(Not) Fake News? Navigating Competing Claims Regarding Status Threat and Trump Support

Jon Green, Sean McElwee, Meredith Conroy, and Colin McAuliffe 

In this research note, we analyze a recent critique of Mutz (2018). In her article, Mutz finds “status threat” to predict support for Trump in 2016. The criticism argues that “status threat” is ill- defined and -measured and that Mutz mis-specified her models. We explore each claim in turn. To view this research note as a PDF, visit here

Summary Of Findings

  • The choice to include immigration, trade, China, terrorism, and isolationism attitudes as status threat, not a material interest, as is done in Mutz (2018) is defensible. Morgan (2018) does not provide sufficient reasons for rejecting this categorization.
  • The modeling choices made in (Mutz 2018) are defensible.
  • Morgan’s concerns regarding causality are also defensible.
  • Attitudes about immigration were a key determinant in the 2016 election outcome.


In a recent article published in the Proceedings of the National Academy of Sciences, political scientist Diana Mutz advances the argument that anxiety among traditionally high-status groups (whites, Christians, men, e.g.) in response to the increasing diversification and globalization of the United States predicts support for Donald Trump in the 2016 presidential election, while material economic concerns do not. To make this claim, she uses two sources of data: a panel survey that re-interviewed the same participants in 2012 and 2016, and a cross-sectional survey conducted shortly before the election. Over multiple analyses on these datasets, she finds that while attitudes toward trade and China did not change between 2012 and 2016, voters perceived differences between the parties’ presidential candidates on these issues in 2016 that they didn’t in 2012. Furthermore, Mutz finds that a set of variables that tap into what she calls “status threat” — or concern over disruptions to existing racial and global hierarchies — does a better job of explaining Trump voting in 2016 than pocketbook economic concerns.

Sociologist Stephen Morgan has challenged Mutz’s article on two fronts. First, he levies a theoretical critique against Mutz, claiming that some of the variables she categorizes as “status threat” are in fact measuring material interests. Second, he levies a methodological critique, arguing that the models Mutz specified do not show what she claims they show. We evaluate both of these criticisms below.

Theoretical Critique: Which Variables Matter, and What do They Mean?

Mutz’s article is framed such that two competing hypotheses of the 2016 election are tested against one another. The first, which informs many of the journalistic retrospectives of the election, is the “left behind thesis,” which holds that the voters who swung from Obama to Trump were primarily those who were experiencing economic precarity at either the individual or community level. The second is the status threat thesis, which argues that dynamics of the 2016 campaign made racial and global hierarchies more salient than they had been in previous elections, leading those at the top of those hierarchies to side with the candidate they felt closer to along those dimensions.

As Mutz notes, it is difficult to fully separate the salience of racial and global hierarchies in American politics. Citing Elizabeth Theiss-Morse (2009), she notes that the prototypical American is seen as being white, Christian, and male, which suggests that citizens who are more heavily invested in white, Christian, and male identities stand to bear greater psychological costs from the perception that the United States is losing status relative to other nations. Donald Trump consistently emphasized this theme on the campaign trail. Furthermore, Mutz cites experimental evidence (Craig and Richeson 2014; Willer, Feinberg, and Wetts 2016) showing this pattern — that making changes to established hierarchies salient changes whites’ political outlooks. In Mutz’s formulation, this justifies the inclusion of attitudes regarding free trade, immigration, China, isolationism, and terrorism under the category of status threat.

Morgan’s first criticism disputes this categorization, and he re-classifies these variables to fall under the umbrella of “material interest” — a broader set of constructs that represent respondents’ perception of current or prospective well-being in some tangible, non-psychological sense. He further argues that partisan identification is endogenous to education, and so he presents results with that variable alternatively included and excluded. Finally, he also includes iterations with whites alternately subsetted or included as an identifier. The below table outlines all of the variables included in Mutz’s and Morgan’s analyses, and how each categorized them.

