cultural analysis, and specifically on those methods that are

formal in the sense that they rely upon the purposeful gathering (or simulating) of cultural data

and a systematic analysis that involves at least some mathematically based technique. The

meaning of culture is more complex, as scholars have understood cultural phenomena in very

different ways across the decades and across the disciplines. These different goals in cultural

analysis give rise to different types or styles of formal methods of cultural analysis. We briefly

review the history of these methods in the social sciences and finish with a description of some

of the main arenas within which formal methods of cultural analysis are being deployed and

actively developed today.

1.  What  is  a  formal  method  of  cultural  analysis?  

We describe developments in the use of formal methods of cultural analysis in the social

sciences, focusing on approaches that rely upon the gathering (or simulating) of data concerning

matters of culture. By this classification, it is precisely the use (either ultimately or potentially) of

formal measurement practices and the construction of a data set that can be analyzed according to

logical, relational or other quantitative methods that define an approach to cultural analysis as

formal in the sense of the term used here. We begin our essay by describing different types or

styles of formal analysis of cultural data. We then review some of the history of how these

methods have been used for analyzing culture in the social sciences. We finish with a description

of a few of the main arenas in which formal methods of cultural analysis are currently being

developed and deployed by research scientists.

2

Our title indicates a focus on formal methods of cultural analysis (suggesting a survey of

techniques) but a more accurate description would be to say that we focus here on alternative

research programs for studying culture (each of which includes a shared tradition, organized

communities with hierarchal social structures, shared ways of scientific knowing linked with

(articulated into) shared systems and styles of practice, (which also implies immediately that

these are also technological ways of knowing and technological ways of doing (see Bourdieu,

1990; Pinch, 1986). A focus on methods alone strikes us as less productive since methods are

never really neutral. For one thing, there are many examples where the same styles of data

collection (e.g., interviewing or surveying) or the same styles of analyzing data (network analysis,

linear modeling or Boolean algebras) have been deployed in the service of quite different types of

research goals.

The meaning of culture is inherently difficult to pin down, as scholars have understood

cultural phenomena in quite different ways across the decades and across the disciplines, a topic

that we take up here as well. As a start, recall that Raymond Williams (1977) traced the origins

of the concept of culture from its early roots referring to “the growth and tending of crops and

animals, and by extension the growth and tending of human faculties” (p. 11) through a series of

changes and developments. With the rise of Romanticism, Williams notes that culture takes on a

new meaning as something set against the character of modern life “as distinct from or actually

opposed to ‘civilization’ or ‘society’ in its new abstract and general sense …‘culture’ as a general

process of ‘inner’ development was extended to include a descriptive sense of the means and

works of that development: that is, ‘culture’ as a general classification of ‘the arts,’ religion, and

the institutions and practices of meanings and values” (p. 14-15). Williams identifies a second

break in the concept of culture that he traces back to the original (18th century) insights of

Giambattista Vico and Johann Gottfried Herder on culture as styles of life which then emerge

full-blown in the 19th century associated with the idea that “a fundamental social process which

shapes specific and distinct ‘ways of life’ is the effective origin of the comparative social sense of

culture and its now necessary plural ‘cultures’” (p. 17). While the theory of culture has changed,

in some ways this age old split in thinking about culture still resonates today with how formal

methods are applied to the study of culture today. On the one hand, a focus on culture as

primarily concerned with domains of creativity and the arts, and on the other, those who would

instead emphasize the study of culture as a “way of life” founded perhaps on core patterns of

understanding, believing, valuing or moralizing. In this case, as Clifford Geertz offered a

definition of culture as “an historically transmitted pattern of meanings embodied in symbols, a

system of inherited conceptions expressed in symbolic forms by means of which men

3

communicate, perpetuate, and develop their knowledge about and attitudes toward life” (1973, p.

89).

For analytic purpose we distinguish between two types of formal research programs for

studying culture. The first type, which we classify as interpretative or hermeneutic styles of

cultural methods have as their goal advancing our ability to know or to understand a shared

cultural meaning or form of collective understanding (or to simply assist in providing a reading of

a cultural text). These are methods that are often grounded in views of culture that see systems of

shared meaning (or core logics of understanding) as being what most deeply anchors an effective

cultural analysis. Methods of content analysis are one example of a style of cultural research

programs that are frequently associated with hermeneutic research goals. Hermeneutic style

projects tend to focus on speech activity (including texts) and their formalisms are generally

geared toward helping the analyst to make sense of the speech data, system of discourse or other

forms of cultural expression. Here, cultural meanings can include larger types of shared cultural

patterns (like ideologies, fields and hierarchies of artistic tastes or institutional logics (Friedland

and Alford, 1991; Geertz 1964; Swidler 2003; Thornton, Ocasio and Lounsbury 2012; Wuthnow

1989). But they might just as well be directed at smaller groups or organization level meaning

systems, including those that Gary Fine calls idiocultures (Fine, 1979) or what Harrison White

describes as netdoms — conjunctures of identities, networked together in a time and a place, with

a foundation in shared collective meanings, ways of talking together and repertoires of stories.

White says that this “presupposes the mixture of relation and topic, plus understanding” (White,

2008, p. 7).

We classify other forms of measuring culture as non-hermeneutic. Here the goal is to

measure aspects or traces of cultural forms, and then to use those measures as indicators (or

factors) for understanding or explaining other social phenomena. One might also say that the goal

of a non-hermeneutic program of cultural analysis is at least potentially a simpler approach to

measurement since it comes without the added ambition of needing to also decipher the

hermeneutic meaning of the cultural data that has been collected. DiMaggio sums it up, “Nothing

is more central to the study of culture than the interpretation of meaning, yet nothing is more

difficult” (DiMaggio, 2011, p. 294).

2.  Some  History  

A. Early uses of data to measure culture

4

As states and state apparatuses grew, so too did their capacities and appetites for the

gathering of systematic data on national, regional and local populations (for taxation,

conscription, tariff collection, and other administrative functions). They also developed measures

of “economic” indicators — the money supply, forms of wealth, the circulation and availability of

commodities, etc. (Porter, 1986; Spengler, 1961). These endeavors were constitutive of the basic

discursive, professional, and organizational cultures and informational infrastructures that

facilitated the rise of modern states, political economies, and systems of surveillance. As this data

was collected, so too were social scientists developing new mechanisms for measuring the types

and characteristic qualities of the people who made up these nations (Foucault, 1970; Fourcade,

2010; Hacking 1975, 1990; Lazarsfeld, 1961; Porter, 1986; Stigler, 1986). And as soon as these

official statistics were being gathered, researchers began to make creative use of those measures

to describe and analyze matters of culture.

In his classic review of the origins of quantification practices in sociology, Lazarsfeld

(1961) recounts numerous efforts by pioneering social scientists to take the measure of norms,

values, moral sentiments, and systems of shared meanings. Lazarsfeld (1961) dates the origins of

modern demography to the work of Graunt in 1662. Spengler (1961) tells us that estimates of the

supply of money were being made as early as 1694. State data efforts in this era were, however,

spotty, unreliable or altogether unavailable (in no small part because local populations were

properly suspicious of state enumeration efforts). For many years, clever estimation strategies

were relied on. As Lazarsfeld explains, “(T)he ingenuity of early scholars was directed mainly

toward obtaining estimates of population size and age and sex distributions from meager and

indirect evidence. Multiplying the number of chimneys by an assumed average family size or

inferring the age structure of the population from registered information regarding age at the time

of death were typical procedures in what was then called political arithmetic” (1961, pp. 149-

150). But it was not only demography that was being estimated. The specialists in these arts, the

political arithmeticians, prided themselves on showing the relationship between national

populations (which were thought to express very specific characters), the nature of particular state

administrations (which were often viewed in highly personalized terms), and the principles of

good governance (which were a curious mix of theistic moralities and Aristotelian inspired proto-

scientific rationalities). In the process, topics regarding culture were mixed in with demographic

facts, mercantilist tallies, and pseudo-sociological folk theories (often built around rhetorics of

national historical dramas) (Lazarsfeld, 1961, Porter, 1986).

