Rice University research shows the harmful impact of myths regarding motherhood, education, and professional agency. Photo via Getty Images

Gender wage discrimination remains a stubborn problem in the United States. On average, women are paid only 80 cents for every dollar paid to non-Hispanic white men, and far greater gaps persist for Latina, Black, and Native American women. Despite progress in recent decades, we have a long way to go on this issue. At the current rate, pay inequity will persist until the distant year of 2152.

What keeps us from bridging the wage gap?

In a peer-reviewed commentary regarding research that examines workplace victim-blaming, Rice University professor Mikki Hebl and former Rice Ph.D. students Shannon Cheng, Abigail Corrington, Linnea Ng and Ivy Watson interrogate the role victim-blaming plays in perpetuating the gender wage gap. According to Hebl and her team, harmful myths regarding women’s relation to the workplace cloud our understanding of why the wage gap exists to begin with. To combat the problem, they say, we must first identify and debunk such misconceptions. And then, organizational leaders must take tangible steps to implement nondiscriminatory practices.

Here are a few of the victim-blaming myths Hebl and her team attribute to the persisting gender wage gap:

Myth: Motherhood drives women to leave the workforce.

This idea doesn’t hold up under scrutiny. In 44 percent of families, women are breadwinners, and 75 percent of single mothers are sole breadwinners. On top of workplace labor, women also spend more time on service-related activities than men and an average of 65 more minutes per day on childcare and household maintenance. Moreover, mothers often face forms of workplace discrimination that fathers simply do not. The more prominent causes of women’s decision to exit are unrelated to motherhood, such as limited career opportunities and unsatisfying work environments.

Myth: Women work in less lucrative professions.

There certainly are male and female-dominated industries. But this myth suggests that women willingly opt for lower-paying careers. It also implies that some professions do not have a problem with wage inequity. But the pay gap persists across professions, and at every level. Even in female-dominated professions, women are paid less than men who share the same level of experience.

Myth: Women don’t have as much education or experience as men, and they don’t ask for what they want.

Women now hold more college and graduate degrees than men, but they continue earning less. And as women and men gain career experience, the gender pay gap widens. Indeed, the gap is largest at the executive level. In terms of women’s experience with promotions and salary increases, stereotypes and gender biases make it challenging for them to secure equal pay for equal work. Men and women are both inclined to ask for what they want, but salary negotiations often do not yield the same results for women as for men.

Victim-blaming myths like these prevent us from making progress on the issue of pay inequity. We must actively debunk them. But just as importantly, researchers argue, company leaders must put energy and resources toward addressing the problem.

Beyond deflating misconceptions about women and work, how can we change the status quo? Based on research, Hebl and her team offer these actionable strategies and suggestions:

  • Identify and remove barriers to pay equity (e.g., hold focus groups with women in the organization).
  • Provide equal growth opportunities (e.g., offer equal access to mentorship).
  • Strive toward work/life balance (e.g., subsidize or create on-site childcare).
  • Ensure nondiscriminatory policies (e.g., publish compensation ranges).
  • Promote male allyship (e.g., men in positions of influence advocate for equity).

This final strategy stands out as perhaps the most intriguing. It seems obvious to implement nondiscriminatory policies like transparency about promotion criteria. Such policies are essential for bridging the wage gap and building a culture of trust.

But what role, according to research, do male allies play in effecting a major societal and organizational change? At the very least, men can help debunk the myths that Hebl et al. describe. But more importantly, research shows that men are more likely to support gender causes when championed by other men. Male allies have immense power in advancing the cause of gender equality, which means their involvement is not just welcome but essential in the pursuit to make one dollar for men equal one dollar for women.

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This article originally ran on Rice Business Wisdom and was based on research from Michelle "Mikki" Hebl, the Martha and Henry Malcolm Lovett Chair of Psychology at Rice University and a professor of management at Jones Graduate School of Business.

A patent is an asset — one with a price associated with it when it comes to procuring a loan for your business. Photo via Getty Images

Rice research: What innovations can be used to borrow against?

Houston voices

For companies and leaders, patents represent important assets. They’re a marker of innovation and tech development. But patents do so much more than protect intellectual property. Firms increasingly deploy them as collateral to secure loans. Between 1995 and 2013, the number of patents pledged as loan collateral increased from about 10,000 to nearly 50,000. Forty percent of U.S. patenting firms have used patents as collateral.

