Instructional staff

• Dr. Shilin Jia
• Email: sjia@uchicago.edu
• Office: Room 213A, 1155 E 60th St
• Office hours: By appointment

Meeting times/locations

Fridays 9:30 am-12:20 pm in room 140A in 1155 E 60th St

Course description

The class will put a special emphasis on the construct of space in cultural analysis. Spatial models have been prevalently used in quantitative studies of culture and ideology, for instance, most famously in Pierre Bourdieu’s analysis of French cultural fields. With the development of big data and machine learning, there has also been bourgeoning advancement in its methodology. In the first five weeks of the lectures and discussions, we will cover the foundational social theories and most commonly-used statistical/computational methods in the studies of cultural space. We will ask and try to answer: what is a cultural space? What are its dimensions? What is its topology? What social processes take place in it? Major statistical techniques, such as principal component analysis, correspondence analysis, and latent class analysis as well as recent advances in computational text analysis and neural-embedding models, will be introduced. The second half of the class will be devoted to empirical studies and student projects. Some prior programming experience will be helpful but not required. Undergraduate students are admitted with the consent of the instructor. Every student is expected to submit an empirical study or extensive literature review at the end of the course.

Course structure

Each class meeting will be divided into an 80-minute discussion of theoretical and social science readings and an 80-minute methodological lecture or workshop. In the later half of the class, we will closely read some empirical analyses and critically evaluate the construction of cultural space in these studies.  Coding exercises will be gradually introduced to help students develop their empirical projects.  

Final project

All students will need to submit a final project at the end of the class. Students can choose to either

1. write an extensive literature review on a topic related to this class, or
2. replicate the empirical analysis of a well-known study of cultural space with potentially some methodological tweaks and/or critique of the original study, or
3. propose and conduct an original piece of empirical research.

Students can and are encouraged to work in groups (of no more than 3) if they choose the last option.

Evaluation

In-class participation (20%)

Every week, students will be expected to complete assigned readings before class and discuss the readings in class. An overall assessment of students' in-class participation will be assessed at the end of the quarter. 

Coding exercises (20%)

Coding exercises will be gradually introduced throughout the quarter. Students are expected to complete the exercises and turn in their work in a timely manner. The exercises will be graded based on completion. 

In-class presentation (30%)

All students are expected to give an oral presentation of their final project during the last class meeting. The instructor will provide oral feedback in class and assign a letter grade for the oral presentation. 

Final paper (30%)

After the oral presentation, students will need to write up their results and turn in a final paper before the quarter ends. The instructor will assign a letter grade for the paper. 

Statement on diversity, inclusion, and disability

The University of Chicago is committed to diversity and rigorous inquiry from multiple perspectives. The MAPSS, CIR, and Computation programs share this commitment and seek to foster productive learning environments based upon inclusion, open communication, and mutual respect for a diverse range of identities, experiences, and positions.

The University of Chicago is committed to ensuring equitable access to our academic programs and services. Students with disabilities who have been approved for the use of academic accommodations by Student Disability Services (SDS) and need a reasonable accommodation(s) to participate fully in this course should follow the procedures established by SDS for using accommodations. Timely notifications are required in order to ensure that your accommodations can be implemented. Please meet with your instructor to discuss your access needs in this class after you have completed the SDS procedures for requesting accommodations

• Email: disabilities@uchicago.edu
• Phone: 773-702-6000

Methods covered in this class

1. Principal Component Analysis (PCA) and Factor Analysis (FA)
2. Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA)
3. Basics of Natural Language Processing (NLP) (only selected topics related to this class will be covered.)
4. World-embedding models (Word2Vec in particular) 
5. (if time permits) Latent Class Analysis (LCA), Multidimensional Scaling (MDS), Procrustes Analysis (PA), extensions of neural-embedding models (Anything2Vec), etc.

Course schedule

Note: Schedule is subject to change. Check back here and on Canvas for updates as the course progresses.

Week 1: Culture as a system

Required readings:

• Mohr, J. W., Bail, C. A., Frye, M., Lena, J. C., Lizardo, O., McDonnell, T. E., ... & Wherry, F. F. (2020). Introduction: Why measure culture? In Measuring Culture. Columbia University Press.
• Inglehart, R., & Baker, W. E. (2000). Modernization, cultural change, and the persistence of traditional values. American sociological review, 19-51.
• Weber, Max. “Religious Rejections of the World and Their Directions.”

OR

• Geertz, C. “Ideology as a Cultural System.”

Method lecture: Principle Component Analysis

Optional method reading:

• Jolliffe I. Principal Component Analysis (2ed., Springer, 2002), Chapter 1 - 4.
• handout5 from STAT 32940

Week 2: Culture in social space, part 1

Required readings:

• Bourdieu, P. (1984). Part I and II in Distinction. pp. 1-254.
• McPherson, M. (2004). A Blau space primer: prolegomenon to an ecology of affiliation. Industrial and Corporate Change, 13(1), 263-280.

Method lecture: PCA continued

Optional method reading:

• Jolliffe I. Principal Component Analysis (2ed., Springer, 2002), Chapter 5-6.

