Omar Wasow

@owasow.bsky.social

39184 Followers 4166 Following

Asst Prof, Berkeley, Political Science: 1/ Agenda Seeding http://j.mp/agenda-seeding 2/ Race as a Bundle of Sticks http://j.mp/bundle-of 3/ Text as Behavior https://cup.org/4cUmoXi 4/ Plutopopulism http://cup.org/4cfm0Az

Title page of an academic working paper titled The Electoral Effects of Anti-Authoritarian Protest: Evidence from the West German Student Movement. The author is Julian Voß, and the paper is dated July 6, 2026.
A minimalist rendering of Leonardo da Vinci's *Vitruvian Man* appears against a light gray background. The nude male figure is shown in two superimposed positions, with one set of arms extended horizontally and another angled upward, while one pair of legs stands together and another spreads outward. A thin black square frames the figure's torso and lower body, and a large circle surrounds the entire composition, intersecting the square in the classic geometric arrangement. The figure is depicted in muted sepia tones with visible sketch-like shading and anatomical detail, while the surrounding circle and square are rendered as simple black outlines. The overall image evokes ideas of proportion, symmetry, human movement, and the relationship between the human body and geometric ideals.
Photo of Jeff Bezos, smiling, in front of red background. His physique looks thin in a loose fitting sweatshirt.
Alt text: A white fee schedule sign with dark blue text is mounted on a gray wall using metal fasteners at each corner. The sign lists towing and storage charges for vehicles impounded by the California Highway Patrol, Marin County Sheriff, and Novato Police Department. Fees include an hourly rate of $360.00, storage at $180.00 per calendar day, a $180.00 gate fee for after-hours, weekends, and holidays, and lien fees of $70.00 for amounts under $4,000 and $100.00 for amounts over $4,000. A paragraph near the bottom explains that vehicles stored under California Vehicle Code section 22850 may only be released to authorized individuals with proper documentation or agency approval. Contact numbers for the California Highway Patrol, Novato Police Department, and Marin County Sheriff Office are listed below. The sign concludes with a notice stating that all vehicles are subject to a lien or charge upon storage beyond 72 hours. Four small yellow markers are visible on the gray wall surrounding the sign.
Photo of a white Waymo robotaxi in San Francisco. Headline: Get-a-Waymo: How a burglar used a robotaxi to flee the scene in a first-of-its kind S.F. case Sub-head: After breaking into a yoga studio in San Francisco and stealing activewear, a burglar made his getaway in an autonomous Waymo in what police said was a novel case in the city.
A retro 1980s-style aerobics workout class performs synchronized exercises in a brightly lit fitness studio. At the center, an instructor with a thick mustache and headband leads the group in a wide squat stance with arms extended forward. Participants wear colorful spandex workout outfits in neon pink, turquoise, purple, black, and white, along with leg warmers, headbands, and athletic shoes. The studio has pastel-colored walls decorated with stylized palm tree graphics, and a large “Buns of Steel” logo is displayed prominently on the back wall. The image has the soft, slightly grainy appearance of a vintage exercise VHS video.
Katie Couric stands outdoors in front of a crowd gathered behind metal barricades. The presenter wears a shiny metallic silver jacket with black accents and has a sleek, platinum-blonde bob haircut that partially covers one eye. Several spectators in casual clothing watch from behind, including people holding cameras and signs. The image appears to be from a live morning television broadcast, with the crowd and urban setting creating an event atmosphere.
A television segment is being filmed outdoors in a busy urban plaza. Two people in futuristic, metallic-themed outfits stand beside a small display table covered with electronic gadgets, including portable music players, watches, handheld devices, and other consumer electronics. The person on the left wears a white sleeveless hooded top over a black long-sleeve shirt and appears to be explaining or demonstrating an item. The person on the right wears a silver-and-black outfit with structured, armor-like panels and is looking down at the products on the table. Behind them, spectators stand behind metal barricades, with storefronts, signs, and a large “Today 2024” banner visible in the background. A television news ticker and station graphics run along the bottom of the screen.
Two-panel chart showing how Republican party affiliation predicts ethnoracial reidentification, before and after controlling for perceived race (measured from Florida Highway Patrol officers' classifications during traffic stops). Top panel (Hispanic to White): effect drops from about +2 percentage points to about +0.7 points after the control but remains above zero. Bottom panel (White to Hispanic): effect is about -0.13 points before and -0.12 points after—essentially unchanged. Both directions remain significant. Sample: Florida voters linked to citation records, 2012-2019.
The Race of Politics: Partisan Affiliation and Ethnoracial Boundary Crossing* Abstract While a long literature has explored ethnoracial patterns of party affiliation and realignment, this paper takes an inverse approach, examining whether in- dividuals’ political affiliations predict shifts in their ethnoracial self-identifications. Using eighteen years of individual voter records from Florida, we show that rates of ethnoracial reidentification among voters are substantively large and patterned by party affiliation. Republicans are significantly more likely than Democrats to change their ethnoracial identities from Hispanic to non-Hispanic White, while Democrats are significantly more likely than Republicans to change their ethnoracial identities from non-Hispanic White to Hispanic. Even after accounting for differences in perceived race and other possible confounding factors, we find large and stable differences between Democrats and Republicans in both the rate and direction of ethnoracial reidentification, suggesting that partisans may both differently value and differently claim Whiteness and non-Whiteness.
Photo backstage at Colbert Report! with Stephen Colbert, my 85 year old grandmother and me. We're standing side by side, with Colbert half smiling with raised eyebrows and me smiling widely. My grandmother is standing in the middle with glasses and a neutral expression.