However, patents are intangible assets, and their liquidity and liquidation value are difficult to assess. To evaluate an individual patent, lenders must consider the invention space to which the patent belongs. A patent’s linkage to prior inventions can provide important information for lenders, as the linkage affects the extent to which the patent under consideration may be redeployed and potentially purchased by other firms in the case of loan default.

Rice Business professor Yan Anthea Zhang examined more closely how this market operates and how both lenders and borrowers can make more informed decisions on which patents make appealing collateral. In their paper, “Which patents to use as loan collateral? The role of newness of patents' external technology linkage,” Zhang, who specializes in strategic management, and her co-authors studied the data on 107,180 U.S. semiconductor patents owned by 436 U.S. firms. The team focused on semiconductor patents because the semiconductor industry involves intensive innovation, which leads to many patent applications and grants. The market for semiconductor patents is an active and well-functioning market, given specialization in different stages of the innovation process and the growing technological market. Information on whether a patent was used as loan collateral came from the USPTO Patent Assignments Database.

Zhang and her colleagues argue that lenders prefer patents linked to prior inventions that are relatively new because these patents are riding on recent technology waves and are less likely to become obsolete. As a result, such patents are likely to remain deployable to other firms in the future. However, patents that are based upon too new prior inventions might not prove to be commercially viable and carry higher risk for lenders.

As a result of this research, Zhang and her colleagues found an inverted U-shape relationship to demonstrate the likelihood that a patent will be used as loan collateral. On one end, patents based upon the newest prior inventions, on the other, patents based upon mature prior inventions. The curve of the U-shape represents the sweet spot for patent collateral—the patents’ technological base is new enough to be relevant and competitive with other firms in its invention space, but not so new that it has yet to prove market success.

Zhang’s team also found that the impact of external linkage also varies depending on borrower attributes, especially the borrowers’ expertise in the invention space. If a borrower is a technological leader in the invention space, the market tends to give the borrower credit, and as a result, even if its patents are based upon very new prior inventions, its patents are still likely to be accepted as collateral.

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This article originally ran on Rice Business Wisdom and was based on research from Yan Anthea Zhang, the Fayez Sarofim Vanguard Professor of Management at Rice Business.

Here's how AI-based chat will effect research. Graphic byMiguel Tovar/University of Houston

University of Houston: Here's what intuitive AI and ChatGPT mean for research

HOUSTON VOICES

Researchers have to write extremely specific papers that require higher-order thinking — will an intuitive AI program like OpenAI’s ChatGPT be able to imitate the vocabulary, grammar and most importantly, content, that a scientist or researcher would want to publish? And should it be able to?

University of Houston’s Executive Director of the Research Integrity and Oversight (RIO) Office, Kirstin Holzschuh, puts it this way: “Scientists are out-of-the box thinkers – which is why they are so important to advancements in so many areas. ChatGPT, even with improved filters or as it continues to evolve, will never be able to replace the critical and creative thinking we need in these disciplines.”

“A toy, not a tool”

The Atlantic published, “ChatGPT Is Dumber Than You Think,” with a subtitle advising readers to “Treat it like a toy, not a tool.” The author, Ian Bogost, indulged in the already tired troupe of asking ChatGPT to write about “ChatGPT in the style of Ian Bogost.” The unimaginative but overall passable introduction to his article was proof that, “any responses it generates are likely to be shallow and lacking in depth and insight.”

Bogost expressed qualms similar to those of Ezra Klein, the podcaster behind, “A Skeptical Take on the AI Revolution.” Klein and his guest, NYU psychology and neural science professor Gary Marcus, mostly questioned the reliability and truthfulness of the chatbot. Marcus calls the synthesizing of its databases and the “original” text it produces nothing more than “cut and paste” and “pastiche.” The algorithm used by the program has been likened to auto-completion, as well.

However, practical use cases are increasingly emerging, which blur the lines between technological novelty and professional utility. Whether writing working programming code or spitting out a rough draft of an essay, ChatGPT does have a formidable array of competencies. Even if just how competent it is remains to be seen. All this means that as researchers look for efficiencies in their work, ChatGPT and other AI tools will become increasingly appealing as they mature.

