Bruce Ackerman argues that major shifts in constitutional law can occur outside the Article V amendment process when there are unusually high levels of sustained popular attention to questions of constitutional significance. This Note develops a new empirical strategy to evaluate this claim using the debate over ratification of the Fourteenth Amendment as its test case. The Note applies a statistical process known as unsupervised topic modeling to a dataset containing over 19,000 pages of text from U.S. newspapers published between 1866 and 1884. This innovative methodological technique illuminates the structure of constitutional discourse during this period. The Note finds empirical support for the notion that the salience of constitutional issues was high throughout the ratification debate and then gradually declined as the country returned to a period of normal politics. These findings buttress Ackerman’s cyclic theory of constitutional change at one of its more vulnerable points.And here's a snippet of Gudridge's essay:
“[F]or all the millions of words and thousands of newspaper articles this Note analyzes,” Mr. Young concedes, “this is a rather modest conclusion.” “[T]here is nothing surprising about the fact that the media was paying attention to the passage of major constitutional amendments in the aftermath of a devastating civil war.” (P. 2053.) It’s not Young’s bottom line, however, that marks his effort as important. “[M]illions of words and thousands of newspaper articles”—no law student reads this much! How did he do that?
“Algorithmic topic modeling,” his Note’s title tersely declares. Forty pages plus (out of 54 total) admirably explain what this involves. There is also an elegant technical appendix. Each newspaper front page from the period (all accessible on line) is treated as a separate document and run through optical character recognition software to identify words as words. The documents are computer-converted to brute lists stripped of all original interior organization, so-called common words deleted; the remaining identified words are counted in cases of repetition within each of the documents. The quantified word lists are statistically analyzed (more software) as word distributions, compared with each other, and the most common clusters of words across the full set of documents extracted. These clusters provide the ultimate working material for purposes of Young’s discussion. Texts become data.I'm curious as to what historians think about this kind of analysis. Thoughts from readers?