Screen grab from Professor X wikipedia page: ... Xavier's pacifist and assimilationist ideology and actions have often been contrasted with that of Magneto, a mutant leader (initially characterized as a supervillain and later as a complex antihero) with whom Xavier has a complicated relationship. Fans and commentators often compare Xavier and Magneto to Martin Luther King Jr. and Malcolm X respectively, though the amount of authorial intent supporting this comparison is disputed.[2] Writer Chris Claremont, who developed Xavier's characterization and originated Magneto's backstory, drew from Israeli politics, comparing Xavier to David Ben-Gurion and Magneto to Menachem Begin.[3] Later writers have additionally emphasized secretive, ruthless, and manipulative aspects of Xavier, while the later "Krakoan Age" storyline sees him align with Magneto to establish a sovereign mutant nation.
View of the World from 9th Avenue A stylized magazine cover for The New Yorker fills the frame. At the top, the magazine’s title appears in large black letters against a cream background. Below, the image presents a cartoonish, exaggerated perspective looking west from Manhattan. In the foreground are several city blocks around 9th and 10th Avenue, drawn in detailed lines and pastel colors. Streets are busy with tiny pedestrians, parked cars, trucks, storefronts, rooftops, and traffic lights. The buildings closest to the viewer are tall and densely packed. Beyond the foreground cityscape lies a narrow strip labeled “Hudson River,” then an enormous flat green expanse representing the rest of the United States. A few scattered labels and landmarks appear across the simplified landscape, including “Jersey,” “Chicago,” “Kansas City,” “Nebraska,” “Utah,” “Las Vegas,” “Texas,” and “Los Angeles.” Small rock-like formations rise from the plain in a few places. Farther back is a thin strip labeled “Pacific Ocean,” and at the horizon are tiny slivers labeled “China,” “Japan,” and “Russia.” “Mexico” and “Canada” appear at the far left and right edges. The exaggerated scale humorously depicts Manhattan as overwhelmingly important while the rest of the world is compressed into distant, simplified fragments.
Sixth, the open-ended nature of text responses—and the nearly infinite number of ways they can be quantified—make them difficult to “game.” Rather than relying on what respondents say, metadata about how they respond, such as length or latency, acts like a behavioral “derivative,” capturing traces of engagement apart from content. Because respondents are typically unaware these features are being measured, such implicit indicators benefit from what we might call metric opacity, making them less susceptible to social desirability or other forms of strategic responding.
Credit from the Film Hackers reads Omar Wasow, Hacking consultant: New York
Screen capture of a tweet that shows a Trump press conference with text: Trump: Gas prices are way down. Have you looked?
Third, more research is needed on detecting fraud in open-ended survey items. As AI tools proliferate, researchers face new forms of automated or AI-assisted deception (Westwood Reference Westwood2025). For example, Veselovsky et al. (Reference Veselovsky, Ribeiro, Cozzolino, Gordon, Rothschild and West2025) capture keystrokes, including copy–paste actions, to identify synthetic text production. Other behavioral traces, such as typing speed, keystroke regularity and mouse movements, can flag automation, though such signals can themselves be forged (Westwood Reference Westwood2025). These process-based indicators differ from the content-level mischief described by Lopez and Hillygus (Reference Lopez and Hillygus2018), where respondents intentionally provide absurd answers for amusement.
Figure 4. Marginal effects of Importance of Democracy Scale on length of open-ended responses about democracy, by language. Three-panel figure showing the predicted number of characters written in response to the question “What, if anything, does democracy mean to you?” as a function of respondents’ scores on an “Importance of Democracy” scale. Separate panels display results for English, French, and Portuguese language respondents. Shaded bands around each line indicate uncertainty intervals. The x-axis in each panel represents the Importance of Democracy Scale, ranging from “Low” to “High.” The y-axis shows the predicted number of characters written in the open-ended response. In the English panel, a solid blue line slopes upward from approximately 33 predicted characters at low levels of democratic importance to roughly 45 characters at high levels. The increase is gradual and approximately linear. In the French panel, a red dotted line shows a steeper upward trend, rising from roughly 31 predicted characters at low democratic importance to nearly 50 characters at high levels. This panel displays the strongest positive relationship among the three language groups. In the Portuguese panel, a purple dashed line also slopes upward, increasing from about 27 predicted characters at low levels of democratic importance to approximately 40 characters at high levels. Confidence intervals are somewhat wider at lower values of the scale. Across all three language groups, higher support for democracy is associated with longer open-ended responses about the meaning of democracy. The figure suggests that respondents who place greater importance on democratic governance tend to provide more expansive or expressive textual answers, even after adjusting for demographic and socioeconomic covariates.
Figure 3. Marginal effects of Expressive Engagement in 2016 on validated turnout in 2016 and 2020. Two-panel figure showing predicted probabilities of validated voter turnout as a function of “Expressive Engagement 2016” among registered voters. Both panels display fitted regression lines with shaded uncertainty intervals representing confidence bands around the estimates. In **Panel A**, the x-axis measures Expressive Engagement in 2016, ranging from 0 to nearly 4. The y-axis shows the predicted probability of validated turnout in the 2016 election. The fitted line slopes upward steadily, indicating that higher levels of expressive engagement are associated with a greater likelihood of voting. Predicted turnout increases from roughly 70% at the lowest level of expressive engagement to approximately 85% at the highest level. The shaded confidence interval widens slightly at higher values of expressive engagement. In **Panel B**, the x-axis again measures Expressive Engagement in 2016, while the y-axis shows the predicted probability of validated turnout in the 2020 election. The upward relationship is similar but somewhat more uncertain than in Panel A. Predicted turnout rises from approximately 65% at the lowest level of expressive engagement to just above 80% at the highest level. Confidence intervals are wider at lower and higher ends of the scale, reflecting greater uncertainty in the estimates. Together, the panels illustrate that higher expressive engagement in 2016 is associated with substantially higher probabilities of validated voter turnout both contemporaneously in 2016 and longitudinally in 2020, even after adjusting for demographic, political, and behavioral covariates.
