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Register for Frontiers of Democracy

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June 24, 2022, 9 am-4:30 pm, live in Boston or online

In 2022, the annual Frontiers of Democracy conference at Tufts University’s Tisch College of Civic Life has a special format. The main activity will be to deliberate in small groups—at tables or on Zoom—about the issues raised in selected “civic cases.”  

Individuals may choose to attend either in-person or remotely. The entire conference will take place between 9 am and 4:30 pm on June 24. The in-person version will be held in Tufts’ downtown Boston campus.

If you have not done so already, please purchase a ticket for the event now, choosing an in-person or remote ticket.

If registration for the face-to-face version looks unexpectedly low, or if the pandemic situation worsens, it may be necessary to cancel the in-person version. In that case, in-person tickets will be refunded in full. The status of the face-to-face meeting will be reviewed on May 13.

In-person attendees will be required to show proof of COVID-19 vaccination and to follow other Tufts procedures in force in June, as described here.

“Civic cases”

Civic cases describe difficult choices faced by real groups of activists, social-movement participants, or colleagues in nonprofit organizations. By discussing what we would do in similar situations, we can develop civic skills, explore general issues, and form or strengthen relationships with other activists and thinkers.

Most of the cases for Frontiers 2022 have been developed by the SNF Agora Institute at Johns Hopkins University, Justice in Schools, or the Pluralism Project at Harvard, which are co-sponsors of Frontiers this summer. Selected cases can be found here, and more options will be available by June. Unlike most cases about business, public policy, or ethics, these stories involve groups of voluntary participants who must make decisions together. This website (based on Peter Levine’s new book,What Should We Do?) provides an optional framework for such discussions. You will be able to indicate your preference for which cases to discuss. Each group will discuss a case either online or face-to-face (not in a hybrid format). There will be time for two case discussions on June 24, plus plenary sessions meant for both remote and in-person attendees together.

About Frontiers

Frontiers of Democracy has been held annually since 2009, with a hiatus due to the COVID-19 pandemic. It traditionally attracted about 140 activists and scholars or advanced students from many countries for relatively informal discussions of civic topics. The 2022 version is intentionally shorter and hybrid in format.

Scholars at Risk opportunity at Tufts

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I am very happy to serve on this committee and would be open to questions about it:


The Scholars at Risk (SAR) Program at Tufts is dedicated to helping scholars, artists, writers, and public intellectuals from around the world escape persecution and continue their work by providing ten-month-long academic fellowships at Tufts University. Tufts has been a member of the international Scholars at Risk (SAR) network, which is chaired by Tufts Trustee Lisa Anderson, since 2011. Tufts has hosted several scholars in the past in both Medford and Boston. These scholars have made positive contributions to our academic life and offered important perspectives to our students and faculty.

Details are here. There may also be opportunities to conduct funded research or to teach from Ukraine (or from other countries in crisis) without coming to Tufts, but that is still being considered.

what explains state variation in COVID-19 mortality?

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Why have some states seen many more deaths from COVID-19 than others? Do differences in state policies matter? Is it mostly about demographics? Or what about factors like climate and population density, which could influence whether and when people congregate indoors?

To explore these questions, I made a spreadsheet with 58 salient variables about the 50 states, drawing most of the data from the Senate Joint Economic Committee or the Kaiser Family Foundation. I then went fishing for variables that could predict cumulative death rates from COVID-19. I use this “fishing” metaphor with irony, because there is a danger of obtaining spurious results when you explore too many variables at once. Still, the following results might suggest tighter research questions.

Below, I describe nine regression (OLS) models, each with a different thematic focus, arranged in order by how much variance in the states’ COVID-19 mortality they seem to explain. (I report adjusted r-square statistics, which should allow the models to be compared despite differences in the number of variables.)

In summary: the states’ policies that I measured and the partisanship of governors did not matter, but the proportion of people who voted for Trump did. That relationship was not explained by demographics, which I controlled for.

Variables that mattered in many of my models included the percentage of the population that was already in poor health, the GOP vote share in 2020, Black/White residential segregation, and the GINI coefficient (a measure of inequality). A model with just those four components could explain 71% of the variance in COVID deaths (unadjusted r-square = .715).

