Hinkle et al.: Theory & Empirical Analysis of Strategic Word Choice in District Court Opinions

Rachael K. Hinkle, JD, and Professor Dr. Andrew D. Martin, both of Washington University Department of Political Science, and colleagues, have published A Positive Theory and Empirical Analysis of Strategic Word Choice in District Court Opinions, Journal of Legal Analysis, 4(2), 402-444 (2012).

Here is the abstract:

Supported by numerous empirical studies on judicial hierarchies and panel effects, Positive Political Theory (PPT) suggests that judges engage in strategic use of opinion content—to further the policy outcomes preferred by the decision-making court. In this study, we employ linguistic theory to study the strategic use of opinion content at a granular level—investigating whether the specific word choices judges make in their opinions is consistent with the competitive institutional story of PPT regarding judicial hierarchies. In particular, we examine the judges’ pragmatic use of the linguistic operations known as “hedging”—language serving to enlarge the truth set for a particular proposition, rendering it less definite and therefore less assailable—and “intensifying”—language restricting the possible truth-value of a proposition and making a statement more susceptible to falsification. Our principal hypothesis is that district court judges not ideologically aligned with the majority of the overseeing circuit judges use more hedging language in their legal reasoning in order to insulate these rulings from reversal. We test the theory empirically by analyzing constitutional criminal procedure, racial and sexual discrimination, and environmental opinions in the federal district courts from 1998 to 2001. Our results demonstrate a statistically significant increase in the use of certain types of language as the ideological distance between a district court judge and the overseeing circuit court judges increases.

Bommarito: Visualization of Reading Level Frequency by Congressional Bill Stage

Michael J. Bommarito II of Computational Legal Studies has posted Visualization of Reading Level Frequency by Congressional Bill Stage, on his blog.

Here are excerpts from the post:

Here’s a fun example of how you might use my data on Congressional bill length and complexity. Imagine you want to understand the empirical distribution of Flesch-Kincaid reading level for Congressional bills and how this distribution is related to bill stage. A first step might be to visualize this relationship. [...]

Based on this visualization, you might infer that engrossed bills tend to have less right-skew and have a lower mean reading level. The story behind this might be that Senators and Representatives are less likely to accept legislation they do not understand. To test this, you might run a simple [Kolmogorov-Smirnov] test to see if the introduced bill reading levels are greater than engrossed bill reading levels.

For graphs and sample code, please see the complete post.

Bommarito: Statistics on the Length and Linguistic Complexity of Bills

Michael J. Bommarito II of Computational Legal Studies has posted Statistics on the length and linguistic complexity of bills on his blog.

This post presents a table of statistics on word count, word and sentence length, and Flesch-Kincaid reading level scores for the bills introduced in the 112th U.S. Congress, and a histogram showing the distribution of word counts in those bills.

Mr. Bommarito says that he will “be adding more automated analysis and figures over the next few weeks.”

HT @mbommar.