The Korea Program at Stanford’s Walter H. Shorenstein Asia-Pacific Research Center is soliciting papers for the Koret Workshop entitled “Korea’s Migrants: From Homogeneity to Diversity” held at Stanford University on April 21, 2017.

This ninth annual conference seeks to examine major issues related to recent migrants in Korea. Korea has long promoted a sense of ethnic unity but in recent years has seen an influx of ethnic and non-ethnic Koreans, making the country more diverse. The government has promoted multiculturalism to deal with such diversity; however, migrants, either permanent or temporary, continue to face discrimination. New approaches are needed to create better social cohesion.

We are looking for empirical papers that address the following questions for one of the following groups of migrants: North Korean refugees, Chosonjok, foreign brides, migrant labor (skilled or unskilled), and Korean returnees.

• What are the real and perceived contributions of this group to Korean society?
• What are the remaining challenges and concerns associated with this group as a migrant living in Korea? Have these challenges worsened or improved over time?
• How have Korean perceptions of your migrant group changed over time, if at all? Why or why not?
• Do you think this particular group has been more or less discriminated against compared to other migrant groups? Is there any change over time?
• Based on your study, do you think that boundaries between various migrant groups are blurring, remaining distinct, or becoming more salient?
• Based on your study, what is the most important barrier to social integration in Korean society? Is it ethnicity, citizenship, class, or something else?
• Has multiculturalism as a policy and social discourse adequately addressed the concerns of your migrant group?  If not, then do we need a new framework? What would you suggest?
• Do you think the influx of your migrant group can lessen the looming demographic crisis that will reduce the working-age population?
• In recent years, there has been growing anti-immigrant sentiment in the United States and Europe. Do you think this will be an issue in Korea as well?
• Based on your study of your migrant group, do you think Korea is ready for large-scale mass migration? Why or why not? If not, what would be an alternative to migration?

Submission for the conference
Upload papers in PDF (6,000-8,000 words) here. Inquiries can be made to Dr. Yong Suk Lee at yongslee@stanford.edu. The submission deadline is Dec. 31, 2016.

The authors of the accepted papers will be invited to and asked to present their studies at the conference. After the workshop, selected papers will be published as a special issue at a top Asian studies journal and/or as an edited volume. Travel (domestic or international economy class) and accommodation costs for the presenters will be reimbursed.

About the Koret Workshop
The Koret Workshop is organized by the Korea Program to bring together an international panel of experts in Korean affairs. The Korea Program established the Koret Fellowship in 2008 with generous funding from the Koret Foundation.

The Koret Foundation of San Francisco has extended its gift to Stanford’s Shorenstein Asia-Pacific Research Center (APARC) in support of contemporary Korean studies.

Two more years have been added to a three-year gift awarded to the Center in 2015, totaling to 12 years of lifetime support from the Foundation, whose mission is to endow scholarly solutions to community problems and to invest in leading institutions that serve as levers for achieving impact.

The gifts have allowed the Center to bring eminent professionals from Asia and the United States to Stanford for an annual fellowship and an annual international conference known as the Koret Workshop, all of which aims to promote greater understanding and closer ties between Korea and the United States.

“The Koret Foundation’s gift represents its commitment to strengthening research and finding solutions to challenges in Korea and the United States,” said Shorenstein APARC Director Gi-Wook Shin. “Their support over the past eight years has enabled our Korea Program to invite numerous visiting scholars, offer new courses to students, and foster important conversations in the Bay Area community and beyond. We greatly value our relationship with the Foundation and thank them for their enduring generosity.”

Since 2008, eight Koret fellows have conducted research at the Center, many public seminars have been held, and each workshop has yielded a book published by Shorenstein APARC and the Brookings Institution Press.

Clifton Parker of the Stanford Report writes about REAP's core researcher, Prashant Loyalka's latest findings on China's higher education system. Read the original article from Stanford News.

China can improve its higher education system by introducing incentives for students and teachers so they take learning more seriously, a Stanford professor says. Under the current system, college students are essentially guaranteed a diploma, offering little motivation to excel.

Prashant Loyalka, a center research fellow at the Rural Education Action Program in the Freeman Spogli Institute for International Studies, led a forthcoming study that found that Chinese college freshmen in computer science and engineering programs began with academic and critical thinking skills about two to three years ahead of their peers in the United States and Russia, but showed almost no improvement in such skills after two years of college. Critical thinking skills are typically defined as the ability to make clear and well-reasoned analyses and evaluations of information.