Variable Mutz Morgan
Female Base Base
White Base Stratum or Base
Non-College Base Base
Age Base Base
Religiosity Base Base
Income Base Base
Party ID Base Excluded or Base
Looking for Work Econ Material Interest
Worried about Expenses Econ Material Interest
Safety Net Econ Material Interest
Personal Finance Econ Material Interest
Nation’s Economy Econ Material Interest
Social Dominance Status Status
Outgroup Prejudice Status Status
Reverse Discrimination Status Status
Worried About America Status Status
Free Trade Status Material Interest
China Threat Status Material Interest
Immigration Status Material Interest + Foreign Policy
Isolationism Status Material Interest + Foreign Policy
Terror Threat Status Material Interest + Foreign Policy
National Superiority Status Status

Here, we review each of the disputed variables, considering whether Mutz’s categorizations are sensible.

Trade:  Morgan takes specific issue with Mutz’s claim, made in an interview following her article’s publication, that “trade is not an economic issue in terms of how the public thinks about it. It definitely is when elites think about it.” Morgan says this claim is “bold,” with the implication that it is incorrect. However, at first glance, Mutz’s assertion seems reasonable. The public’s attitudes on trade certainly aren’t random, but they vary based on factors other than the direct material effects of trade policy. As Rothwell and Diego-Rosell (2016) found, while trade attitudes were strongly associated with favorability toward Donald Trump in the 2016 Republican primary, actual exposure to trade-induced job loss (or immigration, for that matter) was not. Furthermore, trends in trade attitudes do not seem to track with objective economic indicators — instead seeming to respond most strongly in recent years to partisan cues when political elites make the issue salient. This is consistent with Mutz’s finding that while attitudes toward trade did not change much between 2012 and 2016, the public’s perception of where the major party candidates stood on trade changed quite a bit — a finding that Morgan does not dispute.

Morgan further argues that items regarding attitudes toward trade are better classified as tapping material interests for three reasons: First, they are typically asked in batteries that ask about economic standing; second, that survey respondents make cognitive links between trade and personal economic standing; and third, that Trump’s own rhetoric tied those issues to material interests. We consider each objection in turn.

First, while it may be the case that trade questions are often asked alongside questions concerning personal economic interests, that doesn’t mean that trade questions are tapping into respondents’ perceptions of personal economic interests. It is a better indication, consistent with Mutz’s explanation in her interview, that political sophisticates such as political scientists tend to view trade as an economic issue. Either way, trade questions are not always placed in the middle of batteries of questions concerning personal economic interests; per Mutz’s supplementary materials, the set of questions in her Amerispeaks survey asking respondents about their opinions on free trade and its sociotropic economic benefits immediately follows questions asking respondents for their opinions on whether the U.S. healthcare system has improved. Perceived threats — personal economic, as well as national — come immediately after.

Second, Morgan cites McCall and Orloff (2017) to claim that “the standard position in the literature is that respondents make cognitive connections between globalization, trade, and their own economic standing.” At first glance, Morgan’s use of this paper to tie trade attitudes directly to material interests is strange, as the paper in question does not support this claim. To begin with, the paper doesn’t speak to trade attitudes as they are expressed on surveys — searching the paper for the words “survey,” “respondents”, and “cognitive” all yield zero results. Furthermore, as the authors argue, Donald Trump crafted an “American” identity out of “a certain kind of unabashed intersectionality, targeting whiteness plus economic decline in male dominated fields” (pg 40). This identity-based materialism is entirely consistent with Mutz’s status threat hypothesis, which argues that appeals based on the U.S.’s relative economic standing around the globe should resonate particularly strongly with citizens atop domestic hierarchies — namely, white men. In short, Morgan’s one reference to the “standard position in the literature” on trade attitudes is both not clearly the standard position and is not clearly in support of his position.

Third, Morgan claims that Donald Trump’s rhetoric clearly tapped into the working class’s material interests, rather than the threats immigration and trade posed to their status. He cites Lamont, Park, and Ayala-Hurtado (2017) to claim that, “Trump appealed directly to the material interests of working-class voters, praising the dignity of their work and arguing that their past labor had given the country its mid-twentieth century prosperity.” In our view, highlighting the dignity of blue-collar work and praising blue-collar workers’ past contributions to U.S. society are much more clearly identity-based appeals than they are promises of improved material conditions. Furthermore, the paper Morgan cites to claim otherwise describes an appeal that is strikingly status-based, which seems entirely consistent with Mutz’s status threat hypothesis. Lamont, Park, and Ayala-Hurtado write that,

While addressing this gap required making salient the structural character of the economic changes that have transformed the lives of these workers, it also required drawing strong moral boundaries toward undocumented immigrants, refugees, and Muslims, and making salient workers’ high status characteristics in their role as protectors of women. The systematic appeals of Trump’s rhetoric to the various facets of the workers’ quest for recognition can only be captured by considering the full set of boundaries that his presidential speeches made salient or left latent.