5

Elsewhere Lazarsfeld sums up what he sees as the main trend in the early years of culture

modeling by explaining that in the mid-nineteenth century, “Le Play assessed the religious

feelings of the families he studied by examining their budgets and noting how much money they

spent on candles for church masses. It was objected that church attendance might be a better

indicator of the religious feelings Le Play sought to measure, and thus slowly the notion of time

budgets – in addition to money budgets – developed. It was then further argued, that perhaps

church attendance is indicative only of conformity to social customs; it is the attitude toward

religion which really matters. And so attitude measures began their triumphant course”

(Lazarsfeld 1970, p. 65).

These early efforts at measuring culture have a distinctly Durkheimian flavor. The

importance of culture is its role in the collective conscious – or the social glue that provides

cohesion through shared beliefs and sentiments. Largely absent are the conscious deliberations

over various interpretations of cultural objects, as well as more complex systems of meaning in

which cultural processes unfold. Culture is subsumed by social structure, while the structures of

cultural systems are not an empirical concern. It would take some time for culture to escape the

confines of this larger functional edifice.

B. Kroeber’s analysis of fashion trends

An early example of a social scientist who sought to develop a precise set of formal

measures of cultural forms was Alfred Kroeber (1919) who studied changes in women’s dress

fashions. Kroeber systematically coded a sample of images from a collection of women’s fashion

magazines (measured annually from 1844 to 1918). By taking basic measurements of the models

and their gowns (the length of the figure, length of dress, width of dress at hem, width of dress at

waist, etc.) and analyzing these data across time Kroeber found some clearly visible and

apparently cyclic trends in women’s dress fashions. In one graph, for example, he shows the

quick and unidirectional growth in the width of the skirt, neatly capturing the fashion in crinoline

hoop skirts out to an apex, followed by a steep decline (save for a short sartorial diversion by way

of a the widening of trains on the dresses). The data that are used for these analyses are very

simple measures of size and ratio in women’s evening fashions. This is cultural data but it is not

hermeneutic data. In other words, whether meanings are ultimately at work in determining what

goes on here or not, what was actually being recorded as data by Kroeber are just a simple set of

measures assessing the geometric dimensions of the cultural forms (evening dresses), measures

that turn out to be useful for illustrating increments of change in cultural forms over time.

6

Kroeber’s work is, in this sense, a classic and early demonstration of how non-hermeneutic

measures of culture could be collected and usefully analyzed.

Stanley Lieberson (2000) finds a similar type of effect (80 years later) when he studies

baby names. Lieberson calls this the ratchet effect. The cycle is explained by the idea that tastes

involve establishing a distinctiveness, a difference from less valued taste categories and that in a

system such as women's fashion, part of what is valued is the new (as distinct from the old) and

thus an important marker of the new is the length of the dress. If hem lines don't change, then the

newness of the fashion is harder to perceive. So they must be different than last year's hem line.

But if they go backwards, to the length of the previous year then again there will be a danger that

the new will be confused with the old, with the really old– that is, with the passé. As a result the

hem has to continue moving in the direction it has been moving in. And this continues until the

hem hits an obstacle (the floor or some measure of moral propriety) at which point it has to

reverse itself.

Lieberson (2000) shows that given names also exhibit the ratchet effect. Names turn out

to be a good source for cultural data because they are collected very systematically (e.g., in birth

records) and because they have increasingly become defined as free choices and thus they are

subject to the vagaries of style. Names become appealing to a lot of people at the same time. As

this happens the name becomes more and more popular until such point that a given name will

become too popular. As an explanation, Lieberson offers a basic thought experiment. Suppose

you are about to name your child and you were searching for a name that appealed to you. And

you hit upon a certain name. But you looked about you and you saw lots and lots of people with

that name. So many people that you wanted your child to be different, to be special, to be given

their own more unique name. And so you pick something else.

Lieberson describes both endogenous and exogenous effects. The former focus on the

means according to which taste moves. This depends on a largely tacit set of cultural affinity

processes. Choices happen because within the sea of available names, some are in one’s mind,

others aren't. Some sound nice to one’s ear and others don't. Some seem like good things to

choose and others don't. Culture in the end is about how the self is subjectively tuned into a

complex, flowing field of cultural meaning systems. The choice of a child’s name reflects how

some social constellation of influences, embedded within an interlocking set of family, friend,

and significant other networks, usually including a fantasized network of media personalities, are

affected by the rise and fall of aesthetic appeals, and the ebb and flow of identity dilemmas and

associations, all of which weigh down upon the moment when a child is announced to have a

given name. Compare this with the theory of culture held by Kroeber. Like Durkheim, Kroeber

7

was trying to demonstrate institutional effects, seeking to show that there are things that occur in

cultural life that operate at a level which is not just at the experience of a particular individual.

But unlike Lieberson, he has little to turn to as a means of explanation. And, in the end, when he

ponders about what might account for the regularities of culture he suggests, “The super-organic

or superpsychic or super-individual that we call civilization appears to have an existence, an

order, and a causality as objective and as determinable as those of the subpsychic or inorganic”

(p. 263). In this sense, going beyond Durkheim, Kroeber creates a semi-autonomous realm in

which cultural processes unfold according to a guiding internal logic. The value placed upon

“newness” may derive from broader institutional contexts, but the cultural manifestation of this

value is guided by the logic of an inter-related set of symbols and their behavioral correlates.

C. Lévi-Strauss on Formal Methods for Interpreting Myths

Contrast Kroeber’s ambitions with those of the French anthropologist, Claude Lévi-

Strauss. Lévi-Strauss is famous for his contributions to the original development of structuralism.

Coming to the field a generation after Kroeber, Lévi-Strauss made a huge impact on anthropology

by applying the principles of structural linguistics (and formal semiotics) to non-linguistic matters

of culture. Following in the path originally pioneered by Swiss linguist Ferdinand de Saussure

and later by structural linguists such as Roman Jakobson (whom Lévi-Strauss met in New York

during World War Two while teaching at The New School), Lévi-Strauss used structuralist

principles to analyze anthropological data on kinship structures and myths (Lévi-Strauss, 1963).

Kinship structures are the single most important institution in traditional societies and Lévi-

Strauss began his career by showing that structuralism provided a new way of interpreting the

logic of kinship systems. Later he turned to the study of myths in traditional societies and it was

especially here that we can see how Lévi-Strauss provides an early example of a social scientist

seeking to use formal methods of analysis to approach what was an explicitly hermeneutic (or

interpretive) research goal.

For Lévi-Strauss, the goal was to understand how the interpretation of a myth can be

facilitated by analyzing the deeper relational structures (the bundles of relations linking concepts

and actions within the myth). Lévi-Strauss saw that these structures provided an order for the

content and the meaning of the myth, and ultimately it was the structural logic that made the

whole greater than the sum of the parts. Specifically, Lévi-Strauss looked to develop analogues

to the way that linguistic structures worked in language. Rather than investigating the

“morphemes” of language structure, however, Lévi-Strauss gathered up and analyzed the

8

“mythemes” (or “gross constituent units” of meaning) that make up the content elements of

myths.

In his essay on “The Structural Study of Myth,” Lévi-Strauss illustrated this approach by

deconstructing the Oedipus myth. Lévi-Strauss’ technique depends on breaking the myth into

simple sentences (or “statements”) and then looking for commonalities in the semantic functions

that each sentence performs. In the case of the Oedipus myth, the statements are semantically

(and thus structurally) divided into common bundles of relations. Thus Lévi-Strauss’s

interpretation of Oedipus: “The myth has to do with the inability, for a culture which holds the

belief that mankind is autochthonous [born from the earth], to find a satisfactory transition

between this theory and the knowledge that human beings are actually born from the union of

man and woman. Although the problem cannot be solved, the Oedipus myth provides a kind of

logical tool which relates the original problem— born from one or born from two?—to the

derivative problem: born from different or born from the same? By a correlation of this type, the

overrating of blood relations is to the underrating of blood relations as the attempt to escape

autochthony is to the impossibility to succeed in it” (p. 216).