Figure 2, Panel B. Marginal effects of Expressive Alignment on probability of voting for Trump in 2016. Line plot showing predicted probability of voting for Donald Trump in 2016 as a function of “Expressive Alignment 2016,” estimated separately for Democrats, Independents, and Republicans while controlling for additional covariates. Shaded regions around each line represent uncertainty intervals. The x-axis ranges from −1 (“Clinton+”) to +1 (“Trump+”), indicating whether respondents’ expressive responses aligned more strongly with Clinton or Trump. The y-axis shows the predicted probability of voting for Trump in 2016. Three party groups are displayed: Independents in purple with a solid line, Democrats in dark blue with a dashed line, and Republicans in red with a dashed line. Democrats and Independents both show strong positive relationships between expressive alignment and probability of voting for Trump. Democrats begin near zero probability of voting for Trump at the strongly Clinton-aligned end of the scale, then rise sharply as expressive alignment becomes more Trump-oriented, reaching over 50% at the strongly Trump-aligned end. Independents follow a similar but less steep upward pattern, increasing from very low levels to roughly 40% probability at the Trump-aligned end. Republicans display a comparatively flat slope, maintaining a relatively stable predicted probability of voting for Trump across the expressive alignment scale, with only a modest increase from left to right. The figure illustrates that expressive alignment strongly predicts Trump support among Democrats and Independents, while variation among Republicans is comparatively limited, suggesting that expressive alignment captures substantial variation in candidate choice beyond standard partisan and affective measures.
Figure 2. Marginal effects of Expressive Alignment on 2016 candidate choice. Line plot showing predicted probability of voting for Hillary Clinton in 2016 as a function of “Expressive Alignment 2016,” estimated separately for Democrats, Independents, and Republicans while controlling for additional covariates. Shaded bands around each line indicate uncertainty intervals. The x-axis ranges from −1 (“Clinton+”) to +1 (“Trump+”), representing the degree to which respondents’ expressive responses aligned more strongly with Clinton or Trump. The y-axis shows the predicted probability of voting for Clinton in 2016. Three party groups are displayed: Democrats in dark blue with a dashed line, Independents in purple with a solid line, and Republicans in red with a dashed line. Democrats maintain relatively high predicted probabilities of voting for Clinton across the full range of expressive alignment, with only a modest downward slope as alignment becomes more Trump-oriented. Independents show a steeper decline, dropping from roughly 40% probability of voting for Clinton at the strongly Clinton-aligned end to under 10% at the strongly Trump-aligned end. Republicans show the steepest decline, falling from nearly 50% probability of voting for Clinton at the Clinton-aligned end to near zero at the Trump-aligned end. The figure illustrates that expressive alignment strongly predicts candidate choice among Independents and especially out-party partisans, while co-partisans’ predicted probabilities remain comparatively stable across the alignment scale.
Figure 1. Marginal effects of nonresponse on probability of selecting a candidate in 2016. Two-panel plot showing predicted probabilities of voting for Hillary Clinton (Panel A) and Donald Trump (Panel B) by the number of “informative nonresponses,” with separate estimates for Independents, Democrats, and Republicans. Error bars indicate uncertainty intervals around the estimates. In Panel A, the x-axis measures the number of nonresponses to questions asking what respondents liked “for Clinton” or disliked “against Trump.” The y-axis shows the predicted probability of voting for Clinton. Democrats begin with the highest predicted probability of voting for Clinton at zero nonresponses, followed by Independents and Republicans. As informative nonresponse increases from 0 to 2, predicted support for Clinton declines across all party groups, with especially steep declines among Republicans and Independents. In Panel B, the x-axis measures the number of nonresponses to questions asking what respondents liked “for Trump” or disliked “against Clinton.” The y-axis shows the predicted probability of voting for Trump. Republicans maintain relatively stable levels of support for Trump across all levels of nonresponse. In contrast, Democrats and Independents show substantial declines in predicted support for Trump as nonresponse increases, falling from roughly similar levels as Republicans at zero nonresponse to much lower levels at higher nonresponse counts. The figure illustrates that higher levels of informative nonresponse are associated with lower predicted support for the corresponding candidate among most partisan groups, although the relationship is weak or absent among Republicans in the Trump-support model.
Text as Behavior Text as Behavior published in Political Analysis by Omar Wasow Abstract Text analysis typically focuses on content—such as sentiment or topic—but expression is also a form of effortful action. Building on this insight, I propose using simple features of open-ended tasks to study text as behavior. This approach treats expression, such as writing, as cognitively, emotionally and temporally “costly” for subjects but inexpensive for researchers. I show basic statistics like the number of characters can approximate effort and significantly improve estimation of quantities of interest, including candidate choice, the probability of turning out to vote and psychological states about which a subject may not be fully aware. Further, these methods can convert nonresponse into informative data; validate survey instruments; serve as mechanism checks; be hard for a subject to “game”; work across different languages and analogize well to real-world situations. In sum, text as behavior can help address a range of issues related to quantifying attitudes and actions.
Racial and ethnic composition of Mississippi. Shows non-Hispanic white at about 58% and Black at about 38%. All other groups at about 1 to 3.6%
Front page of *The Commercial Dispatch* newspaper dated Friday, May 1, 2026. The main headline reads “Freedom Summer visit to highlight Eighth of May.” A highlighted subheading mentions Bernard Wasow, who taught in a Freedom School in Columbus, being portrayed by an MSMS student at an annual event. To the right, a photo shows a young man outdoors mid-performance, gesturing as if speaking or acting. A caption explains he is portraying a Freedom Summer volunteer during the Eighth of May Emancipation Celebration. At the top right, a smaller sports feature shows a softball player throwing a ball with the headline “Onward and Upward.”