Pseudo-science and reproducibility

The Big Idea reached out to experts across the country to determine what might be the most pressing problems and what might be potential successes for research now that ChatGPT is readily accessible.

Holzschuh, stated that there are potential uses, but also potential misuses of ChatGPT in research: “AI’s usefulness in compiling research proposals or manuscripts is currently limited by the strength of its ability to differentiate true science from pseudo-science. From where does the bot pull its conclusions – peer-reviewed journals or internet ‘science’ with no basis in reproducibility?” It’s “likely a combination of both,” she says. Without clear attribution, ChatGPT is problematic as an information source.

Camille Nebeker is the Director of Research Ethics at University of California, San Diego, and a professor who specializes in human research ethics applied to emerging technologies. Nebeker agrees that because there is no way of citing the original sources that the chatbot is trained on, researchers need to be cautious about accepting the results it produces. That said, ChatGPT could help to avoid self-plagiarism, which could be a benefit to researchers. “With any use of technologies in research, whether they be chatbots or social media platforms or wearable sensors, researchers need to be aware of both the benefits and risks.”

Nebeker’s research team at UC San Diego is conducting research to examine the ethical, legal and social implications of digital health research, including studies that are using machine learning and artificial intelligence to advance human health and wellbeing.

Co-authorship

The conventional wisdom in academia is “when in doubt, cite your source.” ChatGPT even provides some language authors can use when acknowledging their use of the tool in their work: “The author generated this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.” A short catchall statement in your paper will likely not pass muster.

Even when being as transparent as possible about how AI might be used in the course of research or in development of a manuscript, the question of authorship is still fraught. Holden Thorp, editor-in-chief of the Science, writes in Nature, that “we would not allow AI to be listed as an author on a paper we published, and use of AI-generated text without proper citation could be considered plagiarism.” Thorp went on to say that a co-author of an experiment must both consent to being a co-author and take responsibility for a study. “It’s really that second part on which the idea of giving an AI tool co-authorship really hits a roadblock,” Thorp said.

Informed consent

On NBC News, Camille Nebeker stated that she was concerned there was no informed consent given by the participants of a study that evaluated the use of a ChatGPT to support responses given to people using Koko, a mental health wellness program. ChatGPT wrote responses either in whole or in part to the participants seeking advice. “Informed consent is incredibly important for traditional research,” she said. If the company is not receiving federal money for the research, there isn’t requirement to obtain informed consent. “[Consent] is a cornerstone of ethical practices, but when you don’t have the requirement to do that, people could be involved in research without their consent, and that may compromise public trust in research.”

Nebeker went on to say that study information that is conveyed to a prospective research participant via the informed consent process may be improved with ChatGPT. For instance, understanding complex study information could be a barrier to informed consent and make voluntary participation in research more challenging. Research projects involve high-level vocabulary and comprehension, but informed consent is not valid if the participant can’t understand the risks, etc. “There is readability software, but it only rates the grade-level of the narrative, it does not rewrite any text for you,” Nebeker said. She believes that one could input an informed consent communication into ChatGPT and ask for it to be rewritten at a sixth to eighth grade level (which is the range that Institutional Review Boards prefer.)

Can it be used equitably?

Faculty from the Stanford Accelerator for Learning, like Victor Lee, are already strategizing ways for intuitive AI to be used. Says Lee, “We need the use of this technology to be ethical, equitable, and accountable.”

Stanford’s approach will involve scheduling listening sessions and other opportunities to gather expertise directly from educators as to how to strike an effective balance between the use of these innovative technologies and its academic mission.

The Big Idea

Perhaps to sum it up best, Holzschuh concluded her take on the matter with this thought: “I believe we must proceed with significant caution in any but the most basic endeavors related to research proposals and manuscripts at this point until bot filters significantly mature.”

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This article originally appeared on the University of Houston's The Big Idea. Sarah Hill, the author of this piece, is the communications manager for the UH Division of Research.

Owning or even imagining that you own an object linked to a particular task can make you feel — and act — more like an adept. Photo via Getty Images

Houston research: Your tools can reflect your workplace skills

Houston voices

Want to get better at a task? It may be possible to shop — or imagine — your way to success.

Just pretending to shop for items associated with certain skills (for example, a fancy calculator) may actually improve your performance in areas related to that skill (in this case, math).