Lévi-Strauss’s approach is hermeneutic in the sense that his goal is to employ this style of

cultural analysis to facilitate his interpretation of the meaning of the myth. It is formal because he

gathers up data about the content of myths, he measures the distribution of the mythemes and the

relations between them, and he provides us with an analysis (based on a system of Boolean

transformations) that provide us with a plausible reading of one meaning of the myth. It is

explicitly through his linking of the analysis of patterns in his collected data to his research goal

of interpreting the meaning of the myth that Lévi-Strauss provides what is an especially useful

example of a hermeneutically oriented approach to formally measuring cultural data. But, as

Pierre Bourdieu would later show, Lévi-Strauss’s structural approach to culture leads to a largely

disembodied mapping of meaning, overlooking the important lived territories and material

conditions that shape meanings and give rise to various symbolic struggles.

D. Developments of Cultural Measurements in sociology through community studies

and social work theory

In the early years of professional social science there were a variety of quite different

programs of research that sought to develop formal measures of culture. One stream of work

developed out of the efforts to conduct neighborhood surveys such as those that were being used

by Charles Booth to study the London slums in the late 1890’s. These ideas were later applied (in

9

1896) by W.E.B. Du Bois in his survey of a black neighborhood of Philadelphia which was

followed by the Pittsburgh survey of 1907 (and dozens of other city surveys that came after that).

These were intensive collections of data, even though the procedures were not the same as what

we might imagine when we think of a modern, systematic, door-to-door canvassing with a survey

questionnaire. “Rather it was an effort to provide an inventory and an overview of the state of the

city, for which the investigators were omnivorous in their methods of data collection” (Converse

2009 [1987]: 24). Much of the data came in the form of the schedule, which “in the hands of the

social surveyors was an instrument for making observations or for conducting interviews with

respondents, or a mixture of both” (Converse 2009 [1987]: 34). With funding from the Russell

Sage Foundation, the Pittsburgh initiative had seventy-four field staff compiling a mass of

detailed information about all kinds of social phenomena. For example, the checklist for

observing clothing worn by inhabitants included categories for, “spotted… dusty… torn…

worn… patched… mussed… wrinkled…” (p. 34). Researchers compiled information by

collecting “case–history interviews, which were gathered and then counted and compared among

some dimensions, thus providing a ‘casemounting’ that represented a merger of the case study

and statistical methods” (p. 34). But, as Bulmer points out, “the Survey used quantitative data in

more of an exploratory and descriptive than analytic way” (1996, p. 18). Here and elsewhere, “It

was as if there was an intuitive sense of the value of collecting extensive data about individuals in

the population being studied, without the necessary knowledge either about sampling or how to

handle the data once collected other than to compute simple counts of characteristics and then

treat respondents on a case-by-case basis” (p. 26).

A different force for developing measures of culture came from another wing of the

professional social work community. While community workers (including settlement house

workers) had been visiting and surveying slum residents since the mid 1880’s, by the turn of the

century social workers were becoming more professionalized, and this included a strong push to

rationalize and quantify social workers’ field observations and to use those data in productive

analytic ways. From her post as director of the Charity Organization Department at the Russell

Sage Foundation, Mary Richmond (1917) sought to promote a style of professionalization in the

field of social work that was explicitly modeled after the medical profession. Social workers

were encouraged to gather systematic data from their cases in order to create a coherent system of

data about behavior, character and culture that could be used for diagnosing and treating the

social pathologies that lead to poverty and ruin.

Meanwhile, professional sociologists were also studying communities. One of the most

important of the early studies was the research collaboration between W.I. Thomas and Florian

10

Znaniecki (1918) concerning the lives of Polish immigrants. Drawing particularly on letters

written to and from family members in Poland, they emphasized the utility of using these types of

documents for sociological research. “We are safe in saying that personal life-records, as

complete as possible, constitute the perfect type of sociological material, and that if social science

has to use other materials at all it is only because of the practical difficulty of obtaining at the

moment a sufficient number of such records to cover the totality of sociological problems, and of

the enormous amount of work demanded for an adequate analysis of all the personal materials

necessary” (Thomas and Znaniecki 1958: 1832, as cited by Łuczewski, 2009).

Using these materials, Thomas and Znaniecki combined their intellectual interests.

Thomas was mostly focused on attitudes while Znaniecki was concerned with values, but together

they produced a solid grounding and advance for social science’s understanding of how to

analyze cultural forms. Łuczewski (2009) points out that for Znaniecki “The great mistake […]

was to treat the problems of values […] as ultimate and self-sufficient, instead of taking them

only as starting-points of future investigations”. Thus he described values on two levels: content

(ability to become an object of experience either sensual or imaginative) and meaning … In his

relational perspective, meaning of a given value depended on its relations with other values.

Simply put, the meaning is the relation (2009, p. 8). These studies helped early on to situate the

formal analysis of culture within the lived experience of groups seeking to use culture to achieve

practical ends. This more Pragmatic view of culture, and thus the reasons for measuring cultural

artifacts, resurfaces much later in the work of Pierre Bourdieu, which we discuss below.

E. Surveying Values, Attitudes and Opinions

The invention of attitude measurements provided the means to use sample surveys to

measure cultural content systematically. Not long after Thomas and Znaniecki published their

work (1918) both Floyd Allport and Emory Bogardus published independent methods for

formalizing the study of attitudes through the use of “attitude scaling.” Later, Thurstone, Likert,

and others produced rapid advances in the use of social surveys for measuring subjective

experience, including cultural orientation, values, systems of meanings and beliefs, and subjective

understandings of social situations (Converse 2009 [1987]). These developments coincided with

the emergence of independent polling bureaus and university centers that conducted independent

surveys, as well as the growing sophistication of population sampling and statistical methods for

gauging significance and for interpreting scales measuring subjective experience. This includes

11

cultural orientation, values, systems of meanings and beliefs, and subjective understandings of

social situations (Converse 2009 [1987]).

World War Two was a critical juncture. It brought academics together into large, multi-

disciplinary, state-funded research teams. In the case of survey research, Samuel Stouffer led a

team of social scientists who surveyed over a half million U.S. Military personnel through the

war years. Surveys asked about complicated matters like troop morale, racial integration, and

officer performance. By the end of the war, survey methodologies had been improved and their

legitimacy had been secured. Over the course of the next two decades, social scientists like Paul

Lazarsfeld (Director of the Columbia Bureau for Social research) helped to build up the

conceptual, theoretical and practical foundations of modern social survey science, writing essays

and commissioning papers about how to construct effective survey questions, how to use the data

to build variables, how to use cross-tabulations to compare variables, how to assess statistical

significance and to run regression models, eventually establishing survey research as the new

“language of empirical social research” (Lazarsfeld 1968: vii).

Survey research has continued to improve since those years, developing into what is the

social sciences’ most mature and essential means of collecting data about culture and social life

(Schuman, 2008). In recent years, survey analysis methodologies have been undergoing a

renaissance as new scholars have come to the field and challenged traditional assumptions. Steve

Vaisey (2009) helped to bring the dual-process model of information processing over from

cognitive science, arguing that much of how people operate is indeed non-reflexive and driven by

habit, but that other parts of human cognition is active and deliberative and that we should use our

surveys more strategically to unearth these effects. Vaisey and Lizardo (2010) applied the dual-

process model to look at how people’s world views impact the character of the types of social

networks that they form. Amir Goldberg’s (2011) development of relational class analysis is

another example of recent innovations in survey research. Instead of comparing respondents on

the basis of having similar attitudes, relational class analysis emphasizes comparing respondents

that have similar beliefs about the relations between attitudes.

