\documentclass{CUP-JNL-LAN}%[ruler,draftrules]


%%%% Packages
\usepackage{graphicx}
\usepackage{multicol,multirow}
%\usepackage{amsmath,amssymb,amsfonts}
\usepackage{rotating}
\usepackage{appendix}
\usepackage{longtable}
\usepackage[sort&compress,comma]{natbib}
\usepackage[breaklinks,colorlinks,allcolors=blue]{hyperref}
\usepackage{breakurl}

\usepackage{gb4e}
\usepackage{cgloss4e}
\noautomath


\articletype{GENERAL RESEARCH ARTICLE}
\jname{Language}
\jyear{2026}
\jvol{0}
%\jissue{1}
\jdoi{10.1017/lang.2026.10002}


\copyrightline{{\textcopyright}\  The Author(s), 2025. Published by Cambridge University Press on behalf of the Linguistic Society of America.}


\begin{document}

\begin{Frontmatter}

\title[Gubernatorial use of executive orders]{Gubernatorial use of executive orders: unilateral action and policy adoption}

\author*[1]{Simon Devylder\corresp{\email{simon.devylder@unine.ch}}}%
\author[2]{Christoph Bracks}
\author[3]{Poppy Siahaan}

\authormark{Simon Devylder et al.}

\address[1]{\orgdiv{Department of Taiwan Culture Languages and Lit}, \orgname{National Taiwan Normal University}, \orgaddress{\city{Taipei City} \postcode{106}, \country{Taiwan}}}

\address[2]{\orgdiv{Department of Computer Science}, \orgname{University of Kentucky}, \orgaddress{\city{Lexington}, \postcode{KY}, \country{USA}}}

\address[3]{\orgdiv{Linguistics}, \orgname{Harvard University}, \orgaddress{\city{Cambridge}, \state{MA}, \country{USA}}}


\received{04 May 2020}
\revised{10 May 2020}
\accepted{12 May 2020}



\abstract{Given a smooth compact hypersurface $M$ with boundary $\Sigma=\partial M$, we prove the existence of a sequence $M_j$ of hypersurfaces with the same boundary as $M$, such that each Steklov eigenvalue $\sigma_k(M_j)$ tends to zero as $j$ tends to infinity. The hypersurfaces $M_j$ are obtained from $M$ by a local perturbation near a point of its boundary. Their volumes and diameters are arbitrarily close to those of $M$, while the principal curvatures of the boundary remain unchanged.}

\keywords{Steklov eigenvalues, hypersurfaces, linguistic diversity, language and thought, state politics, strategic model}


\end{Frontmatter}


\section{Introduction}
Let $M$ be an $n$-dimensional smooth compact Riemannian manifold with boundary \hbox{$\Sigma=\partial M$}.  The Steklov
eigenvalue problem on $M$ consists in finding all numbers $\sigma\in\mathbb{R}$ for which there exists a nonzero function $u \in C^\infty(M)$, which solves
\begin{equation*}
    \begin{cases}
        \Delta u = 0 & \text{in  $M$,} \\
        \partial_\nu u = \sigma u & \text{on  $\Sigma$.}
    \end{cases}\vspace*{-2pt}
\end{equation*}
Here, $\Delta$ is the Laplacian induced from the Riemannian metric $g$ on $M$, and $\partial_\nu$
is the outward pointing normal derivative along the boundary $\Sigma$. The Steklov eigenvalues
form an unbounded increasing sequence
$0 = \sigma_0 \leq \sigma_1 \leq \sigma_2 \leq \dots \to \infty$, each of which is repeated according to its multiplicity.
Note that if $M$ is connected, then $\sigma_1>0$.

Presidential scholars have long emphasised the role of the executive branch in
federal policymaking. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case. Presidential scholars have long emphasised the role of the executive branch in
federal policymaking. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.

Presidential scholars have long emphasised the role of the executive branch in \cite{adami12}
federal policymaking. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case. Presidential scholars have long emphasised the role of the executive branch in
federal policymaking. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.