That’s because our identities are highly influenced by our possessions — which we often experience as part of ourselves. As a result, this activation of an identity by our possessions, even imaginary ones, can enhance performance. For example, one study found that by using a pen labeled “MIT” on GRE exams, students scored higher than those using a standard Pilot pen, particularly when they believed that their inner ability was fixed, and that they had to rely on external products to improve their ability.

In 2018, Rice Business professor Jaeyeon Chung and Gita V. Johar of Columbia University took a close look at the implications of this human quirk.

In a series of experiments, Chung and Johar found that the product-related activation of our identities (e.g., calculator ownership awakening an inner math prodigy) can actually de-activate our identities unrelated to the product, and undermine performance in other tasks.

For example, shopping for a calculator could make you perform better on a math test, but worse on a creative-writing essay.

Merely owning an item, the scholars discovered, is only part of the equation. Self-concept clarity — that is, the strength and clarity of one’s personal beliefs — makes a difference as well. A person whose self-concept is well-defined, consistent, and stable is less likely to be influenced by external factors such as possessions.

To measure the phenomenon, Chung and her colleague devised a series of experiments. The results showed that when a person merely imagines an item she longs to own, two inner changes occur: Identities related to the product are awakened, and identities unrelated to the desired object are stifled. Strikingly, these changes have measurable consequences on the performance of tasks.

But how do you awaken an inner self through possession, and measure its effects? The team found an ingenious approach: They assigned people to a control group or an experimental group, and then asked them to peruse an online IKEA. The control group was told to shop for items to go in a senior citizen home. The experimental group shopped for items to go into their own homes.

The experimental group, who got to imagine items such as a MALM bed in their own bedrooms, were more likely to think of themselves as artistic designers than were their counterparts, the imaginary retirement home shoppers. The exercise, in other words, had activated participants’ art-related identities.

Next, Chung and Johar asked everyone to complete a math task. The experimental group scored lower at this than did those in the control group. Their newly awakened identities as design mavens had undermined their ability to solve math problems, apparently because they were unrelated to the fetching Scandinavian décor they’d imagined owning.

The researchers then took another approach. Asking one group of participants to imagine owning a calculator, they activated that group’s “math identity.” They then asked all the participants to engage in a short IQ test. Though there was only one test, the researchers labeled it two different ways, indicating to some participants that the test measured math skills, and to others that it measured creative writing skills.

Despite the test being exactly the same, the would-be calculator owners performed markedly worse when they thought they were doing a creative writing project than when they thought the test measured their math skills. Why, exactly? The researchers concluded that imagining owning a piece of math-y technology and activating their “math person identities” tamped down participants’ “creative writer” identities — so much so that it actually degraded their performance in that area.

In a third experiment, Chung and Johar asked a group to envision calculators that they actually owned, rather than simply imagining buying one. Again, the group that felt ownership regarding a math tool performed better on tasks that seemed math-related, but worse on tasks that seemed unrelated to math. The finding was robust when the task itself was exactly the same and the only difference how the task was labeled.

Interestingly, identity activation and performance were influenced by the participants’ level of self-concept clarity. Some people have a clear and consistent self-view that does not vary over time; these are individuals who are less likely to rely on their possessions or other environmental stimuli to infer who they are. These individuals were less likely to be affected by the “ownership” of a calculator.

In other words, self-concept clarity limited the power of ownership on identity activation and performance. Chung and Johar’s findings offer practical implications for both business and academia. Owning or even imagining that you own an object linked to a particular task can make you feel — and act — more like an adept.

So the next time you have a big quantitative test coming up, consider browsing for a high-end calculator first — and unwinding with your oil paints or “Infinite Jest” when you’re done. For best results, of course, take the test with your Rice-labeled pen.

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This article originally ran on Rice Business Wisdom and was based on research from Jaeyeon (Jae) Chung is an assistant professor of marketing at Jones Graduate School of Business at Rice University.

Earnings report delays generally lead to drops in stock prices. Disclosure can soften this market reaction. Photo via Getty Images

Houston research: Is no news always bad news in market reports?

houston voices

Investors eagerly wait for the news in their earnings reports. When these reports don't appear on the expected date, investors worry — and stock prices often fall as a result. But what if managers could present late reports in a way that spared their companies?

