Francesca Gino Scandal: What Really Happened

Summary

Nudge Podcast: Francesca Gino Scandal – What Really Happened

Host: Phil Agnew
Guest: Joe Simmons & Zoe Zanni
Release Date: January 17, 2025

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Introduction to the Scandal

In this episode of Nudge, host Phil Agnew delves into one of the most tumultuous scandals in academia: the rise and fall of Harvard Business School professor Francesca Gino. The episode uncovers allegations of data fraud, plagiarism, lawsuits, and whistleblower actions, shedding light on the darker aspects of academic research and its broader implications.

Phil Agnew [00:00]: "Today we cover one of the most shocking scandals to hit academia."

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Francesca Gino: Credentials and Reputation

Before the scandal erupted, Francesca Gino was a highly respected tenured professor at Harvard Business School, specializing in behavioral science and organizational psychology. She was renowned for her research on decision-making, creativity, workplace ethics, and even cheating. Gino authored the acclaimed book Rebel: Why It Pays to Break the Rules at Work and in Life, consulted for major corporations like Google and Disney, and was a sought-after speaker.

Joe Simmons [00:59]: "Francesca Gino was a tenured professor at Harvard Business School."

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Discovery of Data Fraud by Zoe Zanni

The unraveling began in 2018 when Zoe Zanni, a PhD student in Paris, scrutinized one of Gino's key papers on networking. She found inconsistencies between the numerical data and the participants' qualitative responses, suggesting possible data fabrication. Zoe approached her PhD supervisors, only to face dismissal and discouragement, prompting her to take further action.

Zoe Zanni [03:18]: "But Zoe refused to be silent."

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Investigation by Data Colada

Facing institutional inertia, Zoe collaborated with Data Colada, a blog dedicated to exposing research misconduct. Run by Uri Simonsson, E.F. Nielsen, and Joe Simmons, Data Colada conducted rigorous statistical analyses, uncovering deliberate data manipulations across multiple Gino studies. Their findings were substantiated through meticulous cross-referencing and validation.

Phil Agnew [04:29]: "Data Colada took these findings with Zoe to Harvard."

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Analysis of Fraudulent Studies

Data Colada identified four pivotal studies authored by Gino that exhibited signs of data fabrication:

1. Sign at the Top Study

Published in 2012, this study examined whether signing an honesty pledge at the beginning versus the end of a form influenced self-reported honesty. Data anomalies included unnatural uniformity in reported mileage data and implausible consistencies, indicating manual tampering.

Joe Simmons [07:45]: "Mileage which showed an unnatural uniformity... was a clear indicator of manipulation."

2. Inauthenticity and Moral Impurity Study

This study linked acting inauthentically to feelings of immorality and impurity. Data irregularities included nonsensical demographic responses and highly consistent impurity ratings that contradicted participants' descriptive words, suggesting deliberate manipulation to support the hypothesis.

Zoe Zanni [09:21]: "These anomalies suggested that the data may have been manipulated to produce the hypothesized effect."

3. Dishonesty and Creativity (Evil Genius) Study

Claiming that dishonesty could enhance creativity, this 2014 paper was scrutinized for its data sorting irregularities. Data Colada found that the significant relationship between cheating and increased creativity disappeared once anomalies were corrected, revealing potential data fabrication.

Joe Simmons [13:50]: "The significant relationship between cheating and creativity disappeared."

4. Moral Consequences of Networking Study

This study explored the moral implications of professional networking under different psychological priming conditions. Unusual score distributions and contradictions between self-rated impurity and descriptive words raised serious doubts about data integrity.

Zoe Zanni [16:22]: "There was a severe discrepancy between the words that they used and their impurity rating."

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Retractions and Harvard's Investigation

Following Data Colada's revelations, Harvard Business School launched an 11-month investigation, culminating in the retraction of all four papers and placing Francesca Gino on administrative leave. The investigation confirmed evidence of data manipulation and fraud, underscoring the severity of the misconduct.

Phil Agnew [05:05]: "Harvard's investigation identified significant misconduct across four papers spanning eight years."

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Plagiarism Allegations

As the scandal unfolded, additional allegations of plagiarism surfaced in Gino's published books. Investigations revealed multiple instances where Gino's text mirrored earlier works without proper attribution, further tarnishing her academic integrity.

Joe Simmons [20:57]: "It demonstrates both direct textual overlaps, structural similarities, and a consistent pattern of plagiarism."

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Lawsuit Against Harvard and Data Colada

In response to the mounting accusations, Gino filed a lawsuit against Harvard Business School and the Data Colada team, accusing them of defamation and reputational harm. She sought $25 million in damages, alleging that the accusations led to emotional distress and significant financial losses.