3.   Some  Contemporary  Approaches  to  the  Formal  Analysis  of  

Culture  

A. Measuring Meanings with Cognitive Science

12

Other areas of social science have also focused on the formal analysis of culture and

meaning. In 1957 Charles Osgood (and his first two graduate students, George Succi and Percy

Tannenbaum) published The Measurement of Meaning. The monograph introduced and

demonstrated their efforts to use “the semantic differential” as a tool for formally measuring the

connotative aspects of word meanings. Their basic idea was to present respondents with a key

word (such as “father”) followed by a series of paired opposites presented as 7 point scales,

happy/sad, hard/soft, slow/fast, and the respondent is asked to rank their association of the key

word to each of the scales. Osgood and colleagues explain, “The semantic differential is

essentially a combination of controlled association and scaling procedures. We provide the

subject with a concept to be differentiated and a set of bipolar adjective scales against which to do

it” (1957, p. 20). Factor analysis was used for simplifying the data. Of course a critical issue was

identifying the right paired oppositions and these types of matters occupied Osgood and his

colleagues for many years. Nonetheless by the time the Snider and Osgood volume, “Semantic

Differential Technique: A Sourcebook,” was published in 1969, the editors could report that “the

semantic differential technique has gradually captured the imagination of psychologists…in

recent years, hundreds of articles using the technique have appeared in professional journals”

(1969, p. v). One direction these studies moved in was in the application of semantic differential

tools to comparative studies of cultural meaning.

Much of the research by cognitive scientists tends to focus on the more cognitive and or

biological sides of cognitive processes. But these types of tools were quickly embraced by one

group of anthropologists who had an explicit focus on culture, a story that is effectively told by

one of the field’s leaders, Roy D’Andrade (1995). About the time that Osgood and his colleagues

were preparing to publish their book on meaning measurement, a pair of anthropologists, Floyd

Lounsbury and Ward Goodenough, were publishing two new papers on the use of componential

analysis, a method that was used to break down and analyze the core meanings of kinship systems

(1995, 21). As D’Andrade explains, “The reason that the kinship terminology papers by

Lounsbury and Goodenough had such a large impact was that they presented a rigorous method

for identifying “idea units” and analyzing the organization or structure of these units. While

developed specifically for the analysis of kinship terms, it appeared that the general principles

involved in identification and analysis could be extended to other domains. It was this body of

methods and goals which was to become the agenda of cognitive anthropology” (1995, p. 17). By

the mid-1960’s this field was growing. D’Andrade recounts the move from formal studies of

kinship terms to a variety of studies of language terms (such as words for firewood) and from the

13

use of simple schema toward more elaborate models for measuring cognition. DiMaggio (1997)

provides an account of how studies in cognitive science intersect with research on culture.

B. Content Analysis

While the careful and ordered reading of texts goes back many centuries through various

traditions of hermeneutics, the systematic treatment of texts as data in the more formal sense that

we have employed in this essay is relatively new. Harold Lasswell was an important contributor

to these developments. After having studied the use of propaganda during the First World War

for his doctoral dissertation at the University of Chicago, Lasswell was well prepared to step into

the position as director of the ‘‘experimental division for the study of wartime communications,

established at the Library of Congress during World War II’’ (Lasswell and Leites, 1949;

Lasswell et al., 1952, p. 40). In this position he helped devise rigorous sets of procedures for

coding textual material in a way that was intended to measure cultural and group level discourse

processes. After the war, Lasswell used these methods to coordinate a study of a corpus of

20,000 newspaper editorials sampled from ten major newspapers (in five countries) between the

years of 1890-1945 during which many of these conventions and strategies were published

(Lasswell, Lerner, and Pool, I.d.S., 1952).

Content analysis tools continued to improve. Philip Stone and colleagues at Harvard

(1966) developed the General Inquirer which was a general purpose computer based content

analysis program that relied upon the building of specific dictionaries for various focused

investigations. By the 1980’s sociologists like Roberto Franzosi (1989) were using semantic

grammars as a way to code the meaning of texts. These formal methods and project styles come

together especially well in John Markoff’s (1996 [2004]) analysis of the origins of the French

revolution (which builds on collaborative work done with Gilbert Shapiro). Drawing upon rich

textual material preserved in the cahiers de doléances, (the official lists of grievances drawn up in

1789 by rural communities, and others), Markoff is able to follow the flow of discourse back and

forth between the legislators and the peasants across both time and space. Here the texts are

interpreted with the help of formal tools and the lessons that are learned imply changes in how we

now understand the role of the peasantry within the system of discourse and political maneuver

around the years of the revolution.

One of the most productive off-shoots of the content analysis tradition has been in the

area of scientometrics – that is, a branch of formal analysis concerned with the sociology of

knowledge and its measurement in research publications. A key component of this lineage is

14

citation studies as represented, for example, in the many achievements of Loet Leydesdorff

(2001) and his colleagues. This perspective also reflects an early blending of text analysis and

network analysis, as authors, articles, and even journals are analyzed through the networks they

form in co-authorships and co-citations. Still, in a number of ways, research projects in this field

are being supplanted by new problems and new methods coming from the developments in

computational analysis that are now being incorporated into fields like sociology, linguistics, and

the humanities (see below).

C. Cultural Capital Studies

In a sense the French sociologist Pierre Bourdieu was foundational to the modern use of

formal methods of cultural analysis in several important ways. One way was in the stream of

research concerning the role of culture in social stratification and especially research surrounding

the concept of cultural capital (Bourdieu and Passeron, 1977). The original insight was linked to

the argument that participation in forms of high culture was a mechanism by which elite

households were able to pass along hidden advantages to their children. Children from privileged

households are inculcated with elite styles of cultural knowledge and forms of practice that enable

them to display the required levels of what Bourdieu and his colleagues call cultural capital. The

key insights here — that culture was a resource, one that was associated with mechanisms of

class reproduction, and that these forms of cultural mastery could be identified using standard

survey techniques proved to be a powerful catalyst for new streams of measuring culture. Paul

DiMaggio borrowed the notion of cultural capital from Bourdieu and used a factor analysis to

construct a set of measures of cultural capital that proved to be effective in predicting success in

both educational and marital attainment (DiMaggio, 1982; DiMaggio and Mohr, 1985). Both the

DiMaggio research and the ongoing stream of work by Bourdieu himself led to the production of

a wide stream of research concerning the effects of cultural capital on stratification outcomes in

different countries and across different institutional systems (e.g., DeGraaf, De Graaf, and

Kraaykamp, 2000; Kane, 2000; Roscigno and Ainsworth-Darnell 1999; Van Rees, Vermunt,

Verboord, 1999). One especially influential reformulation grew out of a classic paper on the

phenomena of “cultural omnivores” by Peterson and Kern (1996) showing that increasingly, high

status culture consumers focused less on specializing entirely on exclusive cultural tastes and

instead began to acquire a broad set of cultural engagements.

More recently this field of work has morphed again as a range of new projects on

measuring the relationship between culture and social structure have begun to take center stage.

15

Omar Lizardo (2006), for example, using data from the General Social Survey, finds that

measures of cultural tastes predict an individual’s network style. Specifically, individuals with

higher levels of culture participation are found to be more likely to have a greater number of

strong network ties whereas engagement with popular culture tends to be associated with larger

weak-tie nets. The general sense here, as articulated in an important essay by Pachucki and

Breiger on “cultural holes” (2010), is that scholars are increasingly coming to pay attention to

how it is that culture acts as a general social resource that leads to the creation of things like

social networks, thereby inverting the more traditional assumption that it is networks which are

primary and culture that is epiphenomenal.

D. Field theory

A second important legacy of Bourdieu’s research program is the recent growth in what is known

as field theory. The idea of describing social situations as being defined by “field effects” goes

back to several sources. The Berlin Gestalt psychology made famous the notion of fields in

structuring perceptions. In social psychology, Kurt Lewin was instrumental in developing models

of how to formally model social fields. Lewin himself drew upon the time he had spent with the

Gestalt scholars and also the German philosopher Ernst Cassirer during his years at the University

of Berlin. Borrowing also from physics and especially Einstein’s conception of fields, Lewin

(1951) sought to measure cultural phenomena by applying the idea of a field to an understanding

of socially constructed interactional space. Much of his work drew upon studies of children’s

behavior conducted during his years as director of the Child Welfare Research Station at the

University of Iowa.