Research by K. Ramesh, a professor at Rice Business, shows that managers' approach to late earnings reports can profoundly affect market reaction. When firms put off filing a report, it's up to managers to decide whether to speak up or stay quiet. Those who choose to talk about a postponement then must decide how, what and how much to say.

All earnings delays, whether they're attended by a statement or not, prompt negative market reaction, prior research suggests. But in his research, Ramesh, Herbert S. Autrey Professor of Accounting, wanted to learn more about the exact consequences of these late reports, and how managers can lessen the blowback.

To do this, Ramesh and a team of coauthors first looked at the incidence, timing and contents of a comprehensive sample of press releases announcing an earnings delay. Then they studied what those delays did to market value.

Conventional wisdom in the business press already suggested that investors viewed any announcement of a delayed earnings report as bad news. But finance theorists tell a more complicated story, one in which the market response might be partially shaped by managerial behavior. Subtle factors, they found, such as whether the impending delay is discussed or treated with silence, really can make a difference.

In the view of some theorists, merely announcing a delay can sometimes avert a drop in stock prices. Others argue that this isn’t necessarily the case, especially if the company discloses that the delay stemmed from legal concerns. The better approach: making it clear up front that reports aren't being postponed to hide disastrous information. But what if the information is indeed disastrous?

That may be the one case where disclosure won’t change much, Ramesh and his team found.

“Those companies that are in fact concealing disastrous results will experience no benefits (in the form of higher stock price) from revealing their true situation,” the research team wrote, “because the market will infer the worst from the manager’s decision not to announce the delay.” For this reason, they added, delayed earnings without a stated explanation prompt the most negative market reaction. As in so many areas of public relations, without a narrative, investors will infer a negative one of their own.

To better understand the impact of late reports, Ramesh and his coauthors built a comprehensive sample of 545 delay announcements by using a keyword search of the Dow Jones Factiva database between January 1, 1995, and December 31, 2009.

As conventional wisdom suggested, the study showed that announcements of late earnings reports led to negative market reactions. (Earlier studies have shown smaller firms are hit hardest by this dynamic, perhaps because investors assume large companies have more finely tuned financial reporting systems, so are less worried by their earnings delays).

Consistent with the anecdotal evidence, the average one-day abnormal stock return for the sample was -6.29 percent, while the median return was -2.27 percent. Both figures are economically and statistically significant.

The researchers next classified the announcements according to stated reason, dividing the delays into “Accounting” and “Non-Accounting” categories. “Accounting” explanations were subdivided into “Accounting Issue,” “Accounting Process” and “Rule Change.”

Meanwhile, “Non-Accounting” explanations were divided into “Business,” which linked the delay to some event such as divestitures or regulatory proceedings, and “Other,” which ranged from earthquakes to power outages. Finally, there were delays for no stated reason at all.

About two-thirds of the late announcements, the team found, were linked to accounting. When firms named a specific accounting issue as the cause for delay, the average abnormal return reached a statistically significant -8.15 percent. When managers explained that the accounting process was not complete, the average abnormal return was slightly lower, at -7.04 percent.

After accounting issues, business events drove most earnings delays. In theory, these events could have been either good or bad news. But the average abnormal return for the subsample was a statistically significant -3.74 percent — a reflection of the fact that most business events linked to late earnings reports tend to be negative.

Curiously, the average abnormal return for the grouping classified as “Other” was almost nil — at 0.53 percent. This suggests that the market does not penalize managers for events outside of their control that have little, if any, relevance to firm performance.

“No Reason,” the researchers found, was the most damaging explanation of all. Seven percent of the sample, or 37 out of 545 delays, came without a stated reason. The average abnormal return for these was a significant -10.41 percent, a greater negative number than the returns for any of the other reasons.

So what should managers do when a deadline is going to be busted? Bite the bullet and disclose the reasons, Ramesh suggests. For one thing, it helps limit legal exposure and preserve credibility. When the reason for the late report is innocuous, explaining to investors can also mitigate the market's displeasure. A caveat: While informing investors that a power outage caused earnings delay will calm jitters, disclosure may not make a difference if the company just can’t balance its books.

It's human nature, apparently, to read no news as bad news. Relaying something—anything—about the cause of a late report seems to soothe investors' nerves by preventing them from filling the silence themselves.