Week 3 Culture in social space, part 2

Required readings:

• Bourdieu, P. (1984). Part III, 5-6, in Distinction. pp. 255-372.
• Mohr, J. W., Bail, C. A., Frye, M., Lena, J. C., Lizardo, O., McDonnell, T. E., ... & Wherry, F. F. (2020). 1. Measuring Culture in People In Measuring Culture. Columbia University Press.

Method lecture: Factor Analysis, Correspondence Analysis 

Optional method reading:

• Jolliffe I. Principal Component Analysis (2ed., Springer, 2002), Chapter 7.

• (Introductory) Clausen, S. Applied Correspondence Analysis (Sage, 1998).
• (More advanced) Greenacre, M.J. Clustering the rows and columns of a contingency table. Journal of Classification 5, 39–51 (1988). 

Week 4 Culture in social space, part 3

Required readings:

• Martin, J. L. (2002). Power, authority, and the constraint of belief systems. American Journal of Sociology, 107(4), 861-904.
• Goldberg, A., & Stein, S. K. (2018). Beyond social contagion: Associative diffusion and the emergence of cultural variation. American Sociological Review, 83(5), 897-932.
• Hahl, O., Zuckerman, E. W., & Kim, M. (2017). Why elites love authentic lowbrow culture: Overcoming high-status denigration with outsider art. American Sociological Review, 82(4), 828-856.

Method lecture: Factor Analysis, Correspondence Analysis continued

Week 5 Culture as signs

Required readings:

• de Saussure, F.. Course in General Linguistics. pp. 7-23, 65-70, 79-100.
• Alexander, J. C., & Smith, P. (1993). The discourse of American civil society: A new proposal for cultural studies. Theory and society, 151-207.
• Evans, J. A., & Aceves, P. (2016). Machine translation: Mining text for social theory. Annual review of sociology, 42, 21-50.

Method lecture: Fundamentals of computational text analysis

Additional reading:

Shannon, C. E. & Weaver, W. (1949). The Mathematical Theory of Communication. 

Week 6 Culture in high-dimensional space

Required readings:

• de Saussure, F.. Course in General Linguistics. pp. 101-134.
• Chapter 6: Vector semantics and embeddings from Speech and Language Processing by Daniel Jurafsky & James H. Martin.
• Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16), E3635-E3644.

Additional reading:

• Lévi-Strauss, C. (1955). The structural study of myth. 

Method lecture: Word2Vec

Optional method reading:

• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26.
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Week 7: Mapping American political ideology

Required readings:

• Jost, J. T., Federico, C. M., & Napier, J. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual review of psychology, 60(1), 307-337.
• Poole, K. T. (2005). Spatial models of parliamentary voting. Cambridge University Press. pp. 1-15.
• Everson, P., Valelly, R., Vishwanath, A., & Wiseman, J. (2016). NOMINATE and American political development: a primer. Studies in American Political Development, 30(2), 97-115.
• Caughey, D., & Schickler, E. (2016). Substance and change in congressional ideology: NOMINATE and its alternatives. Studies in American Political Development, 30(2), 128-146.

OR

• Bensel, R. (2016). Lost in translation: an epistemological exploration of the relation between historical analysis and the NOMINATE algorithm. Studies in American Political Development, 30(2), 185-201.

AND

• Bonica, A. (2014). Mapping the ideological marketplace. American Journal of Political Science, 58(2), 367-386.

Additional reading:

• Poole, Keith T., and Howard Rosenthal. 1985. “A Spatial Model for Legislative Roll Call Analysis.” American Journal of Political Science 29(2): 357–84.

Method lecture: Word2Vec continued

Week 8: Discursive space

Required readings:

• Mohr, J. W., & Neely, B. (2009). Modeling Foucault: Dualities of power in institutional fields. In Institutions and ideology. Emerald Group Publishing Limited.
• Barron, A. T., Huang, J., Spang, R. L., & DeDeo, S. (2018). Individuals, institutions, and innovation in the debates of the French Revolution. Proceedings of the National Academy of Sciences, 115(18), 4607-4612.
• Kozlowski, A. C., Taddy, M., & Evans, J. A. (2019). The geometry of culture: Analyzing the meanings of class through word embeddings. American Sociological Review, 84(5), 905-949.

Method lecture: Aligning vector spaces

Week 9 In-class presentation of student projects

Required readings:

• Nelson, L. K. (2021). Cycles of conflict, a century of continuity: the impact of persistent place-based political logics on social movement strategy. American Journal of Sociology, 127(1), 1-59.
• Zhou, D. (2022). The Elements of Cultural Power: Novelty, Emotion, Status, and Cultural Capital. American Sociological Review, 87(5), 750-781.

Project presentation 

Exercises 

• Exercise 1 (PCA): to be released on Mar 29; due on April 18
• Exercise 2 (FA/CA): to be released on April 12, due on April 18 (for FA) and/or 25 (for CA)
• Exercise 3 (Text analysis): to be released on April 17, due on May 2 
• Exercise 4 (World embedding): to be released on May 1, due on May 9
• Exercise 5 (Aligning vector spaces): to be released on May 8, due on May 16

Exercise grading

• A: complete 4
• A-/B+: complete 3 or 3+
• B: complete 2