  1. A politics and policy model. Variables: party of state governor, percent of the 2020 state’s popular vote for Republicans, whether the state required masks indoors for some people in Feb 2022, whether the state required, allowed, or banned local vaccine requirements, and state/local spending per capita. The only statistically significant correlate of the mortality rate: the GOP vote share in 2020. Adjusted r-square = .203, meaning that this model offers little insight.
  2. A geography model. Variables: population density, percentage rural, average commuting time, mean daily temperature. Statistically significant correlates: none. Adjusted r-square = .240 (again, a poor fit).
  3. Sociability model: Variables: average number of close friends, percent of neighbors who regularly do favors, number of nonprofits per 1,000 people, percentage who worked with neighbors to fix/improve something. Statistically significant correlate: working with neighbors (related to lower mortality). Adjusted r-square = .415.
  4. A comorbidities model: Variables (all measured pre-pandemic): percent in poor health, premature mortality rate, mortality from suicide/drug overdose, percent disabled, percent with diabetes, obese, and smokers. Statistically significant correlates: general poor health and disabilities. Adjusted r-square = .451.
  5. A political participation model: Variables: percent who participated in a demonstration, attended a public meeting, served on a committee, and voted in 2012 and 2016. Statistically significant correlate: attending a public meeting (related to lower mortality). Adjusted r-square = .483.
  6. An economics model. Variables: unemployment, incarceration, poverty, GINI coefficient, college graduation rate, internet access at home. Statistically significant correlates: worse inequality, higher incarceration, fewer people with BAs. Adjusted r-square = .623.
  7. An inequality model: Variables: Black/White residential segregation, GINI coefficient, college graduation rate, incarceration rate. Statistically significant correlates: racial segregation, GINI coefficient. Adjusted r-square: .646.
  8. A politics and demographics model. Variables: the party of state governor, percent of the 2020 state vote for Trump, and the racial demographics and median age of the state. Statistically significant correlates: higher GOP vote, more African Americans, more Latinos, a higher median age. Adjusted r-square = .647.
  9. A model that explains most of the variance. Variables: percent in poor health before the pandemic, GOP vote share, Black/White segregation, GINI coefficient, percent over age 65, incarceration rate, college graduation rate. Statistically significant correlates: the first three. Adjusted r-square = .699. (Unadjusted r-square = .735.)

My dataset also included some variables that I have not mentioned here, including several measures of trust (for other people and for institutions) and other types of civic and political participation. None seemed to be influential in any of the models I tried.

seeking a religious congregation for a research study

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I am seeking a congregation (of any religion, denomination, tradition, size, and location) for a research study. My interest is in testing a new method that I have been developing with colleagues that could apply to any community. I would give the congregation’s leadership–or its full membership–easy-to-understand findings about shared values and areas of disagreement within their congregation that should have practical value for planning events and programs.

Please consider whether this project might interest a congregation to which you belong or one that you know. Inquiries are welcome. More details follow:

I would ask the clergy or other leader(s) of the congregation to encourage members to take anonymous online surveys. The minimum would be two: a short survey with open-ended responses followed by a multiple-choice survey a week or two later that is based on the first one. I would be interested in repeating the multiple-choice survey months later to understand change, although that’s optional. If it’s practical, I would also like to visit and observe informally to get a feel for the community.

I would publish a scholarly study that would refer to the congregation anonymously (e.g., “a Protestant church in the Northeastern USA”). I would also provide the congregation with concise findings in PowerPoint format and would be happy to discuss them. No money would change hands. The congregation would own the PowerPoint and would not be obliged to publish or share it in any way. No individuals would be obligated to take the surveys, and I would expect only some people to do so. No identifiable information about individuals would be shared either within or beyond the congregation.

I could provide more detail about the method, but in brief, we don’t simply ask people their opinions about values, beliefs, and norms. Instead, we ask them how their personal opinions relate to each other. For instance, do they value A because they value B? Do they think that A causes B? From those responses, we generate network diagrams of the beliefs of each respondent and of the community as a whole. In this study, the questions would focus on religion and the congregation as a community, not on politics (unless respondents happen to bring up political matters).

Typically, each person’s responses are unique—a nice illustration of the uniqueness of human beings and how much we lose when we assign people to categories. Yet we typically see clusters of agreement and disagreement that can otherwise be overlooked. Understanding these patterns should provide ideas for visitors, readings, events, discussion groups (etc.) that would be valuable for the specific congregation.