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This article originally ran on Rice Business Wisdom and was based on research from K. Ramesh, Tiago Duarte-Silva, Huijing Fu and Christopher F. Noe. K. Ramesh is the Herbert S. Autrey Professor of Accounting at Jones Graduate School of Business at Rice University.

To err is human, after all. Graphic byMiguel Tovar/University of Houston

University of Houston: Navigating non-compliance and human error in research

houston voices

To comply is to obey, or conform to instruction or official requirements. In a perfect world, research non-compliance wouldn’t occur and following the rules would be a behavioral norm. But the reality is, to err is human.

To err is human

Often times the judgement of our own, and others, poor decision-making is rooted in the innate tendency to view things in black or white – categorizing behaviors as either right or wrong, good or bad, thus deeming them as either ethical or unethical.

But this way of thinking often conflicts with the gray world in which we exist. So what happens when research decisions land somewhere in the moral gray area?

Before answering, here are two situations to consider that involve the over-enrollment of research participants:

Case 1:
The IRB has approved a survey for 40 subjects. The PI realizes after the survey has been open for several weeks that she forgot to set a participant limit within the survey program and 60 subjects have completed the survey.

Case 2:

A study involving a new drug to control diabetes symptoms is approved to enroll 30 participants. The study doctor thinks the drug may be beneficial, so she continues enrolling, for a total of 80 subjects.

The devil is in the details

Why is over-enrollment of subjects considered non-compliance?

Many institutions have agreed, within their assurance to the U.S. Department of Health and Human Services (HHS), to apply the Common Rule to all human subjects research, whether the research is funded or not.

The Common Rule regulations found at 45 CFR 46.109(a) and 45 CFR 46.111 (1) state that the IRB shall review and have authority to approve, require modifications in (to secure approval), or disapprove all research activities. This includes the maximum number of research .

And what must the IRB review?

Under the above regulatory requirements, the IRB must evaluate all instances of non-compliance.

In these cases of over-enrollment, the IRB must review the number of subjects over-enrolled and assess any potential effects on additional subjects and/or the research, as well as determine if the noncompliant data may be used for research purposes.

What UH IRB says about Case 1:

While over-enrollment in a survey seems low-risk, depending on the content of the survey questions, the IRB could determine the issue to be more serious, such as for a study collecting data related to illegal substance use or questions about traumatic events (legal or psychological harm). The IRB must ensure that risks to subjects are minimized; only the number of subjects needed to statistically justify the research are approved. Depending on the number of subjects over-enrolled and the time period over which they participated, the non-compliance could also be considered continuing.

What UH IRB says about Case 2:

Investigational drug studies often pose more than minimal risk of harm to subjects. In these studies, it is even more critical to ensure that additional subjects are not exposed to potential harms without scientific justification

In a drug study, the PI may not continue a study based on opinion; the reason a physician is blinded to treatment assignment in many drug studies is to avoid potential bias.

Finding non-compliance: What can you do?

If the number of subjects enrolled exceeds the number approved by the IRB, a finding of non-compliance is justified. The IRB will review the numbers, the Principal Investigator’s reasons for over-enrollment and assess what procedures were conducted in these subjects. Often over-enrollment is inadvertent, however the committee also has the ultimate authority to determine whether the data may be used for research purposes.

Corrective actions, such as continuing education of the PI and/or study team to ensure this issue does not occur again in the future, are often required. In the most serious cases, the IRB may suspend or terminate approval.

If the non-compliance rises to the level of being serious (harms or has the potential to harm subjects or others) and/or continuing in nature, it must be reported to federal oversight agencies such as the Health and Human Services Office for Human Research Protections (OHRP) and the FDA. These agencies ensure that the institution is monitoring for these activities and puts appropriate fixes in place.

The importance of intetrity

Non-compliant research can be due to inadvertent errors or deliberate acts of noncompliance. The results could be the same. Human subjects could be harmed. Funding and reputation at an institution conducting research could be negatively affected. In times of reduced federal funding for basic research, there are direct financial costs to the agencies when funds and resources are misused.

The responsibility of ensuring that research protocols are adhered to rests upon the shoulders of the researchers involved.

If you were a member on the IRB, what would you consider to be appropriate consequences for the PI in these situations?