They continue,

Trump brought the politically marginalized white working class back to the voting booth by cultivating differences (Lamont and Fournier 1992); that is, by reinforcing the boundaries drawn toward socially stigmatized groups. This was accomplished by repeatedly insisting on the moral failings of these groups (in the case of refugees and undocumented immigrants) as well as by making these groups more one‐dimensional, by stereotyping them as in need of protection (for African Americans and women). Trump accomplished all this by using strong language that seemed ‘authentic’, ‘in your face’, and ‘anti‐pc’, and particularly resonated with frustrated white working class Americans eager to ‘tell truth to power’. Thus, Trump acted as an influential cultural agent who knew how to tap into latent and less latent symbolic boundaries that already existed among white working‐class Americans in the early 1990s (Lamont 2000).

This, too, is entirely consistent with Mutz’s status threat hypothesis.

Morgan also relies on Swedberg (2018) to argue that Trump “relied on folk beliefs about how the US economy can be managed in order to argue that renegotiated trade agreements and restrictions on immigration would improve working-class economic security.” However, in the context of Swedberg’s paper, this quote is merely characterizing Trump’s rhetoric on the economy as reflecting “everyday knowledge,” which is inherently messy. As Swedberg notes later on, “In studying folk economics it is important that people’s ways of thinking about the economy should be seen as having their own independent existence. For this to happen, they must not be reduced to other forces, such as material interest, social structures, or just plain ignorance” (Swedberg 2018, 9, emphasis added). In short, Swedberg specifically cautions against using his paper to make the claims Morgan uses it to make.

Moreover, Swedberg (2018) characterizes “Trumponomics” as influenced by Steve Bannon’s “economic nationalism,” and notes that, “the readiness to support nationalist economic policy is a function of the perceived economic threat posed by foreign competition. Economic nationalism is linked with personal job insecurity, authoritarianism, and intolerance of ambiguity” (footnote 19). Thus, Swedberg’s paper may in fact be more supportive of Mutz’s characterization of these issues as evoking status threat than Morgan’s characterization of the public’s understanding of these issues as material interests.

Finally, Swedberg discusses Trump’s announcement that he was running for president as key for understanding Trump’s knowledge about the economy. He quotes the following statements by Trump:

Our country is in serious trouble. We don’t have victories anymore. We used to have victories, but we don’t have them. When was the last time anybody saw us beating, let’s say, China in a trade deal? They kill us. I beat China all the time. All the time.

When did we beat Japan at anything? They send their cars over by the millions, and what do we do? When was the last time you saw a Chevrolet in Tokyo? It doesn’t exist, folks. They beat us all the time.

When do we beat Mexico at the border? They’re laughing at us, at our stupidity. And now they are beating us economically. They are not our friend, believe me. But they’re killing us economically.

There is enough ambiguity in these statements to at least conclude that they are not indicative of material interests in isolation, and we would go as far as to say that they fall closer to Mutz’s conception of tapping into status threat as a sense of declining “global status.” According to McCall and Orloff (2017), these sorts of appeals from Trump, which highlight American economic decline, lay at the intersection of whiteness, masculinity, and blue-collar work. Per Mutz’s theory, white men should be particularly sensitive to making declining global status salient, as their identities are most directly tied to folk theories of the United States. These considerations suggest that there is not sufficient evidence to reject Mutz’s classification as trade as a status threat variable.

