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“Metrics of Success in South Asian Contemporary Art” by Shreeansh Agrawal

Between 2008 and 2012, there was a well-known touring exhibition of contemporary art called Indian Highway which travelled to London, Oslo, Herning, Rome, Beijing, and Lyon. In a review, journalist Charles Darwent had the following to say: “To edit the entirety of India’s contemporary art down to 20 or so practitioners is to fall into the trap of imposing order on a thing that will just not have order imposed on it. The result is that we come away from [the venue] with the idea that Indian art is a pallid form of Western art, albeit with a mild curry flavor.”[1]

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The American and European art world had turned its gaze toward India, introducing a demand for a cosmopolitan, yet “authentic” form of creative expression. In my senior year thesis, I have explored the Indian art world’s participation in the global contemporary art scene, evaluating how institutions evolved in the 2000s to accommodate new forms of practice. Through the Digital Humanities Fellowship, I amassed information about auction house sales, exhibition participants, biennales and museums to understand the relationship between these various institutions. I then made linear regressions such as these:

Scatter plot of artist appearances at museums and exhibitions. The artists are born between 1960 and 1980. There is a much stronger relationship between the two variables for artists who studied in India/Pakistan. The scale is 1:1.
Scatter plot of artist appearances at museums and exhibitions. The artists are born between 1960 and 1980. There is a much stronger relationship between the two variables for artists who studied in India/Pakistan. The scale is 1:1. (Click to enlarge)
Scatter plot of artist presence at biennales and exhibitions. Scale is 1:1. R2 is 0.56, indicating a considerable correlation.
Scatter plot of artist presence at biennales and exhibitions. Scale is 1:1. R2 is 0.56, indicating a considerable correlation. (Click to enlarge.)

I then used these regressions to make a network graph indicating whether artists engaging in one institution helps their inclusion in another institution. A green arrow indicates positive effects whereas a red arrow indicates negative effects.

Network Graph representing what effects artist participation in one institution has on artist participation in another institution. A green arrow indicates positive effects whereas a red arrow indicates negative effects.
Network Graph representing what effects artist participation in one institution has on artist participation in another institution. A green arrow indicates positive effects whereas a red arrow indicates negative effects. (Click to enlarge.)

This process was the most time-intensive portion of the project, because it involved scraping data from a variety of sources. I consulted librarians, art history professors and my friends at UMass so that I could gather the right catalogues and archival websites to collect information from. Then, with the help of some statistics professors at Amherst, I used R and Python to scrape and clean data from those websites. Standardizing the names of the artists was an unexpected challenge, because there were so many variants. Syed Haider Raza, for example, has been alternately spelled as Sayed Haider Raza, Syed Haidar Raza, Syed H. Raza, Sayed H. Raza, SH Raza, S H Raza, S.H. Raza and S. H. Raza. I had to use multiple fuzzy matching algorithms, alternating between calculating the generalized Levenshtein edit distance and using the Jaro-Winckler Algorithm. Sometimes, I just had to manually edit the cells of a 12000+ row database.

As a further step that was not part of the thesis, I made network diagrams of the artists’ participation in biennales and exhibitions, and looked at how their position in the network had changed over time. To do this, I looked at the difference in eigenvector centrality between 2000 and 2015 for each artist, and plotted the difference against the number of exhibitions these artists had participated in. An example of that graph is the one attached below:

Graph comparing artists and changing position of influence in exhibition circuits. The bars are a measure of del E, the difference in the eigenvector centrality of an artist between 2000 and 2015. The scatter points count the total number of times an artist has participated in international survey exhibitions.
Graph comparing artists and changing position of influence in exhibition circuits. The bars are a measure of del E, the difference in the eigenvector centrality of an artist between 2000 and 2015. The scatter points count the total number of times an artist has participated in international survey exhibitions. (Click to enlarge.)

Making the network graphs and doing calculations on the graph involved consulting with statistics professors to gain a robust understanding of graph theory. It was also because of the DH Fellowship that I got the idea of looking at change over time, because I was mostly interested in getting one snapshot of a 15 year timeframe, which is not nearly as revealing.

A crucial step which I hope to take in the next month, is to make an interactive web file so that viewers can interact with the network graphs. They will be able to click on each node (which represents an artist) and see what exhibitions and biennales the artist has participated in, and what museums and auctions have held their work. To do this, I will require a better understanding of JavaScript than I currently possess, because I will use JavaScript in conjunction with Gephi (a statistical network software) in order to make the graphs interactive. More to come soon!

[1] Cathrine Bublatzky, Along the Indian Highway: An Ethnography of an International Travelling Exhibition, Visual and Media Histories (Routledge, 2020), 81.

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