It’s important to note that non-compliance, whether it’s a “little white lie/inadvertent error” or a deliberate violation of the approved protocol can undermine the integrity of both the research process and the academic research enterprise.

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This article originally appeared on the University of Houston's The Big Idea. Nitiya Spearman, the author of this post, is the internal communications coordinator for the UH Division of Research.

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Rice University's edtech company receives $90M to lead NSF research hub

major collaboration

An educational technology company based out of Rice University has received $90 million to create and lead a research and development hub for inclusive learning and education research. It's the largest research award in the history of the university.

OpenStax received the grant funding from the U.S. National Science Foundation for a five-year project create the R&D hub called SafeInsights, which "will enable extensive, long-term research on the predictors of effective learning while protecting student privacy," reads a news release from Rice. It's the NSF's largest single investment commitment to national sale education R&D infrastructure.

“We are thrilled to announce an investment of $90 million in SafeInsights, marking a significant step forward in our commitment to advancing scientific research in STEM education,” NSF Director Sethuraman Panchanathan says in the release. “There is an urgent need for research-informed strategies capable of transforming educational systems, empowering our nation’s workforce and propelling discoveries in the science of learning.

"By investing in cutting-edge infrastructure and fostering collaboration among researchers and educators, we are paving the way for transformative discoveries and equitable opportunities for learners across the nation.”

SafeInsights is funded through NSF’s Mid-scale Research Infrastructure-2 (Mid-scale RI-2) program and will act as a central hub for 80 partners and collaborating institutions.

“SafeInsights represents a pivotal moment for Rice University and a testament to our nation’s commitment to educational research,” Rice President Reginald DesRoches adds. “It will accelerate student learning through studies that result in more innovative, evidence-based tools and practices.”

Richard Baraniuk, who founded OpenStax and is a Rice professor, will lead SafeInsights. He says he hopes the initiative will allow progress to be made for students learning in various contexts.

“Learning is complex," Baraniuk says in the release. "Research can tackle this complexity and help get the right tools into the hands of educators and students, but to do so, we need reliable information on how students learn. Just as progress in health care research sparked stunning advances in personalized medicine, we need similar precision in education to support all students, particularly those from underrepresented and low-income backgrounds.”

OpenStax awarded $90M to lead NSF research hub for transformational learning and education researchwww.youtube.com

2 Houston startups selected by US military for geothermal projects

hot new recruits

Two clean energy companies in Houston have been recruited for geothermal projects at U.S. military installations.

Fervo Energy is exploring the potential for a geothermal energy system at Naval Air Station Fallon in Nevada.

Meanwhile, Sage Geosystems is working on an exploratory geothermal project for the Army’s Fort Bliss post in Texas. The Bliss project is the third U.S. Department of Defense geothermal initiative in the Lone Star State.

“Energy resilience for the U.S. military is essential in an increasingly digital and electric world, and we are pleased to help the U.S. Army and [the Defense Innovation Unit] to support energy resilience at Fort Bliss,” Cindy Taff, CEO of Sage, says in a news release.

A spokeswoman for Fervo declined to comment.

Andy Sabin, director of the Navy’s Geothermal Program Office, says in a military news release that previous geothermal exploration efforts indicate the Fallon facility “is ideally suited for enhanced geothermal systems to be deployed onsite.”

As for the Fort Bliss project, Michael Jones, a project director in the Army Office of Energy Initiatives, says it’ll combine geothermal technology with innovations from the oil and gas sector.

“This initiative adds to the momentum of Texas as a leader in the ‘geothermal anywhere’ revolution, leveraging the robust oil and gas industry profile in the state,” says Ken Wisian, associate director of the Environmental Division at the U.S. Bureau of Economic Geology.

The Department of Defense kicked off its geothermal initiative in September 2023. Specifically, the Army, Navy, and Defense Innovation Unit launched four exploratory geothermal projects at three U.S. military installations.

One of the three installations is the Air Force’s Joint Base San Antonio. Canada-based geothermal company Eavor is leading the San Antonio project.

Another geothermal company, Atlanta-based Teverra, was tapped for an exploratory geothermal project at the Army’s Fort Wainwright in Alaska. Teverra maintains an office in Houston.

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This article originally ran on EnergyCapital.