China: Much of the debate over whether China threat should count as a status threat or material interest variable echoes the debate over trade. However, the specific wording used to measure China threat warrants brief discussion. Whether one considers the item “China provides new markets and is an investment opportunity or is a threat to our jobs and security” to be tapping material interests or status threat likely depends on whether one focuses on the word “China” or “jobs.” Our read of Mutz’s paper leads us to conclude that her decision to categorize this statement as tapping into status threat is at least defensible. This is because it is entirely reasonable to interpret Donald Trump’s rhetoric on China as tapping into American exceptionalism in the abstract sense — lamenting that foreign countries were “beating” the United States — as much as it was making a direct promise to improve workers’ material well-being. This fits neatly within the political psychology literature that Mutz cites to establish her status threat hypothesis, in which those higher in domestic racial categories should be more heavily invested in the United States’s global standing. As discussed above and as cited in Mutz’s paper, this relationship has been established in experimental settings. While Morgan wouldn’t necessarily be in error to treat an item that mentions jobs as an item that taps into material interests in a separate work, the specific theory that informs Mutz’s paper suggests that it should be categorized as a status threat variable as opposed to an economic variable, and we see no clear reason why this should change.

Immigration: Core to this dispute is the question of immigration attitudes. Elsewhere, scholars have found that beliefs about immigration are crucial for explaining the 2016 election (McDaniel and McElwee 2017, Gimpel 2017). And as we will show below, immigration attitudes specifically account for much of the dispute in the models.

There are good reasons to believe that immigration attitudes — especially among white voters — are consistent with Mutz’s status threat formulation. Again referring to Rothwell and Diego-Rosell (2016), actual exposure to immigration was not meaningfully associated with favorable views toward Trump during the primary campaign. Furthermore, those with favorable views toward Trump were no more likely to be unemployed, and had relatively higher household incomes, compared to those with less favorable views.

Additionally, in our own work, we have found an inverse relationship between geographic exposure to immigration and anti-immigrant views. For instance, the share of voters in a given state who endorse the belief that undocumented immigrants commit more crimes than native-born citizens steadily falls as the share of the state’s population that can be accounted for by new immigrants arriving between 2010 and 2016 increases (McElwee 2018). Put another way, people in the states with the least exposure to actual immigration are, on average, those who are most likely to feel threatened by immigration. While this doesn’t completely rule out the claim that anxiety over immigration is grounded in material economic interests, in our view it renders explanations grounded in status threat simpler, and at the very least defensible.

Terrorism and Isolationism: Along with immigration, Morgan categorizes concerns over threats posed by terrorism and isolationism into a separate, broad category that he terms “material interests and foreign policy.” Neither he nor Mutz discuss the theoretical justification for how they categorize each of these variables, and in our view they are the trickiest to place. As is the case with immigration, anxieties over terrorism do not flow from actual exposure to terrorism — practically no one is actually at risk of dying in a terrorist attack (Mueller and Stewart 2012). Nevertheless, in the context of Mutz’s theory, it also isn’t clear that terrorism presents the same threat to group status that, say, immigration does. The latter represents a threat to white Protestants that they will no longer be placed atop the United States’ domestic social hierarchy; the former represents a threat to personal, community, and/or national safety — real or imagined.

Isolationism is even trickier, as only one of the items used to build the five-item index included in Mutz’s model (“take care of the well-being of Americans and not get involved with other nations”) can be reasonably interpreted as a measure of material interests. The other items deal more directly with foreign policy, which informs Morgan’s separate “Material Interests and Foreign Policy” categorization. For readers like us who are not as deeply familiar with the political psychology literature, it is not immediately clear how isolationism neatly fits in with the theory of status threat. Its inclusion receives no discussion in Mutz’s paper or supplementary materials.

Why This Matters: In broadening the conceptual basis that represents a respondent’s tangible wellbeing, Morgan tests a different hypothesis than Mutz, moving away from her intended audience and the narrative to which she aims to respond. Morgan’s definition of “material interests” concerns more than pocketbook issues identified by the journalists with whom Mutz takes issue. If one has to squint in order to see isolationism or being worried about terrorism as an indication of status threat, one has to squint even harder to see them as an indication of economic distress. By itself, this doesn’t mean that Morgan’s conceptualization is wrong, but it does mean that he is addressing a different question than the one Mutz asked.

Model Comparison

We compared several different models to determine if the modeling choices made in Mutz’s original manuscript were appropriate. In our replication models, we use a Bayesian approach with different combinations of variables. In addition, we compared a random effects model to assess the original fixed effects model employed by Mutz. Models were compared with an approximation to leave one out cross validation using Pareto smoothed importance sampling (Vehtari, Gelman, and Gabry 2017).