Lewin began to use the field concept to construct models of an individual’s “life space”

which he described as “the person and the psychological environment as it exists for him”

(1951:57). These were essentially cognitive maps of individual’s choice situations. They included

“specific items as particular goals, stimuli, needs, social relations, as well as more general

characteristics of the field as the atmosphere (for instance, the friendly, tense, or hostile

atmosphere) or the amount of freedom.” The definition was pragmatic. The life space included

within it “everything that affects behavior at a given time” (1951:241). The key goal for Lewin

was to bring a level of formal analysis to the study of social psychology. Field theory worked

well because of the type of mathematics that defined it. Inspired by the work of the German

philosopher Ernst Cassirer on relationalism vs. substantialism, Lewin was convinced that life

spaces were essentially relational systems. Objects in the life space stood in particular relation to

one another but their location could not be defined in precise metric terms. It was impossible to

16

specify precise dimensions, linear measurements, or definable coordinate systems within which

objects could be located inside an individual’s life space, but field theory provided a way to

measure these things in the purely relational sense of topology theory. Extensions of Lewin’s

ideas about measurement theory found their way into modern social network analysis which is

like Lewin’s field space in that it is based on purely relational measures (Mohr, 2013).

Later in his career, Pierre Bourdieu began to focus on the study of fields, a term he used

to designate “relatively autonomous social microcosms” corresponding to regions of institutional

life. For example, Bourdieu studied the fields of art, academia, religion, and law. Each field is

understood to be defined by a set of social relationships and a shared collective understanding

about the meaning of what goes on inside the field or, as Bourdieu puts it, each field consists of

“spaces of objective relations that are the site of a logic and a necessity” (Bourdieu and

Wacquant, 1992, p. 97). Like Lewin, a primary reason that Bourdieu adopts a field approach is

that it made it easier to theorize and measure social life and especially matters of culture. As

Bourdieu explains with respect to his preference for correspondence analysis models, “if I make

extensive use of correspondence analysis, in preference to multivariate regression for instance, it

is because correspondence analysis is a relational technique of data analysis whose philosophy

corresponds exactly to what, in my view, the reality of the social world is. It is a technique which

‘thinks’ in terms of relation, as I try to do precisely with the notion of field” (p. 96). Bourdieu

(1984) famously employed correspondence analysis techniques in his studies of the association

between social class and cultural tastes in France.

Field theory therefore brings together the embodied agency of actors and the

meaningfulness of relational systems that enable and constrain action. The formal analysis of

culture – the arraying of categories and identities in some relational space defined by those

participating in these cultural forms – therefore reveals a state of the struggle for positions and

dominance according to the underlying logic of a field. Inspired by Bourdieu’s research, a

number of other areas of social science have spawned studies of field. Institutional theorists in

organizational studies have focused on studying fields (Powell and DiMaggio, 1991; Fligstein

and McAdam 2012; Martin 2003; Padgett and Powell 2012). Within this group, a strong

emphasis on cultural analysis has emerged among those who study organizational fields as being

fundamentally grounded in a system of meanings described as institutional logics (Friedland and

Alford, 1992; Friedland, Mohr, Roose and Gardinali, 2013; Thornton, Ocasio and Lounsbury,

2012).

E. Networks and Culture Perspective

17

Paul DiMaggio argues that “network analysis is the natural methodological framework

for empirically developing insights from leading theoretical approaches to cultural analysis”

(2011, p. 286). He goes on to suggest that “Social scientific work on culture has three analytic

foci: formally organized systems that produce and distribute cultural products; expressive

symbols that facilitate the production of individual and group identities and intergroup

boundaries; and the symbolic organization of meaning. Relational theories are central to each of

these topics” (DiMaggio, 2011, p. 286).

The interest in relationality comes up again and again in these research projects and this

is partly a reflection of the common lineages of the work. Jacob Moreno observed network effects

in his study of small groups during the 1930s, providing the first real example of modern network

analysis. But Kurt Lewin’s work on field theory was also connected to the emergence of modern

network science. One link occurred through the work of Lewin’s student Alex Bavelas (1950)

who applied ideas from Lewin’s hodological analysis of field space to assess the properties of

networks. Later, Lewin’s ideas were recast in graph theoretic terms by Dorwin Cartwright,

Lewin’s successor as Director of the University of Michigan Research Center for Group

Dynamics (Harary, Norman and Cartwright, 1965; Mohr, 2013). Network theory also has deep

connections with formal traditions of kinship analysis that were being conducted by

anthropologists (Breiger, 2004). Indeed, Harrison White who went on to become the leading

figure in social network analysis in American sociology during these years, started out his career

in sociology in part due to his fascination with Lévi-Strauss’s work on kinship algebras (White

1963).

In its early years (1960’s-1970’s) the sociological field of network analysis was explicitly

anti-cultural in its approach, the feeling being that network ties were material, concrete and

measurable while culture was none of those things. But by the early 1990s that sentiment had

begun to change. Harrison White made a pivot toward culture when he published the first edition

of Identity and Control (1992) in which he lays out an expansive theory of social life that relied

quite explicitly on theorizing the intersection of culture and networks. Karen Cerulo (1988) had

already been working on matching semiotic theory with the empirical analysis of data from

cultural artifacts that included visual symbols and written music. A number of other scholars had

begun merging social network methodologies with the analysis of cultural data. Carley (1994)

used network models to analyze the meaning structure of science fiction novels, Bearman and

Stovel (2000) used network graphs to analyze narrative structures in Nazi party members’ life

stories, and Smith (2007) did something similar with people’s narratives about conflicts they have

18

lived through in Istria. Network analysis reveals how some narrative elements can link disparate

narrative structures together as a kind of cultural bridge.

Much of this work makes use of two mode data, meaning that relationships are examined

that span across two categories or domains of things — relations that link individuals and groups,

members and committees, or categories of persons and categories of treatments. Breiger (1974)

did the original work on this in his development of a formal model for analyzing the linkages

between individuals and groups as a structural duality. These types of cultural models were then

used by Mohr and Duquenne (1997) when they analyzed the implicit institutional logic of 19th

century poverty directories with Galois lattices to assess how organizations matched categories of

poor with poverty treatments regimes. Mische and Pattison (2000) also used lattices as a way to

analyze the duality between Brazilian youth organizations and their projects which they define as

"evolving, imaginatively constructed configurations of desired social possibility, accompanied by

an implicit or explicit theorization of personal and~or collective capacity to act to achieve that

possibility." Yueng (2005) uses lattices to compare members of different communes according to

the types of ways that commune members characterize their relationships with one another.

Martin (2000) compares the kinds of animals and the types of occupations that they hold in

Richard Scarry’s children’s books.

4.   The  New  Computational  Approach  to  Culture  

A. Computational Linguistics

Linguistics has long been more scientifically advanced than the other social sciences in

the sense that more progress has generally been made in understanding the basic rules and

principles of linguistic operations, and much of that is due to the elaboration of sophisticated

theories and methods of structural analysis of language systems. But with the rise of the digital

society, linguistics has also become both a data science (dedicated to processing large amounts of

textual data) and an engineering science focused on trying to create useful technologies.

Whether linguistic projects of cultural analysis are also grounded in hermeneutic styles of

culture analysis depends. In some sense, all of linguistic science has an interpretive goal since it

is all geared toward analyzing and understanding language and speech activity. But on the other

hand, there are also many different levels and styles of understanding in linguistic science.

