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Characters’ actions in movie scripts reflect gender stereotypes

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Machine-learning framework finds female characters display less agency and more emotion than male counterparts

Newswise — Researchers have developed a novel machine-learning framework that uses scene descriptions in movie scripts to automatically recognize different characters’ actions. Applying the framework to hundreds of movie scripts showed that these actions tend to reflect widespread gender stereotypes, some of which are found to be consistent across time. Victor Martinez and colleagues at the University of Southern California, U.S., present these findings in the open-access journal PLOS ONE on December 21.

Movies, tv shows, and other media consistently portray traditional gender stereotypes, some of which may be harmful. To deepen understanding of this issue, some researchers have explored the use of computational frameworks as an efficient and accurate way to analyze large amounts of character dialogue in scripts. However, some harmful stereotypes might be communicated not through what characters say, but through their actions.

To explore how characters’ actions might reflect stereotypes, Martinez and colleagues used a machine-learning approach to create a computational model that can automatically analyze scene descriptions in movie scripts and identify different characters’ actions. Using this model, the researchers analyzed over 1.2 million scene descriptions from 912 movie scripts produced from 1909 to 2013, identifying fifty thousand actions performed by twenty thousand characters.

Next, the researchers conducted statistical analyses to examine whether there were differences between the types of actions performed by characters of different genders. These analyses identified a number of differences that reflect known gender stereotypes.

For instance, they found that female characters tend to display less agency than male characters, and that female characters are more likely to show affection. Male characters are less likely to “sob” or “cry,” and female characters are more likely to be subjected to “gawking” or “watching” by other characters, highlighting an emphasis on female appearance.

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While the researchers’ model is limited by the extent of its ability to fully capture nuanced societal context relating the script to each scene and the overall narrative, these findings align with prior research on gender stereotypes in popular media, and could help raise awareness of how media might perpetuate harmful stereotypes and thereby influence people’s real-life beliefs and actions. In the future, the new machine-learning framework could be refined and applied to incorporate notions of intersectionality such as between gender, age, and race, to deepen understanding of this issue 

The authors add: “Researchers have proposed using machine-learning methods to identify stereotypes in character dialogues in media, but these methods do not account for harmful stereotypes communicated through character actions. To address this issue, we developed a large-scale machine-learning framework that can identify character actions from movie script descriptions. By collecting 1.2 million scene descriptions from 912 movie scripts, we were able to study systematic gender differences in movie portrayals at a large scale.” 

Open-access journal PLOS ONE Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278604

Source: PLOS

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aerospace

NASA Launches Aeronautics Spanish-Language Webpages

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Lee esta nota de prensa en español aquí.

As part of its effort to provide more resources and information to new audiences, NASA has launched new webpages featuring aeronautics information in Spanish. The webpages aim to make aeronautics content more accessible to the Spanish-language community.

“This is a significant step forward in our efforts to make the knowledge we’ve accumulated at NASA available to people all over the country, and the world. We’re making sure that as we explore and tackle the biggest challenges facing aviation, we’re providing benefits for all,” said Bob Pearce, associate administrator for NASA’s Aeronautics Research Mission Directorate. “By presenting aeronautics information and educational materials in Spanish, we’re working to foster a diverse, bold and effective next generation of explorers. We’re counting on this generation to help NASA carry its vision into the future.”

According to data provided by the U.S. Census Bureau, Spanish is the second most widely spoken language in the United States, after English. The translation of NASA’s aeronautics content will help inspire the next generation of NASA explorers.

The webpages provide educational material on the work being done by NASA’s Aeronautics Research Mission Directorate. They contain information on top agency priorities, including sustainable aviation. NASA is committed to achieving net-zero greenhouse gas emissions from the aviation sector by 2050 and works to achieve that goal by leading in fields ranging from green technologies and aircraft design to composite manufacturing and sustainable fuel testing. The new pages will help the agency introduce new members of the public to this work.

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In addition, the webpages will cover technological advances developed by NASA such as the Quesst mission, which will demonstrate quiet supersonic technology, possibly opening the door to commercial supersonic flight over land. Readers will be able to learn about NASA’s Advanced Air Mobility mission, which works to assist with the development of air transportation systems across the country, aeronautics tests at NASA’s wind tunnels and other facilities, and more.

The webpages also contain content designed for young learners focused on science, technology, engineering, and math (STEM), intended to help parents and teachers introduce children to these fields of study.

To view the Aeronautics webpages in Spanish, visit:

https://www.nasa.gov/aeroes

Source: NASA

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Race Relations

Across the US, white neighborhoods have more greenery, fewer dilapidated buildings, fewer multi-family homes

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A new nationwide study utilized millions of Google Street View images and linked built environment characteristics to racial disparities in adverse health outcomes such as diabetes, asthma, and poor sleep.

Newswise — Historic redlining and other racist policies have led to present-day racial and economic segregation and disinvestment in many cities across the United States. Research has shown how neighborhood characteristics and resources are associated with health disparities such as preterm birth and asthma, but most of these studies are limited in scale and overlook many aspects in a neighborhood that are difficult to measure, including dilapidated buildings and crosswalks.