We fit and compared a total of 6 models, the first being the original fixed effects model with all variables included and the second being a random effects variant of the same model. The remaining four models used fixed effects, but only a subset of the available variables beyond the baseline variables. The first of these models included the variables which both authors categorized as status threat and the second included the variables which were categorized as economic interest by both authors. The third model included the variables whose category was disputed, and a fourth model only included immigration. We repeated the model comparison exercise using the full sample and an indicator for white in the baseline variables, in addition to comparing the same set of models on the white respondents only, as well as a comparison with party ID excluded from the set of predictors.

We find that while the random effects model is a better fit to the data, due to the high model complexity relative to the fixed effects model, the expected out of sample fit is better for fixed effects. The inclusion of variables has a much larger effect on predictive fit however, and the variables whose categorization is disputed, most notably immigration, have the largest effects. The conclusions from this analysis are completely unchanged by restricting the sample to white respondents. Based on this dataset, the most important aspect of this discussion is how the disputed variables (especially immigration) should be categorized.

Screen Shot 2018-05-30 at 11.42.06 PM.pngAs the model comparison plot shows, the variables on which the authors disagree are the variables that do the most to add explanatory power to Trump voting above the baseline variables. We further find support for Mutz’s decision to use a fixed effects model, which performs better out-of-sample than the random effects specification.


Morgan concludes his paper by arguing that we still lack the necessary data to make a causal claim that sensitivity to status threat and not economic self-interest produced the outcome of the 2016 election. This is not a narrow point, as it stands in direct contrast to Mutz’s title, conclusion, and public interpretation in the media. And here, we agree that the analyses used to test status threat and economic concerns against each other as predictors of Trump voting, replicated in Morgan and above, are unable to make use of the panel dataset given the small number of observed swing votes. So what we are left with is panel evidence that key components of status threat were made salient in 2016 in ways that they weren’t in 2012, and cross-sectional evidence that these status threat variables explain more variation in one-shot Trump voting than economic variables. These combined analyses, in the context of the experimental evidence cited by Mutz (e.g. Craig and Richeson 2014; Willer, Feinberg, and Wetts 2016), tells a compelling causal story — even if the model used to explain 2016 voting behavior does not support a direct causal claim.  

But even without direct causal observation, these are interesting findings in their own right, and we feel they can be reasonably interpreted as evidence that status threat played a bigger role in organizing white working class attitudes toward the 2016 election than direct economic self-interest. Of course, this is not to say that economic interests didn’t matter at all. It is certainly the case that some working class white voters selected Trump because they genuinely believed that he would improve their economic conditions, and academic scholarship supports this (e.g. Valentino et al. 2018). However, it is also certainly the case that some working class white voters picked him because they felt he would “take our country back” from unspecified out-groups that threaten archetypal American ways of life. Moreover, it is the latter characterization that is likely unique to 2016.


Our analysis suggests that the core findings of Mutz 2018 hold up to scrutiny. The question of whether attitudes toward trade, China, and immigration should be characterized as “status threat” or a “material interest” is interesting, but classifying these as “status threat” fits within a broad literature, and does not merit the designation of “fake news.” We do not find evidence that the key model presented in Mutz 2018 is mis-specified. Conceptually, and operationally, “status threat” adds to our understanding of support for Donald Trump and the outcome of the 2016 election.


Craig, Maureen and Jennifer A. Richeson. 2014. “More diverse yet less tolerant: How the increasingly diverse racial landscape affects white Americans’ racial attitudes.” Personality and Social Psychology Bulletin 40 (6): 750-761.

Gimpel, James G. 2017. “Immigration Policy Opinion and the 2016 Presidential Vote.” https://cis.org/Report/Immigration-Policy-Opinion-and-2016-Presidential-Vote. Online; posted December 4, 2017.

Lamont, Michèle, Bo Yun Park and Elena Ayala-Hurtado. 2017. “Trump’s Electoral Speeches and His Appeal to the American White Working Class.” The British Journal of Sociology 68: 153-180.

