\section*{Appendix}
\addcontentsline{toc}{section}{Appendices}
\renewcommand{\thesubsection}{\Alph{subsection}}
\setcounter{table}{0}
\counterwithin{table}{subsection}


\subsection{Section 1}
\subsubsection{\emph{New York Times} Expert Sources in Political News Codebook}
\label{contentcoding}
\begin{spverbatim}

- Article Unique ID: pre-generated 
- Journalist Name(s)
- Pronoun ID for journalist: Pronoun reference in the journalist’s outlet bio, or other authoritative social media source. (e.g.; Twitter, LinkedIN, Personal webpage, bio from previous employer outlet). Code: 0 = she/her/hers, 1 = he/him/his, 2 = they/theirs/them, 3 = can’t be determined
- Headline
- Opinion: Indicate the article is an editorial or op/ed
- Date
- Academic expert source name: Copy and paste name directly from article. A source is defined as an individual’s voice represented within an article, either through direct quote or paraphrasing, procured either through direct communication with that source or otherwise.  Typically this person’s voice contributes details, expertise, or context to an article and thus, constitutes a source.
Note: if an academic is a subject of a story for something that is not at all related to their expertise (they are in a lawsuit, for example), they are not an expert source. More specifically, we code only academic expert sources, denoted by a university affiliation; such experts are often also described as having a PhD and/or as a professor/PhD candidate – but they may not be. They might be described as a “researcher,” for example. If it is not obvious from the text whether this expert is affiliated with a university do a google search of their name and locate a professional website.  University affiliation includes experts that work direction with a university (e.g. as a professor) or experts that are affiliated with a university in some capacity, for example with a center that is housed within a university. In other words, an expert counts as affiliated with a university as long as one of their employers is a university (it does not have to be their primary employer). Only refers to in-text discussion; e.g. we do not code images, captions, or headlines.  Similarly, we don’t code screenshots of tweets.  A tweet featuring an expert source would only be coded if the content from the tweet was integrated into the text.
- Political science: Academic expert is a political scientist. Note: Is this academic expert a political scientist?  If indicated in the text, either by describing an affiliation with a political science department, class, or research, or by calling them a political science expert then denote a 1.  If it is not clear based on the article text, do a google search, locate a professional website, and identify 1) is this person in a political science or adjacent department?  2) does this person have a political science PhD?  If either one of those questions can be answered in the affirmative code the academic expert as a 1. If the person is NOT a political scientist, then move on to coding the next article. 
- Source university affiliation: Copy and paste directly from article; if not in the article, identify based on google search.  In the event the expert has more than one university affiliation select the primary affiliation. 
- Source gender: Protocol relies on presence of pronoun in the article. If no pronoun included, use google. Process for identification in absence of pronouns on profile/on social media is to determine by frequency of gender associated with name according to the SSA.
- Number Mentions: Each paragraph where source is mentioned counts as 1, if source is mentioned 3 times in a paragraph it still counts as 1; thus you do not need to count the number of mentions attributed to a source overall, just the number of paragraphs that contains a source mention. The mention does not need a direct quote – a paraphrase is fine. If quotations refer to a saying or an adage or a slogan, it does not count (e.g. what we might put air quotes around) .
- Qualifier: Is there an adjective used to describe expert? For example, “Preeminent scholar of political science."
\end{spverbatim}
\bigskip
\textbf{\emph{Political Science Experts Only}}
\begin{spverbatim}
a. Do they have a Twitter account? (search for this by searching their name/Twitter; check the bio to make sure it is them; could also check their website to see if they list a Twitter handle). If they do have a Twitter account, enter “Yes”
b. If they have a Twitter account, copy and paste their Twitter handle (the @[name])
c. While on their Twitter page, code for the following things:
    i. Year they joined twitter (in their bio there will be a “date joined” – the year alone is fine)
    ii. Number of followers
    iii. Number of people they are following
d.	Their subfield
    i.	This will often be listed on their department’s website. 
    ii.	There is no need to go detailed: either American, Comparative, IR, Methods, Theory
\end{spverbatim}

\subsection {Section 2}
\subsubsection{MTurk Pre-Test}
Prior to fielding the study with journalists, we also ran the study with MTurk participants. The instructions read as follows:

\begin{spverbatim}
Suppose you were a subscriber to an online news website. 
The editorial team is interested in what you find newsworthy. On the next page, we will provide a brief description of recent research gathered from university press releases. Please rank the topics from most newsworthy [1] to least newsworthy [5].
\end{spverbatim}

\label{mturkpretest}
{
\begin{table}[htbp]\centering
 \caption{Mean Ranking, Treatment Effect (Woman Researching Women is the baseline)
\label{tab:mturk}}
\begin{tabular}{l c c }\hline\hline 
\multicolumn{1}{c}
{\textbf{Variable}}
 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline
Woman, Men  &  0.669  & (0.201)\\
Man, Women &  0.578  & (0.199)\\
Man, Men  &  0.707  & (0.199)\\
None, Women  &  0.289  & (0.199)\\
None, Men  &  0.358  & (0.203)\\
Constant &  2.590  & (0.141)\\
\hline
N & 492\\
\hline
\end{tabular}
\end{table}
}

\newpage
\subsubsection{Journalists, Study 1}
\subparagraph{\textbf{The Sample}}
Study fielded December 2018 through January 2019.