Hebrew was not a natural choice of a common language. Zionist intellectuals, intent on creating a new Hebrew nation, were divided as to the choice of the language. For example, Theodor Herzl was not quite aware of the extent of the use of modern Hebrew by Zionist settlers in Palestine, and preferred German as the lingua franca of his New Old Land. Moreover, he paid little attention to the role of the language in fostering a new national identity. The use of German became a casus belli several years after Herzl’s death. At issue was the language of instruction at the Technion, a technical university founded largely by German Jews, who naturally expected German to be the language of instruction. In the event, the Russian Zionists won out and affirmed the monopoly of modern Hebrew for fostering the new nation. This was the first but not the last victory of Russian Zionists over their German brethren. In spite of their remarkable contributions to Israel’s economic, cultural and military successes, German Jews could never compete in the political arena with the far more assertive and resourceful Zionists of Russian and other East European provenances. Since Zionists viewed Yiddish as the main threat, Israeli regulations made it illegal to establish a Yiddish theatre, publish a daily in the Yiddish language, establish Yiddish schools, and other Yiddish-language institutions. Political and ideological considerations superseded all others. Yiddish had to be combated more vigorously than any other language, for the simple reason that it threatened the hegemony of Hebrew.
The U.S. is increasingly governed by minority rule, and Callais is the latest symptom. Democrats have won the effective popular vote for the Senate in every cycle but one since 1992. Nonetheless, two presidents who lost the popular vote in their first elections went on to stack the Supreme Court with justices confirmed by that minority-elected Senate. That court just trashed legislation 67% of voters say is still needed — and 65% of voters now support term limits for the justices.
Poster for the “Eighth of May Emancipation Celebration,” an MSMS MoreStory performance on May 8, 2026 at 6 PM at Sandfield Cemetery in Columbus, Mississippi. Features special guest poet A.H. Jerriod Avant and student performers; event is free. **Extended description (contextual):** A green-and-white event flyer announces the “Eighth of May Emancipation Celebration,” a performance project by the Mississippi School for Mathematics and Science (MSMS). It lists the date (May 8, 2026 at 6 PM) and location (Sandfield Cemetery, Columbus, MS). The program highlights performances by students from MSMS, Columbus High School, Columbus Middle School, and Mississippi University for Women, using song, spoken word, step, and drama to honor the history and legacy of African Americans in the region. A circular photo shows special guest A.H. Jerriod Avant, identified as a Mississippi Institute of Arts & Letters Poetry Award winner. Along the bottom are photos of student performers outdoors, an “As seen on HBO” badge, a QR code, and logos of sponsoring organizations including MSMS, MUW, Columbus Municipal School District, Columbus-Lowndes Public Library System, and the City of Columbus.
O WASOW: Papa, can I ask you a question? Just how does it feel to watch the Voting Rights Act get eviscerated by a thousand cuts? B WASOW: I just don't understand how people like the judges on the Supreme Court can do that. I mean, how could they have so little sense of history? Or is it truly that they are - that they have ill will? I find it astonishing.
Bar chart titled “Americans increasingly assign negative traits to Trump.” It shows the difference between the share saying each trait applies to Donald Trump and the share saying it does not, comparing Dec. 2025 (green) and Apr. 2026 (yellow) YouGov/The Economist polls. Traits on the x-axis: Inspiring, Honest, Steady, Intelligent, Effective, Strong, Racist, Out-of-touch, Cruel, Corrupt, Divisive, Bold, Dangerous. Results: Positive traits (inspiring, honest, steady, intelligent) are net negative in both waves and generally more negative in April 2026. “Effective” is near zero/slightly positive. Negative traits (racist, out-of-touch, cruel, corrupt, divisive, dangerous) are net positive and increase from December to April. “Bold” is strongly positive in both waves, slightly lower in April. “Dangerous” shows one of the largest increases, rising to the highest net positive in April. Source: FiftyPlusOne; chart by Datawrapper.
A bear suit laid out on a wooden floor, complete with claws, is part of the evidence that has been used to convict three individuals for staged “bear” attacks on luxury cars for insurance payouts. (California Department of Insurance)
11. Which one of the following episodes in American history took the most courage?...The passengers of Flight 93 stopping the hijackers on September 11th (2001, the date of the terrorist attacks on the World Trade Center and the Pentagon), Harriet Tubman helping escaped slaves on the Underground Railroad, Martin Luther King marching at Selma, the Founders signing the Declaration of Independence, John F. Kennedy holding steady during the Cuban Missile Crisis, Jonas Salk testing the polio vaccine on himself 29% The passengers of Flight 93 stopping the hijackers on September 11th 21% Harriet Tubman helping escaped slaves on the Underground Railroad 16% Martin Luther King marching at Selma 13% The Founders signing the Declaration of Independence 10% John F. Kennedy holding steady during the Cuban Missile Crisis 6% Jonas Salk testing the polio vaccine on himself 4% Don't know/No answer Survey Information Survey Organization: CBS News Survey Sponsor: 60 Minutes; Vanity Fair Study Date: November 5, 2014 - November 9, 2014 Sample: National adult Sample Size: 1018 Geographic Coverage: United States Interview Mode: Telephone Interview
Die stabilisierende Wirkung von linkem Populismus in der Demokratiebewegung – „Gemeinsam gegen Rechts, für Demokratie und Vielfalt“ Ronja Hofmann · Stefan Matern Eingegangen: 11. Juni 2025 / Überarbeitet: 8. März 2026 / Angenommen: 9. März 2026 © The Author(s) 2026 Zusammenfassung Ausgelöst durch die Correctiv-Recherche über ein Treffen rechtsextremer Akteure in Potsdam gingen Anfang 2024 in ganz Deutschland Hun- derttausende auf die Straße. Auch in München mobilisierte die Demonstration „Ge- meinsam gegen Rechts, für Demokratie und Vielfalt“ eine außergewöhnlich große Teilnehmendenschaft. Wir erklären die Mobilisierung der vielen Demonstrant:innen anhand der Diskurs- und Populismustheorie von Ernesto Laclau und Chantal Mouf- fe, deren Übertragung in die Protestforschung bislang kaum systematisch genutzt wurde. Auf Basis einer qualitativen Inhaltsanalyse von Zeitungsartikeln, Pressemit- teilungen und Social-Media-Posts identifizieren wir jene leeren Signifikanten, mit denen im Vorfeld mobilisiert wurde. Anschließend analysieren wir die Konkretisie- rung dieser Begriffe in den Rede- und Musikbeiträgen sowie in 65 exklusiv vor- liegenden Feedback-E-Mails von Teilnehmenden. Unsere Ergebnisse zeigen, dass leere Signifikanten wie ,gegen Rechts‘, ,für Demokratie‘ und ,für Vielfalt‘ breite Mobilisierung ermöglichten, deren Konkretisierung auf der Demonstration jedoch agonistische Konflikte und Momente der Dislokation sichtbar machte. Obwohl die Demonstration linkspopulistische Elemente aufweist (Konstruktion einer In- und Outgroup, Verwendung eines demokratischen Symbols und emotionaler Rhetorik), wirkte sie nicht im Sinne Mouffes transformativ, sondern stabilisierend. Damit zei- gen wir, wie Linkspopulismus in zivilgesellschaftlicher Mobilisierung im Sinne einer Stabilisierung des liberal-demokratischen Systems wirksam werden kann.