Syntax tries to account for language use at the level of the sentence while semantics and

19

pragmatics deal with the relation between these lower level language processes and the broader

problems of meaning and language use. Moreover, modern linguistic science has moved well

behind purely structural analysis. As Hajic (2004) notes, “Today we can say that one of the

turning points for linguistics was Claude Shannon’s work (1948)” (p. 80). Shannon provided the

research that lays out the grounding for modern communication theory. It was built upon a long

tradition of mathematical equations that had been developed to manage the engineering of

telegraph signals. Shannon and Weaver introduce their formulation by writing, “The fundamental

problem of communication is that of reproducing at one point either exactly or approximately a

message selected at another point. Frequently the messages have meaning; that is they refer to or

are correlated according to some system with certain physical or conceptual entities. These

semantic aspects of communication are irrelevant to the engineering problem. The significant

aspect is that the actual message is one selected from a set of possible messages” (1949, p. 3).

Shannon’s approach to communication theory has come to provide one foundation for how

engineering researchers such as computer scientists approach the measuring of culture, through

the use of stochastic based models, an issue that turns out to have relevance in other fields of

computational social science today.

Applied computational linguistics today is divided into a number of sub-specialties, “on

the analysis side it deals with phonetics and phonology (sounds and phonological structure of

words), morphological analysis (discovering the structure of words and their functions), tagging

(disambiguation of part-of-speech and/or morphological function in sentential context), and

parsing (discovering the structure of sentences; parsing can be purely structure-oriented or deep,

trying to discover the linguistic meaning of the sentence in question). Word-sense disambiguation

tries to solve polysemy in sentential context, and it is closely related to lexicon

creation…Language modeling (probabilistic formulation of language correctness) is used

primarily in systems based on stochastic methods” (p. 82).

All of these technologies provide powerful tools for social scientists seeking to analyze

meanings in texts. One of the technologies that is already finding fast adoption among social

scientists are topic models. The most important of this class of stochastic models is latent

Dirichlet allocation (LDA) introduced by Blei and colleagues (Blei 2003). This is a method that

provides an automated procedure for coding the content of a corpus of texts (including very large

corpora) into a set of substantively meaningful coding categories called “topics”. The algorithms

work with little or no human intervention. Instead of beginning with pre-defined codes, the

researcher begins by specifying the number of topics for the algorithm to find. The program then

identifies that number of topics and returns the probabilities of words being used in a topic as

20

well as an accounting of the distribution of those topics across the corpus of texts. While not

infallible, when used thoughtfully and applied carefully, the method seems to consistently yield

very plausible readings of the texts, demonstrating what DiMaggio, Nag and Blei, (2013) describe

as high levels of “substantive interpretability.” Natural Language Processing tools are another

class of text analysis tools that should be of interest. Bail (2014) provides a useful overview of the

range of tools from computational linguistics that are of use to social scientists who want to study

culture.

B. Information Sciences

The digitalization of numerous types of texts and archival records has led to the

increasing demand for quick and efficient information search and retrieval – and, thus, revitalized

the interdisciplinary field of information science. With respect to the formal analysis of culture,

information science scholars most closely resemble their colleagues in computer science – that is,

the research program is one focused upon developing automated tools for quickly (and

efficiently) processing and summarizing the content of a large amount of textual data. The

purpose is not to understand culture within a broader theoretical framework of human behavior,

but to create rapid-fire queries that extend scaling and clustering techniques to more readily

handle very large corpora. Salient examples include the work of Katy Börner and colleagues who

have developed a number of appealing visualization technologies applied to texts (e.g. Börner,

Chen, & Boyack 2005). Social scientists have picked up on some of these tools, and developed

their own, in ways that are more theoretically informed. Kathleen Carley and colleagues, for

example, have worked closely across technical and social science disciplines in producing a suite

of tools for optimizing organizational communication and performance. This work is grounded in

network and organizational theories, and can therefore be considered a line of work that is

consistent with paradigms at the center of operations research or management science – that is,

scholars may create more optimal and rational complex organizations. This is done in part by

seeing an organization as a type of machine or system in which an integrated circuitry of contact

and communication is essential for proper goal attainment. To the extent that culture and

meanings (i.e. interpretations) are essential to the day-to-day workings of and long-term learning

within a complex organization, information science scholars play a vital role by developing tools

for quickly retrieving and summarizing such qualitative data through a number of reduction

techniques. Of course, once created, these tools are available for adapting to projects seeking to

build upon theories of culture as it pertains to understanding and predicting human behavior.

21

C. Computational Humanities

In a sense, the humanities were a natural place to bring computing together with the study

of culture. Early work by Roberto Bursa, for example, who, beginning in 1949, began creating

the first computer based concordance of the writings of Thomas Aquinas was carrying out a style

of work which was in many ways a logical extension of the very sort of systematic archival

analysis and linking that biblical scholars had been doing for centuries. Similarly, there was a

natural adaptation of classics studies to the computer analysis of texts, precisely because scholars

in the field were already used to thinking in terms of carefully classified and categorized bits of

information (Crane, 2004). However, the digital humanities movement has really accelerated

over the last decade or so as massive quantities of digital texts began to become available.

An article published in Science by Michel and colleagues (2011) captured something of

the excitement about this new state of affairs. The essay, “Quantitative analysis of culture using

millions of digitized books” drew upon the availability of digitized texts in the Google Books

project. In this case, over 5 million books (in multiple languages) were included in the database, a

number that according to the authors, represents about 4% of all the books ever published. The

corpus contains over 500 billion words and the authors use this material as a means for marking

off what they call the new science of Culturomics, defined as “the application of high-throughput

data collection and analysis to the study of human culture” (p. 181). Although the corpus itself is

extremely large, the actual analysis of these 5 million texts was fairly simple, the authors focused

on identifying the most frequent n-grams (clusters of words) and then tracking their distributions

across time.

There are other contributors to this field who are themselves accomplished humanist

scholars who have begun using the availability of digitalized texts as an opportunity to continue a

more traditional humanistic style of research, but to do so through a decidedly different

observational lens. Franco Moretti, for example, Director of the Stanford Literary Lab, has

recently published a new book entitled Distant Reading, an obvious nod to the traditional style of

literary methodology known as “close reading.” In this book (and in the series of downloadable

SLL pamphlets) Moretti recounts a number of the ways in which he and his colleagues have

effectively transformed the kinds and styles of research questions that they pursue as a response

to the new opportunities of pursuing “distant readings.” This includes, to cite just one example,

the development of automated procedures that can be used to transform a corpus of thousands of

22

dramas and plays into network graphs (in which speakers are connected in these graphs to other

speakers with whom they share direct dialogue). Describing his analysis of Shakespeare’s play

Hamlet using these techniques, Moretti writes, “You see the possibility here: different uses of

language emerging in different network regions. Style, integrated with plot as a function of plot.

It would be a breakthrough, and not just for literary analysis — but for the analysis of culture

more broadly” (p. 229).

D. Computational Sociology

The rise of digital information also is relevant for a variety of emerging new projects

among sociologists interested in measuring culture. Bail (2014) tells us, “More data were

accumulated in 2002 than all previous years of human history combined. By 2011, the amount of

data collected prior to 2002 was being collected every two days” (p. 465).

To take one example, Brian Uzzi and his colleagues (Saavedraa, Hagerty, and Uzzi 2011)

collected information from commercial day traders, a task that they were able to do remarkably

easily given the fact that all of the relevant information about every transaction, including all

email and text conversations with clients, was automatically recorded and stored as a part of the

modern way of conducting business. By measuring the meaning of texts written (using

automated sentiment analysis algorithms) and correlating those measures with real-time

performance measures, every trade made, the value of the trade obtained and the possible value of

that trade, Uzzi and his colleagues are able to directly connect the style and the tone of a trader’s

conversations with the rationality (as measured by money earned) of that trader’s performance.

It is the ready availability of digital data that makes all the difference here. Social

scientists have traditionally been severely hampered by the difficulties of designing, gathering

and analyzing social data. Now clever researchers have the ability to turn toward the simplicity

of our new digital lives to their advantage. For another example, McFarland, Jurafsky, and

Rawlings (2013) take advantage of this in their study of speed dating encounters by using digital

microphones and other types of real-time data gathering devices whose output can be formally

measured and analyzed in order to provide a far richer record of (in this case) the many small

cues, intonations and emotional markers that characterize everyday interactions.