\subparagraph{\textbf{Coefficient Estimates, Study 1}}
\label{coeffs}
In Table~\ref{meanreg} we present the coefficients for our main set of results. In addition, we also present several checks and alternative model specifications, including the interaction of journalist gender and treatment.
{
\begin{table}[htbp]\centering
 \caption{Mean Ranking By Condition
\label{meanreg}}
\begin{tabular}{l c c }\hline\hline 
\multicolumn{1}{c}

 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline

Woman, Men &  0.346  & (0.186)\\
Man,Women  &  0.370  & (0.185)\\
Man, Men  &  0.600  & (0.180)\\
None, Women  &  0.017  & (0.190)\\
None, Men  &  0.355  & (0.191)\\
Constant  &  2.067  & (0.138)\\
\hline
N & 536\\
\hline
\end{tabular}\\
\emph{OLS}
\end{table}
}

{
\begin{table}[htbp]\centering
 \caption{Effect of the Researcher, Mean Ranking 
\label{tab:rsonly}}
\begin{tabular}{l c c }\hline\hline 
\multicolumn{1}{c}
{\textbf{Variable}}
 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline
Man   &  0.302  & (0.126)\\
No Gender Cue  &  -0.006  & (0.132)\\
Constant  &  2.257  & (0.093)\\
\hline
N & 536\\
\hline
\end{tabular}\\
\emph{OLS}
\end{table}
}

{
\begin{table}[htbp]\centering
 \caption{Rank as Number 1
\label{tab:rankone}}
\begin{tabular}{l c c }\hline\hline 
\multicolumn{1}{c}
{\textbf{Variable}}
 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline
Woman, Men &  -0.209  & (0.070)\\
Man, Women  &  -0.171  & (0.069)\\
Man, Men  &  -0.241  & (0.067)\\
None, Women  &  -0.034  & (0.071)\\
None, Men  &  -0.113  & (0.071)\\
Constant  &  0.427  & (0.052)\\
\hline
\end{tabular}
\end{table}
}

{
\begin{table}[htbp]\centering
 \caption{Interaction of Treatment and Journalist Gender
\label{tab:genderrank}}
\begin{tabular}{l c c }\hline\hline 
\multicolumn{1}{c}
{\textbf{Variable}}
 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline
Women, Men  &  -0.234  & (0.091)\\
Man, Women &  -0.277  & (0.092)\\
Man, Men &  -0.288  & (0.088)\\
None, Women  &  -0.096  & (0.093)\\
None, Men &  -0.148  & (0.093)\\
Female Journalist  &  -0.145  & (0.105)\\
Woman, Men $\times$ Female Journ.  &  0.052  & (0.142)\\
Man, Women $\times$ Female Journ. &  0.250  & (0.141)\\
Man, Men $\times$ Female Journ.  &  0.111  & (0.137)\\
None Women $\times$ Female Journ. &  0.146  & (0.145)\\
None, Men $\times$ Female Journ.  &  0.077  & (0.145)\\
Constant  &  0.488  & (0.068)\\
\hline
\end{tabular}
\end{table}
}

\newpage
\subsubsection{Journalists, Study 2}
\label{s2appendix}
\subparagraph{\textbf{The Sample}}

Study fielded October 2021 to November 2021. 

\subparagraph{\textbf{Additional Analyses}}
\label{s2addtl}
In the main text we discuss the results of an analyses that tracks effects by the gender of the journalist. We present these results, in full, in Figure~\ref{fig:apps2} , alongside the figure from the main text. 

\begin{figure}[htbp]
     \centering
        \caption{Study 2 Results, Separated by Gender of Journalist}
         \includegraphics[scale=0.66]{Appendix Figures/image001 (2).jpg}
        \label{fig:apps2}
\end{figure}
\newpage
\subsection{Section 3}
\subsubsection{Journalists, Study 3 and Study 4}
\subparagraph{\textbf{Sample}}
This study was fielded in October 2020.