Title Page: Veiled Power: How Rosenwald Schools Quietly Shaped the Civil Rights Movement Omar Wasow and Jacob M. Grumbach April 8, 2026 Abstract: What precipitates the collapse of seemingly durable social orders like Jim Crow? During the 1920s, 5,000 schools were built across the rural South through a partnership between philanthropist Julius Rosenwald and Black communities who raised matching funds and donated land. Local elites saw vocational training that would preserve the racial order, but counties with more Rosenwald Schools experienced greater civil rights protest activity in the 1960s. We argue Black educators used accommodationist cover to build veiled capacity: organizational infrastructure for collective action behind a veil of compliance. Mediation analysis reveals that pre-existing Black social capital predicted protest through Rosenwald teacher placements, not overall Black enrollment. Instrumental variable models suggest the effect is not driven by community selection. Moving from no Rosenwald teachers to the 75th percentile predicts 45% more protest participation. Even under conditions of domination, civic capacity can accumulate where elites cannot see and, when activated, transform the social order.
Largely as a result of this growing conceptual ambiguity, neoliberalism is now widely acknowledged in the literature as a controversial, incoherent, and crisis-ridden term, even by many of its most influential deployers. Critics describe it as ‘an oft-invoked but ill-defined concept’ (Mudge 2008:703), that is ‘omnipresent’ and ‘promiscuous’ (Clarke 2008:135) who’s meaning ‘seems to alter its shape from paper to paper’ (Castree 2006:1) such that ‘What it stands for and what it explains is both confused and confusing’ (Turner 2008:2). It seems to suffer from ‘a perplexing mix of overreach and underspecification’ (Brenner et al 2009:2), and is used as a ‘constant master category that can be used both to understand and to explain all manner of political programs across a wide variety of settings’ (Rose et al, 2006:97). In a content analysis of journal articles in Comparative Politics, Human Geography, and Development Studies, neoliberalism was routinely found to be left undefined even
A black-and-white photograph of the Pantego Rosenwald School in Beaufort County, North Carolina, around 1927. The image shows a large brick school building with a central entrance, symmetrical windows, and dormer windows on the roof. In front of the building, a very large group of Black students and several adults are arranged in rows, stretching across the width of the image. The students wear early 20th-century clothing, including dresses, collared shirts, and jackets. Some adults, likely teachers or staff, stand among them. The group is posed outdoors on a wide, open dirt area, facing the camera.
Three side-by-side line graphs labeled A, B, and C compare the relationship between the number of Rosenwald teachers and protest participation (log scale) across different instrumental variable specifications. Each panel shows an upward-sloping line with shaded confidence intervals. Panel A (“Nashville Only”): X-axis is “Rosenwald Teachers,” Y-axis is “Protest Participation (log).” The line rises steeply, indicating a strong positive association when using distance to Nashville as the instrument. Panel B (“Railroad Only”): Same axes. The line also slopes upward, though less steeply than Panel A, indicating a positive association using distance to the Illinois Central Railroad as the instrument. Panel C (“Combined (Both)”): Same axes. The line slopes upward at a moderate rate, similar in magnitude to the other panels, showing a positive association when both instruments are included. Across all three panels, the consistent upward trends indicate that more Rosenwald teachers are associated with higher protest participation, with overlapping confidence intervals suggesting similar effect sizes across specifications.
Three side-by-side line graphs labeled A, B, and C show relationships between educational variables and protest participation (log scale). Each panel includes a fitted line with shaded confidence intervals. Panel A (left): X-axis is “Black Enrollment (log)” and Y-axis is “Protest Participation (log).” The line slopes slightly downward, indicating a small negative association between Black enrollment and protest participation, with wide uncertainty. Panel B (middle): X-axis is “Black Funding (log).” The line also slopes slightly downward, showing a small negative relationship between Black funding and protest participation, with relatively narrow uncertainty. Panel C (right): X-axis is “# Rosenwald Teachers.” The line slopes strongly upward, indicating a clear positive association between the number of Rosenwald teachers and protest participation, with widening confidence intervals at higher values. Caption notes that the figure shows marginal effects from a regression model; Panel C displays predictions for 0–100 teachers, with an outlier county (Shelby County, TN, 213 teachers) omitted for readability.