McCall, Leslie, and Anna Shola Orloff. 2017. “The multidimensional politics of inequality: Taking stock of identity politics in the US Presidential election of 2016.”The British Journal of Sociology https://doi.org/10.1111/1468-4446.12316

McDaniel, Jason, and Sean McElwee. 2017. “Fear of Diversity Made People More Likely to Vote Trump.” The Nation, March 14, 2017. https://www.thenation.com/article/fear-of-diversity-made-people-more-likely-to-vote-trump/.

McElwee, Sean. 2018. “Anti-immigrant sentiment is most extreme in states without immigrants.” https://www.dataforprogress.org/blog/2018/4/5/anti-immigrant-sentiment-is-most-extreme-in-states-without-immigrants.

Morgan, Stephen L. 2018. “Fake news: Status threats does not explain the 2016 Presidential Vote.” doi:10.17605/OSF.IO/EJSB2.

Mueller, John, and Mark G. Stewart. 2012. “The Terrorism Delusion: America’s Overwrought Response to September 11”. International Security https://doi.org/10.1162/ISEC_a_00089.

Mutz, Diana. 2018. “Status threat, not economic hardship, explains the 2016 presidential vote.” Proceedings of the national Academy of Sciences https://doi.org/10.1073/pnas.1718155115.

Rothwell, Jonathan T., and Pablo Diego-Rosell. 2016. “Explaining nationalist political views: The case of Donald Trump.” http://dx.doi.org/10.2139/ssrn.2822059.

Swedberg, Richard. 2018. “Folk Economics and Its Role in Trump’s Presidential Campaign: An Exploratory Study.” Theory and Society 47:1-36.

Theiss-Morse, Elizabeth. 2009. Who counts as an American? The boundaries of national identity. New York, NY: Cambridge University Press.

Valentino, Nicholas, Carly Wayne, and Marzia Oceno. 2018. “Mobilizing sexism: The interaction of emotion and gender attitudes in the 2016 US Presidential Election.” Public Opinion Quarterly 82 (1): 213-235.

Vehtari, Aki, Andrew Gelman, and Jonah Gabry. 2017. “Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.” Statistics and Computing 27 (5): 1413-1432.

Willer, Robb, Matthew Feinberg, and Rachel Wetts. 2016. “Threats to Racial Status Promote Tea Party Support Among White Americans.” (SSRN Scholarly Paper No. ID 2770186) (Social Science Research Network, Rochester, NY).


WPSA Statement of Resignation of Drexel Professor, George Ciccariello-Maher

The Western Political Science Association notes with grave alarm the resignation of Drexel University’s George Ciccariello-Maher as of December 31, 2017. Despite a formal statement in which Drexel acknowledged his “significant scholarly contributions” and “outstanding” teaching record, it had placed him on leave making it impractical for him to practice his craft. Ciccariello-Maher’s resignation is a crucial reminder that academic freedom cannot survive, let alone flourish, without the full support of the entire university community. No one should be placed in Professor Ciccariello-Maher’s positon and be required, in the face of political pressure and personal and familial death threats, to embody and defend the core principles to the mission of higher education by themselves. His fate should serve as a call to arms to the academic community at large. Organized assaults against academic freedom continue.

-Western Political Science Association Policy Committee

Ten Insights Regarding Sexual Harassment and Diversity in the Academy

Loan K. Le


The flood of #MeToo revelations has demonstrated the commonality of sexual harassment and how regularly these claims go unreported, often due to personal safety, and professional retaliation fears. While the women’s movement ushered in legal recourse for sexual harassment claims, many women and men do not exercise their rights. Sociolegal scholars explain what happens to complaints on the ground as an exercise of political power that can quash the rights of those with limited resources. Amy Blackstone, Christopher Uggen and Heather McLaughlin argued in Law and Society Review that assailants often choose women who are least likely to complain. As Anna Maria Marshall and Abigail Saguy have argued, people and workplace organizations explain problems in ways that limit their meaning as unequal working conditions, or sexual assault. Taken together, institutionalized and socioeconomic barriers influence the degree to which individuals are affected by sexual harassment and their access to recourse.

The #MeToo revelations have drawn attention to additional hurdles that women face in the work place, and this includes those of us working in higher education. However, we have yet to see a discussion of the systematic problems in the academy. Here, Loan Le, President of the Institute of Good Government and Inclusion, takes up the invitation in the New West blog to reflect on how sexual harassment, stalking, and assault likely affects gender representation in the academy. Here, she presents ten early insights from an ongoing study.