Now, a new study led by Boston University School of Public Health (BUSPH) and the Center for Antiracist Research (CAR) at Boston University (BU) has utilized panorama digital technology through Google Street View (GSV) to identify these neighborhood characteristics on a national scale and shed light on how they contribute to racial and ethnic disparities in local resources and health outcomes across the US.

Published in the journal JAMA Network Open, the study found that predominantly White neighborhoods had better neighborhood conditions generally associated with good health, such as fewer neglected buildings and multi-family homes, and more greenery than neighborhoods with residents who were primarily Black, of other minority races, or of a variety of races and ethnicities. 

The findings underscore the need for comprehensive and accessible data platforms that researchers can utilize to better understand the role of the built environment on racial and health inequities, and inform policies that aim to create equitable neighborhood resources in all communities.

“Large datasets on determinants of health can help us better understand the associations between past and present policies—including racist and antiracist policies—and neighborhood health outcomes,” says study corresponding author Dr. Elaine Nsoesie, associate professor of global health at BUSPH. “Neighborhood images are one dataset that have the potential to enable us to track how neighborhoods are changing, how policies are impacting these changes and the inequities that exist between neighborhoods.”

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For the study, Dr. Nsoesie and colleagues analyzed national data on race, ethnicity, socioeconomics, and health outcomes, and 164 million GSV images across nearly 60,000 US census tracts. The team examined five neighborhood characteristics: dilapidated buildings, green spaces, crosswalks, multi-family homes, and single-lane roads.

The largest disparities in neighborhood environments were reflected in green space and non-single family homes. Compared to predominantly White neighborhoods, predominantly Black neighborhoods had 2 percent less green space, and neighborhoods with racial minorities other than Black had 11 percent less green space. Compared to White neighborhoods, neighborhoods with racial minorities other than Black had 17 percent more multi-family homes, while neighborhoods with Black residents and neighborhoods with residents representing a variety of races and ethnicities had 6 percent and 4 percent more multi-family homes, respectively.

The researchers also conducted modeling to measure how the built environment may influence the association between health outcomes and the racial makeup of neighborhoods, and found the strongest connections between sleeping problems among residents in neighborhoods with racial minorities other than Black or White, and asthma among neighborhoods with residents representing a variety of races and ethnicities.

“An interesting finding from our paper is how a considerable portion of the racial/ethnic differences of the built environment conditions was shown at the state level,” says study co-lead author Yukun Yang, a data scientist at CAR. “This prompts us to think practically about how state and local government and policymakers could and should address the inequitable distribution of built environment resources which could further address the health disparities we observed today.” 

“Our findings really demonstrate the path-dependent nature of inequality and racial disparities,” says study co-lead author Ahyoung Cho, a racial data/policy tracker at CAR and a political science PhD student at BU. “It is critical to develop appropriate policies to address structural racism.”

Source: Boston University School of Public Health

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News

Happy Chinese New Year, the Year of the Rabbit!

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Happy Chines New Year… https://en.wikipedia.org/wiki/Chinese_New_Year

Today is Chinese New Year…

Chinese New Year is the festival that celebrates the beginning of a new year on the traditional lunisolar Chinese calendar. In Chinese, the festival is commonly referred to as the Spring Festival (traditional Chinese: 春節; simplified Chinese: 春节; pinyinChūnjié) as the spring season in the lunisolar calendar traditionally starts with lichun, the first of the twenty-four solar terms which the festival celebrates around the time of the Chinese New Year. Marking the end of winter and the beginning of the spring season, observances traditionally take place from New Year’s Eve, the evening preceding the first day of the year to the Lantern Festival, held on the 15th day of the year. The first day of Chinese New Year begins on the new moon that appears between 21 January and 20 February.

Chinese New Year is one of the most important holidays in Chinese culture, and has strongly influenced Lunar New Year celebrations of its 56 ethnic groups, such as the Losar of Tibet (Tibetan: ལོ་གསར་), and of China’s neighbours, including the Korean New Year (Korean: 설날; RRSeollal), and the Tết of Vietnam,[6] as well as in Okinawa. It is also celebrated worldwide in regions and countries that houses significant Overseas Chinese or Sinophone populations, especially in Southeast Asia. These include Brunei, Cambodia, Indonesia, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. It is also prominent beyond Asia, especially in Australia, Canada, Mauritius, New Zealand, Peru, South Africa, the United Kingdom, and the United States, as well as various European countries.

The Chinese New Year is associated with several myths and customs. The festival was traditionally a time to honor deities as well as ancestors. Within China, regional customs and traditions concerning the celebration of the New Year vary widely, and the evening preceding the New Year’s Day is frequently regarded as an occasion for Chinese families to gather for the annual reunion dinner. It is also a tradition for every family to thoroughly clean their house, in order to sweep away any ill fortune and to make way for incoming good luck. Another custom is the decoration of windows and doors with red paper-cuts and couplets. Popular themes among these paper-cuts and couplets include good fortune or happiness, wealth, and longevity. Other activities include lighting firecrackers and giving money in red envelopes.

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Source: Wikipedia

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