\subsubsection{Study 3: Ideal Expert}
\label{s3expert}


We conduct checks to ensure that there are no systematic factors that led some journalists to skip the question asking for an expert listing. Here, we run two sets of analyses -- one that uses the timing treatment to predict skipping the question, and one that also controls for pre-treatment demographics. We see no evidence that treatment affected the rate at which the journalists offered no name, but we do see some evidence that women were more likely not to offer a name. 

{
\begin{table}[htbp]\centering
 \caption{Offering No Name in Ideal Expert Task
\label{tab:s3skip}}
\begin{tabular}{l c c c c }\hline\hline 
\multicolumn{1}{c}
{\textbf{Variable}}
 & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} & {\textbf{Coefficient}}  & \textbf{(Std. Err.)} \\ \hline
Treatment & 0.008  & (0.053) &  0.019  & (0.055)\\
Years in Proff.  & --- &--- &  -0.005  & (0.005)\\
Age  & --- &--- &  0.005  & (0.004)\\
Woman  & --- &--- &  0.123  & (0.055)\\
Journ. School  & --- &--- &  0.006  & (0.051)\\
Constant & 0.464 & (0.037)  &  0.271  & (0.123)\\
\hline
N&357&&345&\\
\hline \hline
\end{tabular}
\end{table}
}

\subsection{Faculty Survey}
\subsubsection{Faculty Survey: Sample Construction}
\label{fac:sampconst}
To construct our sample, each college and university was classified by region (Northeast, Southeast, Midwest, West, and Southwest) and by institution type (R1, R2, R3, M1, M2, M3, other 4 year).  We combined these classifications to determine the number of institutions from each group to include in the sample. For example, R1 institutions in the northeast made up 2.15\% of the population, so two R1 institutions in the northeast were randomly selected for our initial February sample. We used the same method for our July wave.

In some cases, the randomly selection resulted in certain institutions which did not have political science departments -- and no dedicated political scientists within more general social science departments. In these cases, the institutions were replaced with another randomly selected school.
{
\begin{table}[htbp]\centering
 \caption{Target Percentages in the Final Sample
\label{tab:s3skip2}}
\begin{tabular}{l c c c c c }\hline\hline 
& Northeast &	Southeast &	Midwest &	West &	Southwest \\ \hline
R1 & 2.15\%	&2.09\%	&1.37\%	&1.37\%	&0.83\%\\
R2   & 1.97\% &	2.26\% &	1.97\% &	1.01\% &	0.83\%\\
R3 & 2.03\% &	2.38\% &	2.21\% &	1.61\%	& 0.83\%\\
M1  & 6.85\% &	3.69\%	& 4.71\% &	3.64\% &	1.97\%\\
M2  & 2.86\% &	2.74\% &	3.22\% &	2.09\% &	0.77\%\\
M3 &1.85\% &	2.62\% &	1.79\% &	1.55\%	& 0.48\%\\
Other 4 year &	9.36\%	& 10.43\% &	8.64\%	& 3.93\%& 	1.91\%\\
\hline

\hline \hline
\end{tabular}
\end{table}
}

In order to retain faculty privacy -- especially in the case of faculty of color -- we list the types of institutions included in our sample (rather than the actual institutions). 
\begin{spverbatim}
R1 Northeast	4
R1 Southeast	4
R1 Midwest	2
R1 West	2
R1 Southwest	2
R2 Northeast	4
R2 Southeast	4
R2 Midwest	4
R2 West	2
R2 Southwest	2
R3 Northwest	4
R3 Southeast	4
R3 Midwest	4
R3 West	4
R3 Southwest	2
M1 Northeast	14
M1 Southeast	8
M1 Midwest	10
M1 West	8
M1 Southwest	4
M2 Northeast	6
M2 Southeast	6
M2 Midwest	6
M2 West	4
M2 Southwest	2
M3 Northeast	4
M3 Southeast	6
M3 Midwest	4
M3 West	4
Other Northeast	18
Other Southeast	20
Other Midwest	18
Other West	8
Other Southwest	4
Ivy 4
HBCU 8
\end{spverbatim}

\subsubsection{Faculty Survey: Sample Selection}
\label{fac:sample1}
We were able to compare the gender of the faculty in our list of contacted faculty to the eventual samples. We find similar rates.