Book cover featuring a small wooden schoolhouse centered at the bottom, framed by dark tree branches overhead against a muted sky. Large white text reads “The Lost Education of Horace Tate,” with smaller text above: “Uncovering the Hidden Heroes Who Fought for Justice in Schools.” The author’s name, Vanessa Siddle Walker, appears at the bottom.
A black-and-white photograph of students and teacher gathered in front of Pee Dee Rosenwald Colored School in South Carolina, a one-story wooden school building with large multi-pane windows and a chimney, raised slightly on a brick foundation. In front of the building, a large group of Black children and a few adults stand in rows facing the camera. The children wear simple clothing typical of the early 20th century—dresses, overalls, and shirts—and appear to be students posed outside the school. The setting is rural, with trees visible in the background and a dirt ground in the foreground. Between 1912 and 1932, approximately 5,000 such schools were built across the rural South through a partnership between Black communities, the Julius Rosenwald Fund, and local public authorities. Photo: S.C. Department of Archives and History
A scanned, typewritten document excerpt labeled “5.” summarizing the impact of the Julius Rosenwald Fund. The text states that the Fund has been an important factor in the advancement of Black Americans, bringing “order, peace, docility” out of prior disorder, and fostering hope, gladness, and contentment. It also claims the Fund encouraged active interest in education and contributed to the improvement and advancement of the state.
A sepia-toned photograph of two men, identified as Julius Rosenwald and Booker T. Washington, walking outdoors side by side. Both wear dark suits and hats typical of the early 20th century. One man gestures slightly with his hand while looking downward, as if mid-conversation. In the background, an early automobile and bare trees suggest a rural or small-town setting.
Veiled Power: How Rosenwald Teachers Quietly Shaped the Civil Rights Movement Omar Wasow∗ Jacob M. Grumbach∗ April 1, 2026 Abstract What precipitates the collapse of seemingly durable social orders like Jim Crow? During the 1920s, approximately 5,000 “Rosenwald Schools” were built across the rural South through a partnership between philanthropist Julius Rosenwald and Black communities who raised matching funds, donated land, and petitioned local governments. Local elites saw vocational training that would preserve the racial order. We argue Black educators used this accommodationist cover to build veiled capacity: organizational infrastructure for collective action behind a veil of compliance. Counties with more Rosenwald Schools show greater civil rights protest in the 1960s. Mediation analysis reveals that pre-existing social capital predicted protest through Rosenwald teacher placements, not enrollment. Instrumental variable models suggest the effect is not driven by community selection. Moving from no Rosenwald teachers to the 75th percentile predicts 45% more protest. The political effects of education may depend less on what elites intend than on what educators build where elites cannot see.
Figure 1 presents two plots. Panel (a) displays the percentage of donors in each wealth rank that contributed to each of the six campaigns (i.e., Democratic and Republican nominees in 2012, 2016, and 2020).Footnote 17 Panel (b) displays the per capita dollars from each wealth bin, by campaign (including those who donate nothing). As Figure 1 shows, the association between wealth and contributions is approximately exponential. The wealthiest are much more likely to contribute, and the wealth gradient is even steeper in dollars because the top 0.1% contribute very large sums. This is one of the most robust findings in the campaign finance literature, but the figure demonstrates it with considerably more precision. By measuring wealth independently of contribution size, we avoid misattributing smaller contributions to non-wealthy donors and underestimating wealthy dollars. Most relevant to our analysis, Figure 1 compares the wealth gradients for Trump versus other candidates. In 2016, compared to other candidates, Trump’s wealth gradient is far flatter, because Trump elicited far fewer wealthy contributors and per capita dollars. For example, among the top 0.1%, Trump’s donors and per capita dollars represent about one third of Romney’s. While Trump did worse than all other candidates among nearly all wealth groups, that deficit was larger among the wealthy. In short, in 2016, wealthy donations to Trump are low compared to other presidential candidates. In 2020, Trump’s performance among the wealthy improved considerably over 2016 (Figure 1). Consider donation rates (Panel a). Among the wealthiest 0.1%, for example, Trump roughly doubled his rate, though he still significantly lagged Biden and Romney. He did even better in per capita dollars (Panel b) than in rates. Open access link to paper: http://cup.org/4cfm0Az
A line chart titled “Donald Trump Approval.” The chart shows U.S. presidential approval ratings over time, weighted by poll quality and recency, with shaded bands indicating 95% confidence intervals. The x-axis runs from early 2025 to March 7, 2026, and the y-axis ranges from about 30% to 65%. A green line representing approval trends downward over the period, while a pink line representing disapproval trends upward. By March 7, 2026, the chart highlights Disapprove 57.8% and Approve 38.7%, a 19-point net disapproval.
Alt text: A cropped black-and-white newspaper excerpt with the subheading “‘Disgusted’ by Leftists.” The text states that in the Clipper Yearbook for 1955, Daniel Burros listed the United States Military Academy at West Point as his first college preference. It continues with a quotation recalling that as a child he was interested in “the right wing” and remembered being “disgusted” by the “left-wing kids” around him.
Screen grab from a news program with Chyron that says Gabriel Diaz, Suspended Cabbie, Channel 2. Image shows a brown-skinned man in a t-shirt outside talking to a reporter holding a mic.
**Alt text:** A black-and-white newspaper clipping from *The New York Times* with the headline **“State Klán Leader Hides Secret of Jewish Origin.”** The article, by McCandlish Phillips, reports that **Dan Burros**, a 28-year-old Queens resident and New York State leader in the Ku Klux Klan and the American Nazi Party, had concealed that he was born Jewish. The text describes his role in white supremacist organizations, notes that his parents were Jewish immigrants, and explains that he tried to keep this background secret while promoting antisemitic and racist views. The article recounts attempts by reporters to contact him and describes his hostile reaction when confronted with the information. A note at the bottom reads “The New York Times, Published: October 31, 1965.”