Once data are disseminated, whatever contractual or other obligations are placed on those receiving \citep{baerman90,baker85} the
data, the data are effectively out of a data providers' control. Data providers must be certain that the data
disseminated do not provide a risk of disclosure necessitating a reduction in the detail available, or they are
constrained to using a resource intensive auditing regime, and are likely to discover any data misuse only
after it has happened. Once data are disseminated, whatever contractual or other obligations are placed on those receiving the
data, the data are effectively out of a data providers' control. Data providers must be certain that the data
disseminated do not provide a risk of disclosure necessitating a reduction in the detail available, or they are
constrained to using a resource intensive auditing regime, and are likely to discover any data misuse only
after it has happened.

Presidential scholars have long emphasised the role of the executive branch in
federal policymaking. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse \nobreak factors that explain gubernatorial use of executive orders, and I consider how
these same \nobreak executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case. Presidential scholars have long emphasised the role of the executive branch in
federal policymaking.

Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same \nobreak executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.

Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same \nobreak executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case. Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same \nobreak executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.


\section{Gubernatorial and presidential use of executive orders across the various states}

Presidents develop policies formally through unilateral
action, but they also pursue their objectives in the legislative arena. Governors fill an
analogous role within their states. They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.


\subsection{Presidential use of executive orders is largely consistent with expectations and previous literature}

The remainder of the findings is largely consistent \cite{chomsky59} with expectations and previous
literature. Diffusion plays a positive role on states adopting sexual orientation
protections; yet, it is not statistically significant in explaining the adoption of
transgender-inclusive statutes. As anticipated, legislatures are more likely to adopt
both forms of legislation in states where the citizens are more liberal.


\subsubsection{Third level heading with two line text style format with two line text style format  with two line text style format}

They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
I analyse factors that explain gubernatorial use of executive orders, and I consider how
these same executive orders influence statute adoption, using lesbian, gay, bisexual and
transgender (LGBT) employment protections as an illustrative case.

\section{Results}

\subsection{Determinants of executive orders}

The probability
of a state adopting legislation protecting \cite{cagnetta24} sexual orientation increases by a
factor of 1.11 for a one-unit increase in Liberal Citizen Ideology, and the probability
increases by a factor of 2.24 for a five-unit increase in citizen ideology. This effect is
even more pronounced for transgender protections. A one-unit increase in Liberal
Citizen Ideology increases the likelihood of adoption by a factor of 1.20, and the
probability increases by a factor of 2.44 for a five-unit increase in citizen ideology.
The findings regarding the Evangelical population hint at a similar conclusion.

\section*{Estimation}

Using Multilevel Event History Analysis, with the state/year as the unit of analysis \cite{debreslioska13},
I evaluate the following:
\begin{enumerate}[2.]
\item The probability that a governor $i$ will issue an executive order protecting
LGBT employees in time $t$, given that no executive order is in place.

They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
\item The probability that the state legislature $i$ will adopt an LGBT-inclusive
employment nondiscrimination statute in time $t$, given that it has not
already done.
\end{enumerate}
Multilevel modelling accounts for these differences and within-state patterns of adoption seen
throughout the years \cite{drager11}. The effect of determinants that
lead to successful statute adoption of LGBT protections share common elements,
but differ based on the type of protections added – sexual orientation versus gender
identity.
\begin{itemize}
\item The probability that a governor $i$ will issue an executive order protecting
LGBT employees in time $t$, given that no executive order is in place.

They manage the bureaucracy and help set the
policy agenda through speeches, calling special sessions or taking unilateral action.
\item The probability that the state legislature $i$ will adopt an LGBT-inclusive
employment nondiscrimination statute in time $t$, given that it has not
already done.
\end{itemize}
Multilevel modelling accounts for these differences and within-state patterns of adoption seen
throughout the years. The effect of determinants that
lead to successful statute adoption of LGBT protections share common elements,
but differ based on the type of protections added – sexual orientation versus gender
identity.

