To create a machine-made competing\u00a0map, I used SCOTUS Mapper to generate\u00a0a 2-degree network linking Sorrell<\/em> to Virginia Pharmacy<\/em>. The program’s algorithm pulls into the network all the cases that Sorrell<\/em> cites that in turn cite Virginia Pharmacy<\/em>. This is what that network looks like (as with all the images in this post, click for full-size map).<\/p>\n I wanted to edit out these extra cases so I ran the network through a text filter on “commercial speech.” This machine-based edit knocked out 4 cases (the remaining 21 cases all contained the phrase). From there, I had to edit out by hand the 7 cases that Sullivan & Feldman did not include in the canonical line. (Let’s call those cases “non-canonical 7” — I’ll return to them below). After that editing, I ended up with this map:<\/p>\n Note that this second map uses a “Spaeth projection.” The Y axis is no longer random — it represents that Supreme Court Database code\u00a0for both outcome direction and judgment vote. Red cases are Spaeth-coded “conservative” — meaning the Court upheld a restriction on commercial speech. Blue cases are Spaeth-coded “liberal” — meaning the Court struck down a restriction on commercial speech.<\/p>\n Now what does\u00a0it take to automatically capture the remaining 9 squib cases from the Sullivan & Feldman canonical line? I tried generating a 3-degree network connecting Sorell<\/em> to Virginia Pharmacy<\/em>. So in addition to all the 2-degree cases, this network includes all the cases cited by 2-degree cases that in turn cite Virginia Pharmacy<\/em>. Here’s what that network looks like on a random Y-axis projection:<\/p>\n Unsurprisingly, this network is very large — 81 cases. The good news is that the network easily picks up all 23 cases from the Sullivan & Feldman canonical line. The bad news is that it picks up 58 extra cases.<\/p>\n To get rid of these, I first tried the machine filter route. After automatically excluding cases without the phrase “commercial speech”, the network shrunk from 81 to 53 cases. Once again, that’s a good start but not nearly good enough. To get rid of the other 20 cases, I had to edit by hand. Upon completion of that edit, here’s what the new network looks like using a Spaeth projection:<\/p>\n So how important are those extra 9 cases? Are they really part of the core commercial speech line? All that we can conclude for certain is that none of the opinions in Sorrell<\/em> cited those 9 cases. As far as the justices sitting in 2011 were concerned, none of those 9 cases was important in justifying their decisions. Of course, the diminished “2011 value” of the cases does not mean that “the missing 9” had no impact on the network’s development.<\/p>\n Comparing the 2- and 3-degree edited maps, one feature jumped out at me: almost half of the canonical cases missed by the 2-degree map concerned lawyer advertising (4 out of the 9 missed cases). \u00a0With a little more editing, I modified the last map to highlight the lawyer advertising cases in magenta.\u00a0This is the result:<\/p>\n With the benefit of this new visualization, we can easily appreciate how the Sullivan & Feldman text took a deeper academic dive into the lawyer advertising cases that the\u00a0Sorrell<\/em> opinions found necessary. It seems hard to fault Sorell<\/em>\u00a0for only citing 3 of the 7 cases in the magenta line.<\/p>\n The other 5 missed cases — Carey<\/em> (1977), Metromedia<\/em> (1981), Posadas<\/em> (1986), United Reporting<\/em> (1999), and Lorillard<\/em> (2001) — are certainly important. But are they more important than the “non-canonical 7” cases referred to above? Recall those are the 7 cases included in the 2-degree network after applying the “commercial speech” filter. In other words, those are ostensibly commercial speech cases cited in Sorrell<\/em> but not included in Sullivan & Feldman.<\/p>\n Forgive me as I dive deep into the First Amendment weeds for just a moment more and name the “non-canonical 7” cases:\u00a0Dun & Bradstreet<\/em> (1985), RAV<\/em> (1992), Edenfield<\/em> (1993), Glickman<\/em> (1997), Greater New Orleans Broadcasting<\/em> (1999), Playboy<\/em> (2000), and United Foods<\/em> (2001). Three of those cases (Dun & Bradstreet<\/em>, RAV<\/em>, and Playboy<\/em>) certainly do not belong the main commercial speech line. But the other four do. And they are arguably as important as the 5 missed cases above.<\/p>\n Now let’s step back and review. Sullivan & Feldman have 23 cases in their (human created) canonical network. \u00a0The machine generated 2-degree network (filtered for commercial speech) has 21 cases. 14 cases overlap between the networks and are clearly core to the line. Of the 9 cases captured by Sullivan & Feldman but not the machine, all are relevant and 5 are\u00a0uniquely so. Of the 7 cases captured by the machine but not by Sullivan & Feldman, 3 are irrelevant and 4 are uniquely relevant.<\/p>\n All in all — Sullivan and Feldman’s editing fares better. This is as you would hope and expect. But the 2-degree network is still remarkably efficient at identifying relevant cases. (The 3-degree network, on the other hand, is far too large and unwieldy.) And the machine-generate network approach suggests potentially fruitful doctrinal angles for further reading outside of the Sullivan & Feldman line.<\/p>\n In the end, it bears emphasis that identifying relevant cases is very different from reading and understanding those cases. And in that department, the human editing of a casebook is completely indispensable. It would probably take 100s of extra hours to read unedited versions of cases identified by the 2-degree network. So thank your stars for human editors!<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" This semester I’m teaching First Amendment law for the very first time and it’s proving to be a wonderful experience. First Amendment cases have great stories behind them and the Supreme Court’s doctrine is complex and highly contested. To navigate … Continue reading ![\"Sorrell_to_VA_2degree_random\"](\"http:\/\/blogs.ubalt.edu\/cstarger\/wp-content\/uploads\/sites\/273\/2015\/03\/Sorrell_to_VA_2degree_random.jpg\")
<\/a>\u00a0Now this 2-degree network is actually quite rich. It contains 25 cases — including all three of the Sullivan & Feldman principal cases as well as 11 of the 20 squibs. While the 2-degree network thus picks up 14 out of the 23 canonical cases, it also picks up 11 “extra” cases not included in the canonical line.<\/p>\n![\"Sorrell_to_VA_2degree_edited_genealogy\"](\"http:\/\/blogs.ubalt.edu\/cstarger\/wp-content\/uploads\/sites\/273\/2015\/03\/Sorrell_to_VA_2degree_edited_genealogy.jpg\")
<\/a><\/p>\n![\"Sorrell_to_VA_3degree_random\"](\"http:\/\/blogs.ubalt.edu\/cstarger\/wp-content\/uploads\/sites\/273\/2015\/03\/Sorrell_to_VA_3degree_random.jpg\")
<\/a><\/p>\n![\"Sorrell_to_VA_3degree_edited_genealogy\"](\"http:\/\/blogs.ubalt.edu\/cstarger\/wp-content\/uploads\/sites\/273\/2015\/03\/Sorrell_to_VA_3degree_edited_genealogy.jpg\")
<\/a>This map represents all the Sullivan & Feldman canonical commercial speech cases. Getting those extra 9 cases in requires a healthy\u00a0dose\u00a0of editing of the 3-degree network.<\/p>\n![\"Sorell_to_VA_3degree_edited_genealogy_lawyer\"](\"http:\/\/blogs.ubalt.edu\/cstarger\/wp-content\/uploads\/sites\/273\/2015\/03\/Sorell_to_VA_3degree_edited_genealogy_lawyer.jpg\")
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