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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Rice, Houston Methodist award $90K for cancer research projects

seed grants

Rice University’s Synthesis X Center, in partnership with the Houston Methodist Neal Cancer Center, announced earlier this month the organizations would award $90,000 in seed grant funding to two projects that could help fight cancer. One would ease the pain of chemotherapy, while the other could change the way the progression of leukemia is tracked.

“We’re excited to be collaborating with the Neal Cancer Center to support collaborative, novel and interdisciplinary proposals to improve cancer care and outcomes,” Han Xiao, Rice professor of chemistry and the director of the SynthX Center, said in a news release. “Together, we can achieve translational excellence.”

The projects

Chemotherapy is one of the most effective cancer treatments, but it can be hard on the body. The human body doesn’t like being injected with radioactive material, especially the skin around the injection point, which can become extremely irritated. For patients in long-term treatment, the skin irritation can be more than just a bother; it can lead to infections that are dangerous to a compromised immune system.

Angel A Marti, a professor of chemistry at Rice University, and Biana Godin, an associate professor of nanoscience at Houston Methodist Research Institute, are experimenting with metal nanoclusters as a way to block radiation at the injection site. The nanocluster could be applied in a cream or a gel on the skin, serving as a type of shield against the harsh radioactive material.

Meanwhile, Yuan Ma, an assistant professor of chemistry at Rice, and Shu-Hsia Chen, a professor of immunology at Houston Methodist Research Institute, are working with m6A. Discovered in the 1970s, m6A is the most prevalent chemical modifier found in mRNA in mammals. It is prevalent in many cancers, including leukemia.

Ma and Chen are working on measuring the amount of m6A in leukemia to see if it can determine the most effective cancer treatments. The team is also experimenting with ways to shut off m6A to see if it makes current leukemia treatments more effective.

Progress from SynthX

SynthX was first launched in April 2024 to turn research from Rice and Houston Methodist into real-world cancer treatments. Within a year, the center had secured $1.5 million in grant money to work on crossing the blood-brain barrier in brain cancer treatments. These latest awards show that SynthX Center continues to bridge the worlds of research and clinical practice.

“This collaboration reflects a shared commitment to team science in cancer research,” Daniela Matei, the director of the Houston Methodist Neal Cancer Center, added in the release. “These are the types of translational and pioneering projects that lead to transformation in patient care.”'

The SynthX Center awarded $90,000 in seed grants to three teams in 2025 and $80,000 to three teams in 2024.

Tesla self-driving mode wasn't to blame in Houston-area crash, report suggests

Tesla news

Federal safety investigators looking into a runaway Tesla that killed a grandmother in her home say the driver had pressed the accelerator to full speed, suggesting the vehicle's self-driving software was not to blame.

The driver had told police that he had the self-driving software turned on, but a report from the National Transportation Safety Board concluded that he had actually overridden that feature when he pushed hard on the pedal. Moments later the Tesla Model 3 raced down a residential street in Katy, Texas, at highway speeds, slammed into a brick home and killed a 76-year-old woman standing in the front room.

The crash last month drew national attention because Tesla CEO Elon Musk is seeking to reassure the public its self-driving feature is safe as he prepares to turn hundreds of thousands of Teslas already on the road into fully automatic vehicles and begin selling two-seated Cybercabs missing steering wheels and pedals.

The crash came two months after officials at a separate federal agency, the National Highway Traffic Safety Administration, announced it was elevating a 2024 investigation of the self-driving feature to new “engineering analysis” level, raising the possibility of a recall of 3.2 million Tesla vehicles.

That NHTSA probe was triggered by crashes where the self-driving feature failed to alert drivers to take control in fog and other poor visibility conditions.

The agency opened an investigation last year into 58 incidents in which Teslas reportedly violated traffic safety laws while using self-driving technology, leading to more than a dozen crashes and fires and nearly two dozen injuries.

Separate from the National Transportation Safety Board, NHTSA is also looking into the Tesla house crash in Texas, one of 46 “special crash” investigations of Tesla's self-driving or driver-assistance technology in the past decade, according to the agency’s records. In more than a dozen of those crashes, at least one person — a driver, passenger or pedestrian — was killed.

Tesla had originally called its driver assistance software Full Self-Driving, or FSD, but auto experts and regulators complained it was misleading because drivers must always keep their eyes on the road and be ready to take over at any time.

The company has since changed the name to Full Self-Driving (Supervised).

Video of the Katy, Texas, accident shows the Tesla traveling at more than 70 mph (112.65 kilometers per hour), jumping a curb then tearing across a lawn before crushing through a brick wall of a home. A woman standing feet away, Martha Avila, was found amid piles of crumbling plaster, split beams and bits of furniture and rushed to a hospital but died.

Sales of Tesla cars still haven't recovered fully from boycotts last year over Musk's political stands, but the stock is rising anyway as he has successfully shifted attention away from the sales figures. He says they matter less now that the company is on the cusp of major technological advances, such as turning Teslas into hands-free vehicles and having its Optimus robots take over for humans for tasks at home and work.

Tesla stock has risen 22% in the past year and is currently trading at 170 times expected annual earnings compared to 20 for the S&P 500.

For its second-quarter financial results, financial analysts surveyed by FactSet expect earnings per share will barely budge — 32 cents versus 33 cents a year earlier — continuing a sixth quarter streak of flat or falling profits.

London AI startup selects Houston for first U.S. office after $20M raise

welcome to houston

London-based AI firm Applied Computing has announced a $20 million Series A round and a new office in Houston.

The new Bayou City office is Applied Computing’s first in the United States and part of its North American expansion. The company is known for its Orbital AI platform, which is tailored for energy operations.

The funding round was led by Houston-based KBR Inc., with participation from San Francisco-based Databricks Ventures. KBR’s investment was first announced in March.

KBR and Applied Computing have also entered into a multi-year agreement to deliver exclusive AI products for the energy sector. KBR already has integrated Orbital into its INSITE 3.0 platform for energy projects, and is also using the product for ammonia production.

Applied Computing’s Orbital platform combines physics-grounded intelligence with models across chemical engineering, time-series forecasting and language, according to the company. The system analyzes sensor readings and can recognize a facility’s equipment constraints and operator activity. The platform can also allow technicians to run simulations of how a change to a facility could affect the rest of its operations.

According to TechCrunch, Applied Computing will use the $20 million to further explore projects and deployments with the energy sector, hire engineering and research positions, and continue to expand internationally, potentially into the Middle East.

The company is also working on deals with a major U.S. stream operator, TechCrunch reports. And Applied Computing shared on LinkedIn that it plans to announce its first partnership with a major European oil company in the coming weeks.

“Yesterday we showed Orbital live in deployments at our demo day at the Energy Institute in London,” Callum Adamson, CEO and co-founder of Applied Computing, posted on LinkedIn on July 16. “Today, we're announcing the capital to scale it globally as well as the launch of our new offices in Houston and Bangalore. In the weeks following, there will be more announcements on our progress, partnerships and deployments.”

The company opened its Bangalore offices in December.