\begin{figure}[t]%
\FIG{\includegraphics[width=0.9\textwidth]{Fig}}
{\caption{This is a widefig. This is an example of long caption this is an example of long caption  this is an example of long caption this is an example of long caption}
\label{fig1}}
\end{figure}

\begin{figure}[t]%
\FIG{\includegraphics[width=0.9\textwidth]{Fig}}
{\caption{This is an example of short caption this is an example of short caption}
\label{fig2}}
\end{figure}



\begin{table}[t]
\TBL{\caption{Tables which are too long to fit,
should be written using the ``table*'' environment as shown here\label{tab1}}}
{%\begin{fntable}%
%\tabcolsep=8pt
\begin{tabular}{@{\extracolsep{\fill}}lcccccc@{}}
\toprule
 \TCH{Projectile} & \TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} &
\TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} \\\midrule
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4\footnotemark[1]}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
\botrule
\end{tabular}%
%\footnotetext[]{\textit{Note:} This is an example of table footnote this is an example of table footnote this is an example of table footnote this is an example of~table footnote this is an example of table footnote}%
%\footnotetext[1]{This is an example of table footnote}%
%\end{fntable}%
}
\end{table}


The final covariates analyse social factors that influence gubernatorial use of
executive orders. These results differ across the models. Diffusion is not statistically
significant for the sexual orientation model, but reaches conventional
statistical significance for the analysis of gender identity protections. This
tentatively suggests that governors are more likely to issue executive orders as
more neighbouring states add similar protections. Governors are more likely to
issue executive orders to protect \nobreak sexual \nobreak orientation when the states are more
liberal, and composed of fewer Evangelicals. Both terms reach conventional
statistical significance. However, this does not hold when the analysis turns to the
determinants of executive orders that protect gender identity. Citizen ideology
is not statistically significant and, counter to sexual orientation protections,
governors are more likely to issue executive orders when the Evangelical rate
increases. These discrepancies may be related to the changing strategies of
governors and LGBT advocates in later years, or it may be a reflection of the late
adopters that added protections through executive orders, i.e. the remaining
governors in states that were still ``at risk'' of adopting transgender protections
were in more socially conservative states. Both models show that governors are
more likely to issue protections later into the time frame, and the variance across
the states is statistically significant.
\begin{exe}[(1)]
\ex
\begin{xlist}[]
\ex \gll der  Film\\
\textsc{def.nom.m.sg} film(\textsc{m})[\textsc{nom.sg}]\\
\glt `the film'

\ex \gll die Symphonie\\
\textsc{def.nom.f.sg} symphony(\textsc{f})[\textsc{nom.sg}]\\
\glt `the symphony'

\ex \gll das Buch\\
\textsc{def.nom.n.sg} book(\textsc{n})[\textsc{nom.sg}]\\
\glt `the book'
\end{xlist}
\end{exe}

Diffusion plays an inconsistent role in policy adoption, but overall it seems that
the diffusion of pro-LGBT policies encourages the issuance of executive orders and
adoption of similar legislation. However, diffusion does not come up as statistically
significant and positive across the board, and thus caution should be taken when
examining its role in policy adoption. Governors used executive orders more
commonly to establish protections for sexual orientation, whereas legislation was
more prevalent for gender identity; therefore, this might explain why diffusion is
only statistically significant in those respective models. One possible explanation
for why diffusion of LGBT protections does not function as previous diffusion
studies suggest is because states consider several competing policies at once.
Throughout the time periods, states do not simply consider adopting one form of
the protections. %



\begin{Backmatter}


\paragraph{Supplementary material} To view supplementary material for this article, please visit \url{http://doi.org/10.1017/Sxxx850725xxxx}

\paragraph{Data availability} Code for all simulations and experiments is available at \url{https://github.com/morph-order}

\paragraph{Acknowledgments}
The authors also wish to thank Lucie Laporte-Devylder, Jordan Zlatev, Georgios
Stampoulidis, Joost Van de Weijer, Katie Hoemann, and two anonymous reviewers for their invaluable
feedback and comments on earlier versions of this paper.


\paragraph{Author~contribution}
Conceptualisation: P.-S.K., J.F.U.; Software: A.A., E.T.; Visualisation: A.A., E.T.; Writing and reviewing code and paper: A.A., E.T., J.F.U. All authors approved the final submitted draft. \verb+\url{https://credit.+  \verb+niso.org/implementing-credit/}+.


