[gov3009-l] 9/6 Applied Stats Change!
danahiggins at fas.harvard.edu
Tue Sep 5 16:57:02 EDT 2017
Due to a scheduling issue, Stephen Raudenbush will be presenting next
week. With less than 24 hours notice, *Mayya Komisarchik and Aaron Kaufman
*have graciously offered to present their work in progress, entitled *How
to Measure Legislative Compactness If You Only Know It When You See It*.
Please find the abstract below.
As usual, we will meet at noon in CGIS Knafel, Room 354, and lunch will be
provided. See you there!
-- Dana Higgins
*Title: **How to Measure Legislative Compactness If You Only Know It When
You See It*
*Authors: *Aaron Kaufman, Gary King, and Mayya Komisarchik
*Abstract: *The US Supreme Court, many state constitutions, and numerous
require that legislative districts be “compact,” a concept assumed so
simple that no
definition is offered other than “you know it when you see it.” Academics,
have concluded that the concept is so complex that it has multiple
dimensions requiring large numbers of conflicting empirical measures. We
that both are correct — that the concept is complex and multidimensional,
one particular unidimensional ordering represents a common understanding of
in the law and across people. We develop a survey design to elicit this
without bias in favor of one’s own political views, and with high levels of
intracoder and intercoder reliability (even though the standard paired
comparisons approach fails). We then create a statistical model that
predicts, with high accuracy and solely from the geometric features of the
district, compactness evaluations by judges and other public officials from
many jurisdictions (as well as by redistricting consultants and expert
witnesses, law professors, law students, graduate students,
citizens, and Mechanical Turk workers). As a companion to
this paper, we offer data on compactness from our validated measure for
state legislative and congressional districts, as well as software to
compute this measure
from any district shape. We also discuss what may be the wider
our general methodological approach to measuring important concepts that
know when you see.
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