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\articletype{RESEARCH ARTICLE}
\jname{Phonological Data and Analysis}
\jyear{2025}
\jvol{0}
%\jissue{1}
\jdoi{10.1017/pda.2026.xxxxx}


\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]{Sample article title content: with article type as research article}

\author*[1]{Author Name\corresp{\email{authorname@cambridge.org}}}%
\author[2]{Author Name}
\author[3]{Author Name}

\authormark{Author Name \textit{et al.}}

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

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

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

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


\abstract{This is sample abstract text style. This guide is for authors who are preparing papers for the \emph{Phonological Data and Analysis} using  \LaTeXe\ and the Phonological Data and Analysis class file.\lipsum[1]}

\keywords{Keywords text; sample keywords; sample keywords two; sample keywords three; sample keywords four}


\end{Frontmatter}


\section{Introduction}
\lipsum[1]
\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 $\sigma_1>0$.

\lipsum[1-3]

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.

\lipsum[1-4]

\section{First level heading sample text}

\lipsum[5]


\subsection{Second level heading sample text}

\lipsum[6]

\subsubsection{Third level heading sample text}

\lipsum[7]

\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\footnote{This is sample footnote text. Such a view carries consequences for the analysis below, but we
preferred to refrain from adopting it since there is no clear language-internal evidence for a higher level of complexity in
affricates.} 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*{Unnumbered first level heading}

\lipsum[6]
\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{quote}
Consequently, governors
may elect to pursue legislation to adopt more expansive and enduring policies by
negotiating with the legislators first. Governors that see legislation as likely to pass
in the legislature, or governors with weaker institutional powers to dictate
administration policies, are especially likely to take this approach.
\end{quote}

\begin{figure}[t]%
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\begin{table}[t]
\TBL{\caption{Tables should be written using the ``table'' environment as shown here\label{tab1}}}
{%\begin{fntable}%
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\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}@{\hspace*{6pt}}lcccccc@{\hspace*{6pt}}}
\toprule
 \TCH{Projectile} & \TCH{Energy} & \TCH{Calc} & \TCH{Expt} &
\TCH{Energy} & \TCH{Calc} & \TCH{Expt} \\\midrule
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\\hline
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\\hline
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\\hline
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\\hline
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\\hline
{{Element 4}}&500 A &961 &$\hphantom{0}922\pm10$ &900 A &1268 &$1092\pm40\hphantom{0}$\\\hline
{Element 3}&990 A &1168 &$1547\pm12$ &780 A &1166 &$1239\pm100$\\\hline
{{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}%
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}
\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.



\begin{Backmatter}


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

\paragraph{Acknowledgments}
Insert the Acknowledgment text here.

\paragraph{Data availability} A statement about how to access data, code and other materials allowing users to understand, verify and replicate findings -- e.g. Replication data and code can be found in Harvard Dataverse: \url{https://doi.org/link}.


\paragraph{Conflict of interest} 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{Funding statement} This research was supported by grants from the <funder-name> <doi> (<award ID>); <funder-name> <doi> (<award ID>).

\paragraph{Ethics statement} None



%\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/}+.



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\end{Backmatter}

\citethisarticle{Author, Name, Author Name and Author Name.}

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