Bail (2014) provides an overview of the types of digital data sources that are becoming

available for use as well as an assessment of the many kinds of algorithmic tools and measures

that computational researchers have developed to facilitate the exploration and analysis of large

scale text corpora and internet network systems. He reports that there are a great many

23

opportunities as well as a number of definite drawbacks to simply making use of computational

tools that have been designed and crafted by scientists and engineers with very different

ambitions and goals. Here we are reminded again of the wisdom of Ernst Cassirer who warns us

that any attempt to measure culture will be dependent upon our conceptual framework for

theorizing culture. Cassirer writes, “Everywhere physical thought must determine for itself its

own standards of measurement before it proceeds to observation…. It is not clocks and physical

measuring-rods but principles and postulates that are the real instruments of measurement.

(Cassirer 1923, p. 364–365).

References

Bail, Christopher A. 2014. “The cultural environment: measuring culture with big data.” Theory

and Society July 2014, Volume 43, Issue 3-4, pp 465-482.

Bearman, P.S. and Stovel, K. (2000) ‘Becoming a Nazi: A Model for Narrative Networks.’

Poetics, 27: 69-90.

Blau, P. and O.D. Duncan (1967) The American Occupational Structure.

Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. Latent Dirichlet Allocation. Journal of

Machine Learning Research 3, 993-1022.

Börner, Katy, Chaomei Chen, and Kevin Boyack (2005) ‘Visualizing Knowledge Domains’

Annual Review of Information Science and Technology 37: 179-255.

Bourdieu, Pierre. (1990) Homo Academicus. Stanford, CA: Stanford University Press.

Bourdieu, Pierre (1984) Distinction. Cambridge, MA: Harvard University Press.

24

Bourdieu, Pierre and Jean-Claude Passeron (1977). Reproduction in Education, Society, and

Culture. Thousand Oaks, CA: Sage.

Breiger, R. L. (2000) ‘A tool kit for practice theory’. Poetics, 27, 91-115

Breiger, Ron. 2004 [2009]. “The Analysis of Social Networks.” Chp. 22 in The Handbook of

Data Analysis. Los Angeles: Sage. p. 505-526.

Bulmer, M. (1996) ‘The Social Survey Movement and Early Twentieth Century Sociological

Methodology.’ Pp. 15-34 in Pittsburgh Surveyed: Social Science and Social Reform in

the Early Twentieth Century. Edited by Maurine W. Greenwald and Margo Anderson.

Pittsburgh: University of Pittsburgh Press.

Carley, Kathleen (1994) 'Extracting culture through textual analysis', Poetics, 22: 291–312.

Cassirer, Ernst. (1965) The Philosophy of Symbolic Forms, Volume 1: Language. New Haven:

Yale University Press.

Cerulo K. 1988. ‘Analyzing cultural products: a new method of measurement.’ Social Science

Research 17:317.52

Converse, J. M. 2009 (1987). Survey Research in the United States: Roots and Emergence 1890-

1960. New Brunswick, NJ: Transaction Publishers.

Crane, Greg. 2004. “Classics and the Computer: An End of the History.” Pp. 46-55 in A

Companion to Digital Humanities, edited by Susan Schreibman, Ray Siemens and John

Unsworth. Oxwell: Blackwell Publishing.

D’Andrade, Roy (1995) The Development of Cognitive Anthropology. New York: Cambridge

University Press.

DeGraaf, Nan Dirk, De Graaf, Paul M., Kraaykamp, Gerbert, 2000. Parental cultural capital and

educational attainment in the Netherlands: a refinement of the cultural capital

perspective. Sociology of Education 73, 92–111

25

DiMaggio, Paul (1997) ‘Culture and Cognition.’ Annual Review of Sociology 23: 263-87.

DiMaggio, P.J. (1982) ‘Cultural capital and school success: The impact of status-culture

participation on the grades of U.S. high school students.’ Am. Sociol. Rev. 47:189-201

DiMaggio, Paul. (2011) Cultural Networks. Chapter 20 in The Sage handbook of Social Network

Analysis. Edited by John Scott and Peter J. Carrington. Los Angeles. Sage. Pp. 286-300.

DiMaggio, Paul, Manish Naga, David Blei. (2013) “Exploiting affinities between topic modeling

and the sociological perspective on culture: Application to newspaper coverage of U.S.

government arts funding” Poetics Volume 41, Issue 6, December 2013, Pages 570–606

Fine, Gary Alan (1979) ‘Small Groups and Culture Creation: The Idioculture of Little League Baseball Teams.’ American Sociological Review 44: 733-45.

Fligstein, Neil (2001) ‘Social Skill and the Theory of Fields.’ Sociological Theory 19: 105-25. Foucault, Michel (1970) The Order of Things: An Archaeology of the Human Sciences. New

York: Pantheon. Fourcade, Marion (2010). Economists and Societies: Discipline and Profession in the United

States, Britain, and France, 1890s to 1990s. Princeton: Princeton University Press. Friedland, Roger O. & Robert Alford (1991) Bringing society back in: Symbols, practices and

institutional contradictions. In W. W.Powell and P. J. DiMaggio (Eds.), The New Institutionalism in Organizational Analysis (pp. 232–263). Chicago: University of Chicago Press

Friedland, Roger, John W. Mohr, Henk Roose and Paolo Gardinali. “An Institutional Logic for

Love: Measuring Intimate Life.” Theory and Society. Vol. 43(3-4) 333-370

Geertz, Clifford (1964). ‘Ideology as a Cultural System.’ In D.E. Apter (ed.) Ideology and Discontent. Glencoe, IL: Free Press.

Geertz, Clifford (1973). The interpretation of cultures. New York: Basic Books.

Gibson, D. R. (2003) ‘Participation shifts: Order and differentiation in group conversation.’

Social Forces, 81, 1135-1181.

26

Hacking, Ian (1975) The Emergence of Probability. Cambridge, UK: Cambridge University

Press.

Hacking, Ian (1990) The Taming of Chance. Cambridge, UK: Cambridge University Press.

Hajic, Jan. 2004. “Linguistics Meets Exact Sciences.” Pp. 79-87 in A Companion to Digital

Humanities, edited by Susan Schreibman, Ray Siemens and John Unsworth. Ox?ford:

Blackwell Publishing.

Harary, Frank, R. Z. Norman and Dorwin Cartwright. 1965. Structural Models: An Introduction

to the Theory of Directed Graphs, New York, NY: Wiley

Kane, Danielle, 2000. Distinction worldwide: Bourdieu’s theory of taste in international context. Poetics 31, 403–421.

Kroeber, Alfred (1919) ‘On the Principle of Order in Civilization as Exemplified by Changes in

Fashion.’ American Anthropologist 21: 235-63.

Lasswell, H.D., Lerner, D., Pool, I.d.S., 1952. The Comparative Study of Symbols: An

Introduction. Stanford University Press, Palo Alto, CA.

Lazarsfeld, P.F. (1968) ‘Foreword.’ Pp. vii-x in The Logic of Survey Analysis, by Morris

Rosenberg. NY: Basic Books.

Lazarsfeld, Paul F. 1961. “Notes on the History of Quantification in Sociology—Trends, Sources and Problems.” Pp. 147–203 in Quantification: A History of the Meaning of Measurement in the Natural and Social Sciences, edited by Harry Woolf. New York: Bobbs-Merrill.

Lazarsfeld, Paul F. 1968. “Foreword.” Pp. vii-x in The Logic of Survey Analysis, edited by

Morris Rosenberg. New York: Basic Books. Lazarsfeld, Paul F., and Alan H. Barton. 1982 [1955]. “Some Functions of Qualitative Analysis in

Social Research.” Pp. 239–285 in The Varied Sociology of Paul Lazarsfeld, edited by Patricia Kendall. New York: Columbia University Press.

Lazarsfeld, Paul F. 1970. “Sociology.” Pp. 61-165 in Main Trends of Research in the Social and Human Sciences: Part one: Social Sciences. Paris: UNESCO.