{
\begin{table}[htbp]\centering
 \caption{Gender Comparisons
\label{tab:s3skip3}}
\begin{tabular}{l c c c c }\hline\hline 
& Contacted Feb. &	Feb. Completed & Contacted July &	July Completed  \\ \hline
Men & 66.2\%	&62.5\%	&68.9\%	&74.5\%	\\
Women   & 31.5\% &	36.9\% &	30.94\% &	24.5\% \\

\hline

\hline \hline
\end{tabular}
\end{table}
}

\subsubsection{Faculty Survey: Question Wording}
\label{facquestions}
Note: we are including the measures used in Section 4 analyses. There were additional questions asked -- which focus on who faculty would recommend as experts in their field -- which are for a different study outlining overlap between perceived expertise in the news and by the experts themselves. 
\begin{spverbatim}
Which broad field best describes your scholarly expertise?
<1>	American Politics   
<2>	Comparative Politics  
<3>	International Relations   
<4>	Methodology   
<5>	Theory   
<6>	Other [please describe]: 

What is your gender?
o	Man  
o	Woman  
o	Other [please describe]:  

What is your age?
o	21-30  
o	31-40  
o	41-50  
o	51-60  
o	61-70  
o	71-80  
o	81 or older 

How many years since you earned your most advanced degree? Round up.
o	Fewer than 5 years  
o	5-10 years   
o	11-20 years   
o	More than 21 years  

To the best of your knowledge, in the current US News and World Report Department Rankings, how does the department where you got your Ph.D. rank? 
o	Rank 1-10  
o	Rank 11-20  
o	Rank 21-30  
o	Rank 31-40  
o	Rank 41-50  
o	Rank 50-60  
o	Rank 60-70  
o	Rank 70+  

Which of the following best describes your primary employer?
o	Public university  
o	Private university  
o	Community or junior college  
o	Liberal arts college  
o	Historically black college or university  
o	Tribal college  
o	Technical Institute or professional school  
o	Single-sex college 

Are you currently in a department that grants Ph.D.s?
o	Yes  
o	No  

To the best of your knowledge, in the current US News and World Report Department Rankings, how does the department where you work rank? 
o	Rank 1-10  
o	Rank 11-20  
o	Rank 21-30  
o	Rank 31-40   
o	Rank 41-50  
o	Rank 51-60   
o	Rank 61-70  
o	Rank 70+  

What is your job title?

When you think about your ethnic background, which category do you fall into?
o	Asian American/Pacific Islander  
o	Black/African American (non-Hispanic)  
o	Native American or Aleut   
o	White/Caucasian (non-Hispanic)  
o	Hispanic/Latino   
o	Other [please specify]:   
o	Don't know  

Next we'd like to ask you a few questions about your experience with media requests, or when a journalist from a professional media outlet (not a student media outlet) asks to interview you for a story. 

Have you ever received a media request?
o	Yes.  
o	No.  

Again thinking about requests made by journalists from professional media outlets (not student media outlets)...

How often do you receive media requests in a non-election year?
o	Several times a week  
o	Several times a month  
o	Several times a semester  
o	Several times an academic year  
o	Rarely  

How often do you receive media requests in an election year?
o	Several times a week  
o	Several times a month  
o	Several times a semester  
o	Several times an academic year  
o	Rarely  

 How often do you say yes to media requests?
o	Always  
o	Almost always  
o	Often  
o	Rarely 
o	Never 

When you say no to a media request, what are the most common reasons? Select all that apply.
- I don't have time.  
- The turnaround is too tight.   
- Scheduling is too difficult.  
- Mode of interview is not preferred.  
- I don't feel comfortable with the topic.  
- Manner of request is not appropriate.  
- Outlet is not prestigious.  
- Journalist is unknown.  
- University does not reward media requests.  
- University discourages media requests.   
- I have had a negative experience with media requests in the past.  
- Fear of repercussions or harassment.  
- I do not feel comfortable giving media requests.  
- I don't like media interviews.  
- Other [please describe]:  

Have you ever had the following experience:

"I offered a reporter information for an article, but I was not referenced in the story." 
o	No, I have never had this experience  
o	Yes, I have had this experience once  
o	Yes, I have had this experience a number of times  

What sort of factors affect your decision to say yes to a media request? Select all that apply.
- Topic.  
- Outlet size.   
- Outlet Prestige.  
- Journalist's communication.  
- Relationship with journalist.   
- Mode of interview. 
- Mode of contact.   
- Timing.  

Typically, how do journalists contact you for media request?
o	Email  
o	Telephone  
o	Twitter  
o	Facebook  
o	Via media relations  
o	Other [please describe]:  

On average, how many days turnaround do journalists give you for media requests?
o	Several hours   
o	1 day  
o	2 days  
o	3 days  
o	4 or more days  

Do you ever receive media requests because someone else has recommended you?
o	Yes.  
o	No.  
o	Don't know. 

Have members of the public contacted you by email, social media, mail, or phone in response to a media appearance? 
o	Yes, typically, to give positive feedback.  
o	Yes, typically, to give negative feedback.  
o	Yes, the feedback has been mixed.  
o	I have never been contacted.  

Have you ever been threatened by a member of the public via email, social media, mail, or phone in response to a media appearance? 
o	Yes.  
o	No. 

Are media requests included in your university's annual evaluation?
o	Yes.  
o	No.  
o	Don't know.  






















\end{spverbatim}


