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Harvard Revokes Tenure of Professor Accused of Data Fraud (in Research on Honesty) – HotAir

Yesterday, Harvard revoked the tenure of a well-known researcher named Francesca Gino who studies ethics and honesty. Ironically, she has been accused of fabricating data. This is the first time Harvard has stripped a professor of tenure in 80 years.





Harvard University revoked the tenure of Harvard Business School professor Francesca Gino after years of data fraud allegations, a university spokesperson confirmed…

A spokesperson for Harvard provided no additional details, noting that it does not discuss personnel matters. Gino also did not immediately respond to a request for comment.

No professors are known to have lost their tenure at Harvard since the 1940s, when the American Association of University Professors formalized rules of termination, according to The Harvard Crimson, the student newspaper.

Professor Gino is fairly famous in her field. She was one of the top earning faculty members at Harvard, making over $1 million per year. Her work frequently attracted media attention. She was a star who had a lucrative side-gig giving talks about ethics to major corporations. 

Gino’s loss of tenure (and with it her job) comes after a two year battle with the school after she was suspended from teaching in 2023. But the real beginning of this story goes back about 13 years to an influential paper about inducing honesty that was cited hundreds of times.

The head-spinning saga began in 2012, when a team of five researchers claimed that three experiments they’d done separately, and combined into one paper, showed that when people signed an honesty pledge at the beginning of a form, versus the end, they were less likely to cheat on the form. This intuitive-sounding conclusion turned heads at government agencies and companies.

But by 2020, it was falling apart. The researchers, plus two others, reported in a new paper that they were unable to replicate the effect after running essentially larger versions of experiments Nos. 1 and 2, which involved university students and employees filling out tax forms in a lab. Max H. Bazerman, a Harvard Business School professor, has said that the two experiments were written up by him, Gino, and Lisa Shu, then of Northwestern University.





Initially it was experiment #3 which came under scrutiny. Data used in that study allegedly came from an insurance company but when the researchers agreed to published some of their data in an effort to prove it was legitimate, it was shown to have been manipulated.

Poring through the Microsoft Excel spreadsheet, Simmons and the other data detectives unearthed a series of implausible anomalies that pointed to at least two kinds of fabrication: Many of the baseline, preexperiment mileages appeared to be duplicated and slightly altered, and all the mileages supposedly collected during the forms test looked like they were made up. Much of this data seemed to be produced by a random number generator, they wrote in their Data Colada post.

The person responsible for providing that data for that experiment, Dan Ariely at Duke University, never admitted any wrongdoing. Duke investigated and concluded the data was bogus but not that he had known about it. He still has his job.

It was only after this first set of bogus data came to light that the same group of investigators began looking closely at experiments #1 and #2. And once again they found even more evidence of data fraud. Professor Bazerman wrote a book which included some details about how he wound up as an author on a fraudulent study which pointed the finger at Francesca Gino.

“In retrospect, Gino reported that her lab manager at her prior university managed data collection for the two laboratory experiments in the 2012 paper,” Bazerman wrote in a chapter about the risks of putting trust in relationships. “Thus, none of the authors, including me, provided sufficient supervision of these experiments. In addition, as I review emails from 2011 containing the dialogue between coauthors of the 2012 paper, I see concerns raised about the methods. I failed to actively engage and deferred to the decisions of my colleagues, and that failure makes me complicit.”

He added, “The irony of this being a story about data fraud in a paper on inducing honesty is not lost on me.”





Meanwhile, Harvard was looking at at least four other papers co-written by professor Gino. In 2023 she was put on administrative leave. She filed a lawsuit against Harvard and the trio of bloggers who had uncovered some of the fraud.

In 2022, HBS launched an 18-month investigation, which ultimately determined that Gino had committed academic misconduct. HBS Dean Srikant M. Datar placed Gino on unpaid administrative leave, barred her from campus, and revoked her named professorship in June 2023…

In July 2023, the University also initiated a formal review of Gino’s tenure at Datar’s request. A month later, Gino filed her $25 million lawsuit, alleging that the University, HBS Dean Srikant M. Datar, and the Data Colada bloggers — Uri Simonsohn, Leif D. Nelson, and Joseph P. Simmons — had conspired to defame her.

The defamation parts of her claim were dropped by a judge but the breach of contract portion appears to be ongoing. Gino continues to protest and maintain her innocence.

Gino launched a website dedicated to the lawsuit, with the last update in March 2024 reading: “Harvard shared their case. And while my lawyers have discouraged me from speaking out, I just need to say that I did not — ever — engage in academic fraud.

“Once I have the opportunity to prove this in the court of law, with the support of experts I was denied through Harvard’s investigation process, you’ll see why their case is so weak and that these are bogus allegations,” the statement continued.

So there you have it. This one very influential study on honesty which made headlines when it was released in 2012 included three separate experiments and uncovered at least two clear instances of data fraud. But only one person has been punished and that took well over a decade.










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