Black-and-white newspaper article titled “Educator Feels U.S. … Moving Toward Police State” by Ted Lacey, page 6 of Chicago Defender, May 11, 1970. The article reports comments by Black educator Dr. Norman Francis, president of Xavier University of New Orleans, who warned that the United States was moving toward a “police state” because of force used against student protests, including at Kent State. Francis criticized harsh government responses to demonstrators and urged more humane treatment while supporting nonviolent protest. The article also discusses fundraising for the United Negro College Fund, growth in Black college enrollment, and differences in protest activity across regions and campuses.
Line chart titled “Texas Democratic Senate Primary” showing polling averages through Feb. 27, 2026. Two main candidates are tracked: Crockett (teal line) and Talarico (orange line), with shaded bands indicating 95% confidence intervals. Crockett begins the period with a clear lead around 50%, while Talarico starts near 42–43%. Both candidates decline into the low 40s during late 2025, with Crockett maintaining a small advantage. In February 2026, support for both candidates increases slightly. The latest average (Feb. 27, 2026) shows Crockett leading with 46.9% to Talarico’s 42.9%, a margin of about 4 points. Confidence intervals overlap, suggesting a competitive race. Additional context (mid-February poll): A University of Texas at Tyler poll conducted Feb. 13–22 shows a much larger Crockett lead: Likely voters: Crockett 55%, Talarico 37%, Hassan 4% (Crockett +18) Registered voters: Crockett 56%, Talarico 34%, Hassan 7% (Crockett +22) Interpretation: The polling average suggests a close race (roughly Crockett +3 to +5). The UT–Tyler poll is an outlier with a very large Crockett lead. Because averages weight multiple polls and adjust for house effects, the overall picture remains competitive rather than a Crockett landslide.
Line chart titled “Texas Democratic Senate Primary” showing polling averages over time through Feb. 27, 2026. Two candidates are tracked: Talarico (orange line) and Crockett (teal line), with shaded bands indicating 95% confidence intervals. Crockett began with a lead near 50% while Talarico was around 42–43%. Both candidates declined into the low 40s through late 2025, with Crockett maintaining a slight advantage for much of the period. In late February 2026, Talarico’s support rises sharply while Crockett’s dips slightly. The latest estimate (Feb. 27, 2026) shows Talarico leading with 46.6% to Crockett’s 43.1%, a margin of about 4 points.
Alt text: Line chart titled “Chart 1: The share of U.S. consumers who use cryptocurrency for payments has declined slightly,” showing percentages from 2021 to 2024. Four lines represent different uses: overall payments, purchases only, money transfers only, and both purchases and transfers. Overall payment use declines from 2.6% in 2021 to 1.9% in 2024, with a low of 1.7% in 2023. Purchases-only use fluctuates around 1.2% in 2021, 1.0% in 2022, 0.8% in 2023, and 1.1% in 2024. Transfers-only use rises from 0.8% in 2021 to 1.0% in 2022, then drops to 0.3% in both 2023 and 2024. Use for both purchases and transfers stays below 1%, moving from 0.6% in 2021 to 0.7% in 2022, 0.6% in 2023, and 0.5% in 2024. Sources: Federal Reserve Board of Governors and authors’ calculations.
White Backlash Resulting In Upsurge Of Power For American Nazi Party The Chicago Defender (National edition); Aug 27, 1966; pg. 8 By Negro Press International Nazis come into power in America? Ridiculous, it has been pointed out by civic and government officials. The number of Nazis in the whole country is said to amount to only a handful. Most of them are lower-class white derelicts. And Nazism could hardly become powerful in a country that went to war some 25 years ago to wipe it out, it has been noted. But there were disquieting signs that Nazism could turn out to be a force to be reckoned with in this country, thanks to a "white backlash" resulting from recent civil rights marches. In the Gage Park area, Nazis led a rally which 2,000 residents of the lily-white neighborhood attended. The rally was said to be the largest Nazi-led meeting in the city since 1941, when the German-American Bund was spewing out its hate. The Nazi organization, meanwhile, was planning marches through Negro areas of the city—likely the Southside—in protest against civil rights marches into white areas. The Nazis would not have stood a chance of surviving such a march until recently, when the city's police went all out to protect civil rights marches. Now, Nazi marches in Negro neighborhoods were sure to receive police protection, as well. At the same time, Albert A. Raby, convenor of the Coordinating Council of Community Organizations, expressed fear that all-white areas of the city where marchers have demonstrated "could become breeding grounds for the fascists." Evidence that the Nazis were a force to be reckoned with was found in Police Supt. O. W. Wilson's denial of a Nazi charge that police discriminated against whites in protecting civil rights marchers. Wilson dignified the Nazis' charge by answering it and affirming that the Chicago Police department does not discriminate. At the same time, the Nazis got themselves an articulate spokesman, Ericka Himmler, who is 6 …
When the name change to MS NOW was announced in October, the network’s internal research showed 31% of viewers found the idea somewhat unappealing or very unappealing, a warning light for what might be ahead. Two months later, that figure dropped to 17%, while the percentage of viewers who found it very appealing or somewhat appealing jumped from 30% to 44%.
