Visualizing SCOTUS Doctrine III - Network Analysis Compared

In the first two posts of this series, I discussed the basics of the SCOTUS Mapping Project and its method of looking to dissents to uncover the competing traditions at play in the Court's contested doctrines. In today's installment, I explore how this project compares with other scholarly and technological approaches to analysis of legal citation networks. I hope both to put the project in context and to give Readers potentially interested in in making maps a sense of the process involved.  

Let's start with a little background. Prawfs fans may or may not be familiar with the  fairly large and robust literature on legal "network analysis." (For a good survey article see here; for an influential example see here). A basic premise of this literature is that we can better understand the importance and influence of precedents (including, but not limited to Supreme Court precedents) by analyzing the patterns of their subsequent citation in networks of interrelated opinions. People who write in this field often create sophisticated mathematical models and generate alogorithms to be executed by computers parsing large volumes of case data.

I'll stress right off the bat that the SCOTUS Mapping Project is not an algorithm-driven or computer-automated undertaking. Rather, the Mapper software enables users to visualize doctrine after they conduct thier own close readings of cases. At the same time, the project derives inspiration from network analysis -- I thus fully expect human cartographers using the Mapper software to look for citation patterns just as computers are trained to do in network analysis.

The difference is quantity versus quality. While computers can quickly sift through reams of text to identify co-occuring words and phrases across thousands of cases, people can make deeper conceptual and thematic connections between opinions based on synthesized understanding. People can both interpret text and read "between the lines" of opinions in ways that computers, at least for now, cannot. Visualizations engineered by network analyses will not produce the same perspective as visualizations created using the Mapper software and method.

The best way to illustrate this difference is via a concrete example. Consider then the Supreme Court's doctrine regarding stare decisis. This is the Court's "precedent about precedent" -- pronouncements about when the Court may (or may not) overrule its own prior decisions. How might we visualize the most critical Court opinions in this area? First, let's take a glimpse an image produced using applied network analysis -- courtesty of the geniuses over at Ravel Law. 

Figure 1 (Full size image)  

I created this image by visiting Ravel Law's brilliant website and typing in "stare decisis." The software found 6678 cases with this phrase and displayed the top 75 most relevant. This relevance was presumably calculated based (in part) on the number of subsequent citations to found cases by other cases in the network. I then moved my cursor over the earliest cases in the network, which also happened to be one of the largest bubbles (meaning most cited), and it highlighted 1932's Burnet v. Coronado Oil & Gas Co. The software automatically highlighted other cases that cited Burnet. Then I took a screen shot.

The power and speed of the Ravel visualization is profound. It takes mere seconds to produce.  It is dynamic (changes depending on where you put your cursor) and hyper-linked to the the cases themselves. I am a huge fan and insist my 1L students learn to use the site. Yet I regard Ravel more as a tool for legal research than one for doctrinal mapping. Its network analyses efficiently guide human users to oft-cited cases. However, the visualization does not reveal the character of the cites -- whether they signal agreement or diagreement, extension of prior case's reasoning or sharp and damning distinction. To recognize these kinds of doctrinal fault lines,  a human reader is still required. And to visualize these fault lines, the Mapper has advantages.

Map 7 (Full size image)

Map 7 represents the Court's "precedent about precedent" in an different way. Note first that 1932's Burnet also has a prominent place in the visualization. However, from this map it becomes immediately apparent that it is Justice Brandeis' dissent from that case that is important. Furthermore, the legend reveals that Brandeis' Burnet opinion adovcated overruling a challenged precedent -- as do all the opinions in red. On the other hand, the blue opinions represent cases that advocated affirming prior precedent. The map thus suggests the existence of competing schools about how to approach stare decisis and charts the debate up through the recent clash in 2010's Citizens United. 

As I've said in prior posts, I do not seek in this forum to engage in substantive discussion about particular doctrines. (For those interested in my take on "precdent about precedent", see here and here too).  Rather, the point is to suggest that, as a matter of form, the scheme behind Map 7 permits users to draw a picture of the lines of agreement and disagreement in doctrine that cannot be represented in a conventional network analysis. 

Of course, the automated nature of network analysis also has advantages. The biggest one is time. While the Ravel image took me seconds to generate, Map 7 took many hours. To uncover the competing lines, I first examined the stare decisis debate between the majority and dissenting opinions in Citizens United. Then, I pulled all the opinions those competing opinions cited and examined them. I repeated this process back over 100 years. Meanwhile, I noted all the opinion authors, tallied the votes their opinions received, and coded all the opinions by whether they were arguments for or against overruling. I read and re-read closely in order to ascertain which opinions actually fit together and which were opposed.

Throughout this process, I used the Mapper to help me organize my understanding of the connections between cases. I edited and re-edited maps and tried different looks. This is key. Mapping is an iterative process. Map 7 obviously has far fewer data points represented than does the Ravel visualization. Yet this is the result of a deliberate attempt to distill the relevant doctrine down to its essentials. Indeed, the value of editing can be seen by taking a peek at my "unedited" map of this same territory.

 

Map 7.a (Full size image) 

To my eyes, the map above is far too busy to be useful. While it represents more of the Court's stare decisis opinions, it does not capture the essential doctrinal dialectic as sharply as Map 7. This brings me to a final observation. The Maps are not the Territory. Doctrinal maps do not purport to depict the whole of any given doctrine so much as sketch its essential competing lines.  The point is not to provide exhaustive detail of the terrain but rather to produce a useful guide to the key landmarks and contested boundaries.

In the end, doctrinal mapping involves far more editorial decision-making than network analysis. This is not a bad thing. Legal advocacy, scholarship and teaching all implicate editorial choices. So does judicial opinion writing. Making arguments is what we do as lawyers. Maps just provide a way to make the argument about doctrine and communicate it efficiently to an audience.

That's it for this time. In my next post, I hope to discuss ways doctrinal maps might be used in the law school classroom. I'll also provide some more details on the stipends available for folks who may have interest in creating their own maps and contributing to the project library. As always, comments, questions, or critique is welcome. Thanks for listening and stay tuned!