\paragraph{Funding statement} This research was funded by the European Research Council, grant SUBLIMINAL (\#101054902) awarded to Harald Baayen, and partially supported by the Yushan Fellow Program by the Ministry of Education of Taiwan awarded to Yu-Ying Chuang.


\paragraph{Competing interests} A statement about any financial, professional, contractual
or personal relationships or situations that could be perceived to impact the
presentation of the work -- or `None' if none exist.

\paragraph{Ethical standard} The larger research project was approved by the UCSB Human Subjects Committee (protocol number 5-21-0059 and 2-20-0480)


\begin{thebibliography}{99}

\bibitem[Ackerman and Malouf(2013)]{ackerman13}
Ackerman, Farrell, and Robert Malouf. 2013. Morphological organization: The low conditional entropy conjecture. \textit{Language} 89.429--64. \url{https://doi.org/10.1353/lan.2013.0054}.

\bibitem[Adami(2012)]{adami12}
Adami, Christoph. 2012. The use of information theory in evolutionary biology. \textit{Annals of the New York Academy of Sciences} 1256.49--65. \url{https://doi.org/10.1111/j.1749-6632.2011.06422.x}.

\bibitem[Baerman et al.(1990)]{baerman90}
Baerman, Matthew; Dunstan Brown; and Greville G. Corbett (eds.) 2015. \textit{Understanding and measuring morphological complexity}. Oxford: Oxford University Press. \url{https://doi.org/10.1093/acprof:oso/9780198723769.001.0001}.

\bibitem[Baker(1985)]{baker85}
Baker, Mark. 1985. The mirror principle and morphosyntactic explanation. \textit{Linguistic Inquiry} 16.373--415. \url{https://www.jstor.org/stable/4178442}.

\bibitem[Chomsky(1959)]{chomsky59}
Chomsky, Noam. 1959. Review of Verbal behavior, by B. F. Skinner. \textit{Language} 35.26--58. \url{https://doi.org/10.2307/411334}.

\bibitem[Cagnetta \& Wyart(2024)]{cagnetta24}
Cagnetta, Francesco, and Matthieu Wyart. 2024. Towards a theory of how the structure of language is acquired by deep neural
networks. \textit{Advances in Neural Information Processing Systems (NIPS)} 37.83119--63. \url{https://doi.org/10.52202/079017-2645}.

\bibitem[Debreslioska et al.(2013)]{debreslioska13}
Debreslioska, Sandra; Asli \"Ozyürek; Marianne Gullberg; and Pamela Perniss. 2013. Gestural viewpoint signals referent accessibility. \textit{Discourse Processes} 50.431--56. \url{https://doi.org/10.1080/0163853X.2013.824286}.

\bibitem[Drager(2011)]{drager11}
Drager, Katie K. 2011. Sociophonetic variation and the lemma. \textit{Journal of Phonetics} 39.694--707. \url{https://doi.org/10.1016/j.wocn.2011.08.005}.

\bibitem[Elbourne(2008)]{elbourne08}
Elbourne, Paul. 2008. Demonstratives as individual concepts. \textit{Linguistics and Philosophy} 31.409--66. \url{https://doi.org/10.1007/s10988-008-9043-0}.

\end{thebibliography}


\begin{appendix}
\section*{Appendix A: Formal arguments}

To illustrate how boosting trees work, Figure \ref{fig3} presents a toy example. In this task, we want to classify each observation as being either a circle or a square (these would be our toy genders), based on their position on the $x$-axis and the $y$-axis (these would be our toy predictors). Boosting trees approach this problem by doing multiple iterations and, at each iteration, building a new classifier to solve part of the problem not solved by previous iterations

The first cohort of interns started work with Alexia. The team began by using ELAN to review the pilot survey
recordings and devising an inductively arrived-at coding scheme based on the types and range of responses received. Anna used this scheme to create an initial Qualtrics interface for survey coding, adapting the questions to a multiple-choice format where necessary to enable later quantitative analysis. Alexia and the interns also provided the larger MILPA team with feedback on the structure and phrasing of the survey in order to elicit richer responses targeting the desired information when the original question proved ineffective.