What are the strengths of the Chinese educational system?

China’s education system is strong in many ways. By the end of junior high school and high school, students in urban areas display high levels of academic skills like math, science, language arts and English. They also show very high levels of higher-order thinking skills, such as critical thinking and quantitative reasoning. Students from both urban and rural areas tend to be incredibly hardworking and disciplined as well. This is partly due to the fact that Chinese students and their parents spend a lot of time and resources on their studies, both inside and outside of school.

Policymakers in China have also done a great job universalizing access to primary school and getting kids to at least start junior high school.

The weaknesses?

Perhaps the most glaring weakness is that the quality of education is low for the millions of kids who live in rural China. The majority of students in rural China are unlikely to complete high school. About one-third drop out of junior high school. Furthermore, the vast majority of students also do not appear to learn very much after primary school. China shuttles millions of rural kids into vocational high schools that, as several of our studies show, fail to build students’ cognitive and non-cognitive abilities.

Another big weakness is that students, on average, do not improve their academic or high-order thinking skills during college. One of the reasons for this could be that students are essentially guaranteed to graduate college on time and correspondingly have few incentives to work hard during college.

What are the main highlights of your forthcoming study?

In 2014, I led a pilot study to measure the skill levels and gains of engineering and computer science students in China, Russia and the United States. Entering university freshmen in China were roughly three years ahead of U.S. students in critical-thinking skills and roughly two years ahead of Russian students in critical thinking, math and physics skills.

After two years of study, students in the U.S. and Russia closed about half the skill gap with students in China.

The reduction in the skills gap between countries was due to the fact that while U.S. and Russian students made positive skill gains, Chinese students made no skill gains over two years.

I am now in the midst of conducting a much larger study, using nationally random, representative samples of engineering and computer science students in China, Russia and other developed and developing countries. The goals of the study are not just to measure how much students learn in university, but even more to explain what types of factors can contribute to increases in learning.

Why are China’s high schools doing better than their colleges?

Students in China have to study extremely hard if they want to go to college. There are a limited number of spots in academic high schools and colleges, and students have to pass highly competitive exams to get into each level of schooling. In order to prepare students for college, academic high schools offer a rigorous curriculum in math, science, Chinese and English. Academic high school administrators and teachers maintain a highly disciplined environment in which students study long hours in school and do lots of homework and tutoring outside of school. Academic high school teachers are also highly incentivized to make sure students do well.

The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world. Applying machine learning to satellite images could identify impoverished regions in Africa.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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“We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty,” said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. “At the same time, we collect all sorts of other data in these areas – like satellite imagery – constantly.”

The researchers sought to understand whether high-resolution satellite imagery – an unconventional but readily available data source – could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.

“There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor,” said study lead author Neal Jean, a doctoral student in computer science at Stanford’s School of Engineering. “This makes it hard to extract useful information from the huge amount of daytime satellite imagery that’s available.”

Because areas that are brighter at night are usually more developed, the solution involved combining high-resolution daytime imagery with images of the Earth at night. The researchers used the “nightlight” data to identify features in the higher-resolution daytime imagery that are correlated with economic development.

“Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans – things like roads, urban areas and farmland,” said Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

“Our paper demonstrates the power of machine learning in this context,” said study co-author Stefano Ermon, assistant professor of computer science and a fellow by courtesy at the Stanford Woods Institute of the Environment. “And since it’s cheap and scalable – requiring only satellite images – it could be used to map poverty around the world in a very low-cost way.” 

Co-authors of the study, titled “Combining satellite imagery and machine learning to predict poverty,” include Michael Xie from Stanford's Department of Computer Science and David Lobell and W. Matthew Davis from Stanford's School of Earth, Energy and Environmental Sciences and the Center on Food Security and the Environment. For more information, visit the research group's website at: http://sustain.stanford.edu/

CONTACTS: 

Neal Jean, School of Engineering: nealjean@stanford.edu, (937) 286-6857

Marshall Burke, School of Earth, Energy and Environmental Sciences: mburke@stanford.edu, (650) 721-2203

Michelle Horton, Center on Food Security and the Environment: mjhorton@stanford.edu, (650) 498-4129