27

Leví-Strauss, Claude. 1963. Structural Anthropology. New York: Basic Books.

Lieberson, Stanley. 2000. A Matter of Taste: How Names, Fashions, and Culture Change. New

Haven, CT: Yale University Press.

Omar Lizardo. 2006. "How cultural tastes shape personal networks." American Sociological

Review 71: 778-807.

Łuczewski, Michał (2009). “Revisiting Boudon, Znaniecki, and Polish Peasants.” Pp. 1-21 in

Raymond Boudon. A Life in Sociology. Essays in honour of Raymond Boudon, edited by

Mohamed Cherkaoui and Peter Hamilton, Oxford, Bardwell Press.

Leydesdorff, Loet. 2001. The Challenge of Scientometrics: The Development, Measurement, and

Self-Organization of Scientific Communications. Leiden, DSWO Press.

Markoff, John. 2004. The Abolition of Feudalism: Peasants, Lords and Legislators in the French

Revolution. Penn State University Press.

Martin, John Levi (2003) "What is Field Theory?". - American Journal of Sociology. 109. - pp.1-

49.

Martin, John Levi. (2000) ‘What do animals do all day?’ Poetics.

McFarland, Daniel, Dan Jurafsky, and Craig M. Rawlings. 2013. “Making the Connection: Social

Bonding in Courtship Situations” American Journal of Sociology 118: 1596-1649

McLean, Paul (2007) The Art of the Network. Durham, NC: Duke University Press.

Michel, J.B., Y. K. Shen, A. P. Aiden, A. Veres, M. K. Gray, T. G. B. Team, J. P. Pickett, D. Hoiberg,D. Clancy, P. Norvig, J. Orwant, S. Pinker, M. A. Nowak, and E. L. Aiden. (2011) Quantitative analysis of culture using millions of digitized books. Science, 331(6014):176–182

Mische, A. and P. Pattison (2000). ‘Composing a civic arena: Publics, projects, and social

settings.’ Poetics, 27, 163-194.

28

Mohr, J. W. 1994. “Soldiers, Mothers, Tramps and Others: Discourse Roles in the 1907 New

York City Charity Directory.” Poetics: Journal of Empirical Research on Literature, the

Media, and the Arts 22: 327-357.

Mohr, J. W. 2013. “Bourdieu’s Relational Method in Theory and in Practice: From Fields and

Capitals to Networks and Institutions (and Back Again).” Pp. 101-135 in Applying

Relational Sociology. Edited by François Dépelteau and Christopher Powell. NY:

Palgrave.

Mohr and Duquenne (1997). “The Duality of Culture and Practice: Poverty Relief in New York

City, 1888-1917.” Theory and Society, (April/June) Vol. 26/2-3: 305-356.

Mohr, J. W. and H.C. White. (2008) ‘How to Model an Institution.’ Theory and Society 37:485-

512.

Pachucki, Mark A., Ronald L. Breiger, 2010: “Cultural Holes: Beyond Relationality in Social

Networks and Culture”, in: Annual Review of Sociology 36

Peterson, R A. and D. G. Berger (1975) ‘Cycles in Symbol Production: The Case of Popular

Music.’ American Sociological Review 40: 158-173.

Peterson, Richard A. and Roger M. Kern: Changing Highbrow Taste: From Snob to Omnivore. American Sociological Review, Vol. 61, No. 5 (Oct., 1996), pp. 900-907

Pinch, Trevor (1986) Confronting Nature: The Sociology of Solar-Neutrino Detection. Dordrecht,

Holland: Kluwer.

Platt, J. (1996) A History of Sociological Research Methods in America: 1920 – 1960.

Cambridge: Cambridge University press.

Porter, (1986)

Powell W. W., DiMaggio P. J. (1991) The New Institutionalism in Organizational Analysis.

Chicago: University of Chicago Press.

29

Rawlings, C. M. and Bourgeois, M. D. (2004) ‘The complexity of institutional niches:

Credentials and organizational differentiation in a field of U.S. higher education.’

Poetics, 32, 411-437.

Rawlings, Craig M., and Daniel McFarland. (2011). “Influence Flows in the Academy: Using

Affiliation Networks to Assess Peer Effects among Researchers.” Social Science

Research 40: 1001-17.

Richmond, M. (1917). Social Diagnosis. New York: Russell Sage Foundation.

Ross, D. (1991) The Origins of American Social Science. New York: Cambridge University

Press.

Roscigno, Vincent J., Ainsworth-Darnell, James W., 1999. Race, cultural capital, and educational resources: persistent inequalities and achievement returns. Sociology of Education 72, 158–178

Ruef, M. (1999) ‘Social ontology and the dynamics of organizational forms: Creating market

actors in the healthcare field, 1966-1994.’ Social Forces, 77, 1403-1432.

Saavedraa, Serguei, Kathleen Hagerty, and Brian Uzzi. "Synchronicity, instant messaging, and

performance among financial traders", Proceeding of the National Academy of Sciences (PNAS), 2011: 1018462108v1-201018462

Schuman, Howard. 2008. Methods and Meaning in Polls and Surveys. Cambridge, MA: Harvard

University Press.

Shannon, Claude E. and Warren Weaver. 1949. The Mathematical Theory of Information. The

University of Illinois Press: Urbana.

Smith, T. (2007) ‘Narrative boundaries and the dynamics of ethnic conflict and conciliation.’

Poetics, 35, 22-46.

Spengler, Joseph J. (1961). ‘Theory, Ideology, Non-Economic Values, and Politico-Economic

Development’ in Ralph Braibanti and J.J. Spengler (eds.) Tradition, Values, and

Socioeconomic Development. Durham, NC: Duke University Press.

30

Stigler (1986)

Stouffer, S.A., Suchman, E.A., DeVinney, L.C., Star, S.A., and Williams, R.M. Jr. 1949. Studies

in Social Psychology in World War II: The American Soldier. Vol. 1, Adjustment During

Army Life. Princeton, NJ: Princeton University Press.

Swidler, Ann (2003) Talk of Love. Chicago: University of Chicago Press.

Thomas, W. I. with F. Znaniecki. (1918) The Polish Peasant in Europe and America. Chicago:

University of Chicago Press.

Thornton, Ocasio and Lounsbury 2012. The institutional logics perspective. Oxford: Oxford University Press.

Turner, C. F. and E. Martin. (1984) ‘Introduction.’ Page 3 – 21 in Surveying Subjective

Phenomena, Volume 1.’ Edited by Turner, Charles F. and Elizabeth Martin. New York:

Russell Sage Foundation.

Vaisey, S., 2009. Motivation and justification: A dual-­‐process model of culture in action. American Journal of Sociology. 114, 1675-1715.

Vaisey, S., Lizardo, O., 2010. Can cultural worldviews influence network composition? Social

Forces. 88, 1595-1618.

Van Rees, Kees, Vermunt, J.K., Verboord, M., 1999. Cultural classifications under discussion: latent class analysis of highbrow and lowbrow reading. Poetics 26, 349–365

Watts, Duncan. 2011. Everything is Obvious: How Common Sense Fails Us. New York: Crown.

White, Harrison C. 1963. An Anatomy of Kinship: Mathematical Models for Structures of

Cumulated Roles, Englewood Cliff, NJ: Prentice Hall.

White, H. (1992) Identity and Control: A Structural Theory of Social Action (Princeton, NJ:

White, H.C. (1997) ‘Can Mathematics Be Social?: Flexible Representations for Interaction

Process and its Socio-cultural Constructions.’ Sociological Forum 12(1):53-71

Williams, Raymond. (1977) Marxism and Literature. Oxford: Oxford University Press.

31

Wuthnow, Robert (1989) Communities of Discourse: Ideology and Social Structure in The

Reformation, The Enlightenment, and European Socialism. Cambridge, MA: Harvard

University Press.

Yeung, King-To (2005). “What does love mean? Exploring network culture in two network

settings. Social Forces 84(1): 391-420.