\begin{figure}[b]%
\FIG{\includegraphics[width=0.9\textwidth]{Fig}}
{\caption{This is a widefig. This is an example of long caption this is an example of long caption  this is an example of long caption this is an example of long caption}
\label{fig3}}
\end{figure}


\section*{Appendix B: RA experience questionnaire}
To calculate the uncertainty of the accuracy, we assume that model accuracy is binomially distributed. We organize our data in a table (Table \ref{tab3}), where each row contains a classification model and the number of correct predictions.

\begin{table}[b]
\TBL{\caption{Tables which are too long to fit,
should be written using the ``table'' environment as shown here\label{tab3}}}
{%\begin{fntable}%
%\tabcolsep=8pt
\begin{tabular}{@{\extracolsep{\fill}}lcccccc@{}}
\toprule
 \TCH{Projectile} & \TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} &
\TCH{Energy} & \TCH{$\sigma_{\mathit{calc}}$} & \TCH{$\sigma_{\mathit{expt}}$} \\\midrule
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\
\botrule
\end{tabular}%
}
\end{table}


The earlier approach was parsimonious, following the established scientific method,
enshrined by Occam. And the justification was that it provided a linguistic model, and that any subsequent
psycholinguistic account needed to accomplish at least those results. The approach based on statistical
modeling outperforms the parsimonious models (though not reaching full accuracy). However, while we
knew exactly how our earlier assignment rules worked, we are less clear about the results from statistical
modeling. This is a general core question when we apply machine learning models like the ones described
in this article: how do they work internally, and what and how do they relate to what humans do? Our
methods are a great tool for linguists and can show us how successful a machine-learning approach based
on similarities is, but they do not divulge what is going on in the mind of the speaker when performing
gender assignment. So while we present impressive results, we have to be cautious and refrain from
overambitious claims.

The surprisal of the second slot is the same for Language A. For Language B, the surprisal of the second slot is the same as that in Language A since there are no alternative morphemes; however, if a form were to have more than two morphemes, the surprisal would similarly increase. Thus, we can see that category clustering effectively lowers local surprisal, therefore encouraging more favorable memory-surprisal tradeoffs.

\subsection{Advantage of fusion}

The intuition is as follows: in an agglutinative language, where each morpheme represents a feature unambiguously and in a context-invariant way, morphemes must be slightly redundant when the underlying features cooccur predictably. By contrast, in a fusional language, the substring that corresponds to one feature can adapt based on other features, reducing redundancy.

Now consider the entropy of the second string part $Y_2$ out of context: H[$Y_2$]. This corresponds to the average surprisal of $Y_2$ when there is no memory for context.We claim that this can be lower in a fusional compared to an agglutinative language. To support this claim, we show that in an agglutinative language, the entropy of the second string part $Y_2$ is equal to the entropy of the input feature $X_2$; whereas in a fusional language, the entropy of the second string part $Y_2$ has a lower bound that is less than the entropy of $X_2$.

\subsection{Advantage of category clustering}
Pragmatics is at the heart of this overarching account of the use of spatial loci in sign languages. This
allows sign language loci, as agreement markers, to be subsumed by more general accounts of reference
resolution. The agreement analysis is supported by the observation that locational modification
exhibits typologically common effects of verb agreement, such as argument drop and noncanonical
word order.

\end{appendix}

\begin{biography}
\author{Mary Bucholtz}{ is a distinguished professor in the Department of Linguistics at the University of California, Santa Barbara. \email{bucholtz@ucsb.edu}.}
\and
\author{Katie Lydon}{was an undergraduate student at the University of California, Santa Barbara. \email{katie@lydon.net}.}
\and
\author{Griselda Reyes Basurto}{ was a laboratory assistant at the University of California, Santa Barbara and program coordinator for the Maintaining Indigenous Languages project at MICOP. \email{griselda.mixteco@gmail.com}.}
\end{biography}

\end{Backmatter}

\citethisarticle{Simon Devylder, Christoph Bracks and Poppy Siahaan.}

\end{document}
