How we describe inequality is significant because it impacts our view of who causes it and how society should address it. Photo via Getty Images

Look closely at any news article about inequality and you will quickly notice that there is more than one way to describe what is happening.

For example:

“In 2022, men earned $1.18 for every dollar women earned.”

“In 2022, women earned 82 cents for every dollar men earned.”

“In 2022, the gender wage gap was 18 cents per dollar.”

When pointing out differences in access to resources and opportunities among groups of people, we tend to use three types of language:

  1. Advantaged — Describes an issue in terms of advantages the more dominant group enjoys.
  2. Disadvantaged — Describes an issue in terms of disadvantages the less dominant group experiences.
  3. Neutrality — Stays general enough to avoid direct comparisons between groups of people.

The difference between these three lenses, referred to as “frames” in academic literature, may be subtle. We may miss it completely when skimming a news article or listening to a friend share an opinion. But frames are more significant than we may realize.

“Frames of inequality matter because they shape our view of what is wrong and what should be fixed,” says Rice Business Professor Sora Jun.

Jun led a research team that conducted multiple studies to understand which of the three frames people typically use to describe social and economic inequality. In total, they analyzed more than 19,000 mainstream media articles and surveyed more than 600 U.S.-based participants.

In Chronic frames of social inequality: How mainstream media frame race, gender, and wealth inequality, the team published two major findings.

First, people tend to describe gender and racial inequality using the language of disadvantage. For example, “The data showed that officers pulled over Black drivers at a rate far out of proportion to their share of the driving-age population.”

Jun’s team encountered the same rhetorical tendency with gender inequality. In most cases, people describe instances of gender inequality (e.g., the gender pay gap) in terms of a disadvantage for women. We are far more likely to use the statement “Women earned 82 cents for every dollar men earned” than “Men earned $1.18 cents for every dollar women earned.”

"We expected that people would use the disadvantage framework to describe racial and gender inequalities, and it turned out to be true,” says Jun. “We think that the reason for this stems from how legitimate we perceive different hierarchies to be.” Because demographic categories like gender and race are unrelated to talent or effort, most people find it unfair that resources are distributed unevenly along these lines.

On the other hand, Jun expected people to describe wealth inequality in terms of advantage rather than disadvantage. The public typically considers this form of inequality to be more fair than racial or gender inequality. “In the U.S., there is still a widespread belief in economic mobility — that if you work hard enough, you can change the socioeconomic group you are in,” she says.

But in their second major finding, she and fellow researchers discovered that the most common frame used to describe wealth inequality was no frame at all. We find this neutrality in statements like “Disparities in education, health care and social services remain stark.”

Jun is not sure why people take a neutral approach more frequently when describing wealth inequality (speaking specifically of economic classes outside of gender and race). She suspects it has something to do with the fact that we view wealth as a fluid and continuous spectrum.

The merits of the three frames are up for debate. Using the frame of disadvantage might seem to portray issues more sympathetically, but some scholars point to potential downsides. The language of disadvantage installs the dominant group as the measuring stick for everyone else. It may also put the onus of change on the disadvantaged group while making the problem seem less relevant to the dominant group.

“When we speak about the gender gap in terms of disadvantage, and helping women earn more compared to men, we automatically assume that men are making the correct amount,” says Jun. “But maybe we should be looking at both sides of the equation.”

On the other hand, Jun cautions against using a one-size-fits-all approach to describing inequality. “We have to be careful not to jump to an easy conclusion, because the causes of inequality are so vast,” she says.

For example, men tend to interrupt conversations in team meetings at higher rates than women. “Should we frame this behavior in terms of advantage or disadvantage, which naturally leads us to prompt men to interrupt less and women to interrupt more?” asks Jun. “We really don’t know until we understand the ideal number of interruptions and why this deviation is happening. Ultimately, how we talk about inequality depends on what we want to accomplish. I hope that through this research, people will think more carefully about how they describe inequality so that they capture the full story before they act.”

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This article originally ran on Rice Business Wisdom and was based on research from Sora Jun, Rosalind M. Chow, A. Maurits van der Veen and Erik Bleich.

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Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”

12 winners named at CERAWeek clean tech pitch competition in Houston

top teams

Twelve teams from around the country, including several from Houston, took home top honors at this year's Energy Venture Day and Pitch Competition at CERAWeek.

The fast-paced event, held March 25, put on by Rice Alliance, Houston Energy Transition Initiative and TEX-E, invited 36 industry startups and five Texas-based student teams focused on driving efficiency and advancements in the energy transition to present 3.5-minute pitches before investors and industry partners during CERAWeek's Agora program.

The competition is a qualifying event for the Startup World Cup, where teams compete for a $1 million investment prize.

PolyJoule won in the Track C competition and was named the overall winner of the pitch event. The Boston-based company will go on to compete in the Startup World Cup held this fall in San Francisco.

PolyJoule was spun out of MIT and is developing conductive polymer battery technology for energy storage.

Rice University's Resonant Thermal Systems won the second-place prize and $15,000 in the student track, known as TEX-E. The team's STREED solution converts high-salinity water into fresh water while recovering valuable minerals.

Teams from the University of Texas won first and second place in the TEX-E competition, bringing home $25,000 and $10,000, respectively. The student winners were:

Companies that pitched in the three industry tracts competed for non-monetary awards. Here are the companies named "most-promising" by the judges:

Track A | Industrial Efficiency & Decarbonization

Track B | Advanced Manufacturing, Materials, & Other Advanced Technologies

  • First: Licube, based in Houston
  • Second: ZettaJoule, based in Houston and Maryland
  • Third: Oleo

Track C | Innovations for Traditional Energy, Electricity, & the Grid

The teams at this year's Energy Venture Day have collectively raised $707 million in funding, according to Rice. They represent six countries and 12 states. See the full list of companies and investor groups that participated here.

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This article originally appeared on our sister site, EnergyCapitalHTX.com.