mount. Figure 1 presents two plots. Panel (a) displays the percentage of donors in each wealth rank that contributed to each of the six campaigns (i.e., Democratic and Republican nominees in 2012, 2016, and 2020).Footnote 17 Panel (b) displays the per capita dollars from each wealth bin, by campaign (including those who donate nothing). As Figure 1 shows, the association between wealth and contributions is approximately exponential. The wealthiest are much more likely to contribute, and the wealth gradient is even steeper in dollars because the top 0.1% contribute very large sums. This is one of the most robust findings in the campaign finance literature, but the figure demonstrates it with considerably more precision. By measuring wealth independently of contribution size, we avoid misattributing smaller contributions to non-wealthy donors and underestimating wealthy dollars. Most relevant to our analysis, Figure 1 compares the wealth gradients for Trump versus other candidates. In 2016, compared to other candidates, Trump’s wealth gradient is far flatter, because Trump elicited far fewer wealthy contributors and per capita dollars. For example, among the top 0.1%, Trump’s donors and per capita dollars represent about one third of Romney’s. While Trump did worse than all other candidates among nearly all wealth groups, that deficit was larger among the wealthy. In short, in 2016, wealthy donations to Trump are low compared to other presidential candidates. In 2020, Trump’s performance among the wealthy improved considerably over 2016 (Figure 1). Consider donation rates (Panel a). Among the wealthiest 0.1%, for example, Trump roughly doubled his rate, though he still significantly lagged Biden and Romney. He did even better in per capita dollars (Panel b) than in rates. Open access link to paper: http://cup.org/4cfm0Az
One of the more compelling data visualizations produced in recent years is the following representation of measles incidence from 1928 to 2003 by state produced by the Wall Street Journal graphics team: Animated heatmap chart titled “Measles” showing reported measles cases by U.S. state over time from roughly 1930 to 2010. States are listed in rows on the left (e.g., Alaska, Arkansas, California, Connecticut, etc.), and years run along the horizontal axis. Each small square represents a state-year, colored by number of cases using a scale from light blue (near 0) through green and yellow to orange and red (highest, up to about 4,000 cases). A vertical black line around 1963 is labeled “Vaccine introduced.” Before this line, many states show frequent yellow/orange/red squares, indicating large outbreaks. After the line, the grid shifts mostly to very light blue with only occasional darker squares, indicating a sharp and sustained drop in measles cases following vaccine introduction.
One of the more compelling data visualizations produced in recent years is the following representation of measles incidence from 1928 to 2003 by state produced by the Wall Street Journal graphics team: Heatmap chart titled “Measles” showing reported measles cases by U.S. state over time from roughly 1930 to 2010. States are listed in rows on the left (e.g., Alaska, Arkansas, California, Connecticut, etc.), and years run along the horizontal axis. Each small square represents a state-year, colored by number of cases using a scale from light blue (near 0) through green and yellow to orange and red (highest, up to about 4,000 cases). A vertical black line around 1963 is labeled “Vaccine introduced.” Before this line, many states show frequent yellow/orange/red squares, indicating large outbreaks. After the line, the grid shifts mostly to very light blue with only occasional darker squares, indicating a sharp and sustained drop in measles cases following vaccine introduction. In the two years after its release, many prominent data journalists, designers, and developers have analyzed/applauded the design decisions in the chart, which urge the reader to its take-home message: the introduction of the measles vaccine drastically reduced the incidence of the virus.
Title page of an academic article titled “Plutopopulism: Wealth and Trump’s Financial Base.” Authors listed are Sean Kates (University of Pennsylvania), Eric Manning (Princeton University), Tali Mendelberg (Princeton University), and Omar Wasow (University of California, Berkeley). The abstract below the title states: Comparative scholarship suggests authoritarian candidates often rely on backing from the wealthy. The wealthy are also said to play an important role in American campaign finance. Studies of Donald Trump, however, found that he drew significant support from white Americans with less education and privilege. We evaluate wealthy and non-wealthy Americans’ financial support for Trump, compared to other candidates, by constructing a comprehensive dataset of property values matched to contributions and voter files. We find Trump underperformed among wealthy Republican donors while mobilizing new non-wealthy donors. Trump also diversified the donorate, especially by education. That is, Trump built an unusual coalition of wealthy and non-wealthy donors. Our results support an alternative, “plutopopulist” model of Trump’s financial base. This study demonstrates the importance of studying both non-wealthy and wealthy Americans, the group who give the most but whose individual behavior has been studied the least. Open access link to paper: http://cup.org/4cfm0Az
Figure 4. Partisan Difference in Matched Dollars, by Cycle and Wealth. Note: Bars above $0 indicate Republican advantage (in red). Figure 4 shows the same dollars as Figure 2, but as a difference between partisan opponents. In 2012, Obama substantially out-raised Romney among the bottom 90%, while Romney out-raised Obama among the top 1%. In 2016, Clinton out-raised Trump among the bottom 90%, but also out-raised Trump in every wealthy bin. That is, Trump’s partisan balance of dollars was much worse than Romney’s in every wealth group. Finally, in 2020, Trump had a remarkable partisan advantage among the bottom 90%, nearly flipping the Democrats’ typical advantage. And while he largely attenuated his 2016 disadvantage among the wealthy, he fails to reach Romney’s wealthy partisan advantage. Even the top 0.1% have a slight Democratic tilt. These results again point to Trump’s weakness among the wealthy and unusual non-wealthy support.
Figure 2 in paper presents the proportion of a campaign’s itemized total from each wealth group in stacked bar charts. Each bar represents a candidate-year, with segments showing the percentage of total dollars that come from each of four wealth bins. In both 2016 and 2020, Trump was much less reliant on the wealthy than Romney. Compared to Romney, Trump’s share of dollars from the bottom 90% is 9 percentage points higher in 2016, and 20 percentage points higher in 2020. In dollar terms, Trump got 2.7 times more dollars than Romney from the bottom 90% (in 2020). Likewise, Trump’s share from the top 1% (the sum of the top two bins) is 9 and 15 percentage points lower than Romney’s. To be sure, most of Trump’s dollars still came from the wealthy. Even in 2020, the wealthiest 10% gave him 55% of his dollars. However, the bottom 90% nearly matched them. To put this in sharp relief, the candidate with a wealth distribution of dollars closest to Trump’s in 2020 is Obama. Open access link to paper: http://cup.org/4cfm0Az