Susan Sterett and Jennifer Diascro

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It’s Not “Just A Cake”: Masterpiece Cakeshop v. Colorado Civil Rights Commission

By Lorna Bracewell

Gay Wedding Cake

On Tuesday, December 5, 2017, the U.S. Supreme Court heard oral arguments in a case that court-watchers are calling the biggest case of the termMasterpiece Cakeshop Ltd. V. Colorado Civil Rights Commission. The facts of the case are not in dispute. In 2012, David Mullins, Charlie Craig, and Deborah Munn, Craig’s mother, went to Masterpiece Cakeshop, a Denver-area bakery, to order a specialty wedding cake. After a brief conversation, the bakery’s owner, Jack Phillips, refused to make a cake for the same-sex couple on the grounds that he is a Christian and believes same-sex marriage is sinful.

Humiliated by Phillips’s refusal – in an interview with NPR, Craig’s mother recalls her son breaking down in tears once they returned to the car – Mullins and Craig decided to file a complaint against Masterpiece Cakeshop with Colorado’s state commission on civil rights. Colorado is one of only 21 states with a law prohibiting discrimination in public accommodations on the basis of sexual orientation and one of only 19 states with a law prohibiting such discrimination on the basis of gender identity. The only federal legislation affording LGBT people any form of anti-discrimination protection is the Matthew Shepherd and James Byrd, Jr. Hate Crimes Prevention Act, which makes hate crimes based on sexual orientation or gender identity federal crimes.

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Advancement Through Narrative: Reflecting, Listening, Changing

By Jennifer Diascro and Susan Sterett


“I need to build a house for my house.”  So began a reflection at a workshop that brought together faculty and administrators from political science to discuss what graduate students and faculty members need in the academy to thrive. What allows people to have a house for their house?

More often than not, when we think about how to thrive we try to take advice about how to succeed in our particular work settings. And this individualized information can be very valuable. Yet, our tendency is to provide suggestions that work within existing institutions. To be sure, we may share what hasn’t been effective, but most advice comes from a position of successfully navigating current structures by those who have benefitted from them. Among the notable lessons from the recent explosion of news about sexual assault in the public and private sectors is what happens when people in a profession treat persistent practices as universal and out of our control. We have legitimate complaints that we may vocalize from time to time, but mostly we work around the problems we encounter and go about our business because the norms of behavior and structures for advancement seem to provide us few choices. This is particularly true for the more disadvantaged among us, including in the academy, who are encouraged to choose strategically about family, and to negotiate individually and collectively. While well-meaning, the concern is that this contributes to an already lopsided playing field, where the burden rests on those with more to lose.

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Metrics, Metrics, (Alt)metrics

By Amy Atchison


If it sometimes feels like success in academia boils down to metrics, that’s because in many US institutions it does (sadly) boil down to metrics. We all know that it isn’t just the number of publications you have. It’s also the impact factor of the journals in which those articles were published, the citation count per article, and your h-index score. (And don’t get me started on the non-research metrics, like course evaluations—which we all know are notoriously flawed. See here, here, and here.) Those are all pretty common measures that are widely used. But a recent Twitter thread indicated to me that some political scientists may not be aware of a new(ish) measure that can help to quantify use of your non-publication outputs as well as your social media reach: altmetrics. This is helpful if your institution puts a premium on public engagement.

Altmetrics are simply alternative measures of scholarly reach/output. They include measures like the number of downloads of your work from your institutional repository or number of mentions on social media. The usage metrics provided by Academia.edu or Research Gate are also considered alternatives to traditional metrics (use the latter with caution, though).

In this post, I focus on Altmetric Badges from Altmetric.com because they aggregate many sources of attention and because many leading journals have started adding Badges to their sites. I give a brief overview of altmetrics, including how they can be used in promotion and tenure (P&T) applications, as well as the pros/cons. I end the post with a brief overview of the problems inherent in many of the traditional measures we use to evaluate scholarship (citation counts, journal impact factors, etc.) since I have found that almost no one tells people these things in grad school. (But they’re helpful to know!)

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