Joe Simmons [23:40]: "Francesca Gino's lawsuit against Harvard and Data Colada initially sought $25 million in damages."

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Court Decision and Final Verdict

The court dismissed Gino's defamation claims against Data Colada, ruling that their criticisms were protected under the First Amendment. However, her breach of contract claims against Harvard proceeded. This decision reinforced the legitimacy of whistleblowers and the critical role of external watchdogs in maintaining academic integrity.

Joe Simmons [26:10]: "The court dismissed the defamation claims against Data Colada, reinforcing that their research was conducted in good faith."

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Implications for Behavioral Science and Trust in Science

The Francesca Gino scandal has ignited a "believability crisis" in social sciences, prompting increased skepticism towards research findings. Renowned figures like Danny Kahneman have expressed a shift towards greater scrutiny of surprising results. Joe Simmons highlighted the broken incentive structures in academia, advocating for systemic changes to prevent future misconduct.

Zoe Zanni [28:02]: "Harms trust in science."

Phil Agnew [28:07]: "Science earns trust not by claiming authority, but by showcasing self-correction."

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Conclusion: Self-Correction in Science

Despite the immediate erosion of trust, the scandal ultimately underscores the self-correcting nature of scientific inquiry. Whistleblowers like Zoe Zanni and platforms like Data Colada play a crucial role in upholding research integrity. As Joe Simmons eloquently puts it, the ability of science to self-correct fosters long-term trust and progress.

Joe Simmons [28:58]: "Science earns trust not by claiming authority, but by showcasing self-correction."

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Final Reflections

The Francesca Gino case serves as a pivotal lesson in the importance of vigilance, transparency, and accountability within academic research. It highlights the need for robust mechanisms to detect and address misconduct, ensuring the credibility and reliability of scientific endeavors.

Joe Simmons [29:03]: "Failure and setbacks aren't the problem. It's the ability to learn from these mistakes that is important."

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Notable Quotes:

• Phil Agnew [00:00]: "Today we cover one of the most shocking scandals to hit academia."
• Joe Simmons [05:05]: "Harvard's investigation identified significant misconduct across four papers spanning eight years."
• Zoe Zanni [09:21]: "These anomalies suggested that the data may have been manipulated to produce the hypothesized effect."
• Phil Agnew [28:07]: "Science earns trust not by claiming authority, but by showcasing self-correction."
• Joe Simmons [28:58]: "Science earns trust not by claiming authority, but by showcasing self-correction."

---

For a deeper dive into the Francesca Gino scandal and its ramifications on academia and behavioral science, listen to the full episode of Nudge.

Transcript

[Phil Agnew]

Today we cover one of the most shocking scandals to hit academia. The rise and fall of Harvard professor Francesca Gino. From allegations of data fraud and plagiarism to lawsuits and whistleblowers, this story reveals the dark side of academia research and what it means for the future.

[Joe Simmons]

Trust in behavioral science. All of that coming up.

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[Joe Simmons]

So let's start with Francesca Gino.

[Phil Agnew]

Who is Francesca Gino, what are her credentials and what was her reputation like before for this scandal?

[Joe Simmons]

Well, Francesca Gino was a tenured professor at Harvard Business School.

[Phil Agnew]

She specialized in behavioral science and organizational psychology.

[Joe Simmons]

She was really known for her research on decision making, creativity, ethics in the workplace, and also ironically, cheating.

[Phil Agnew]

She was the author of Rebel why.

[Joe Simmons]

It Plays to Break the Rules at.

[Phil Agnew]

Work and in Life, which received at the time very widespread acclaimed.

[Joe Simmons]

And she regularly consulted for organizations like.

[Phil Agnew]

Google, Disney, Walmart and the US Military.

[Joe Simmons]

She was a sought after speaker and consultant. Earlier last year, I recorded an episode.

[Phil Agnew]

On Francesca Gino's work with Richard Shotten. Richard had just released a book called.

[Joe Simmons]

The Illusion of Choice and in it.

[Phil Agnew]

He mentioned her work on the Red.

[Joe Simmons]

Sneakers effects, which was a really interesting research. We recorded an episode on it, published it. But right before I hit published, the news about Francesca Gino broke. And we put a big disclaimer at the start, but you can go back.

[Phil Agnew]

And listen to that episode to get a gist of how important and how.

[Joe Simmons]

Noteworthy her research was at the time. But that was all before the allegations of fraud. And all of this started in around 2018.

[Phil Agnew]

This is really the origin of the case. And in this time, Zoe Zanni, a PhD student, analyzed a key paper by.

[Joe Simmons]

Gino on networking and she discovered these.

[Phil Agnew]

Inconsistencies which suggested data fabrication.

[Joe Simmons]

So Zanny was a PhD student in Paris and she was conducting her dissertation and she encountered this paper which was.

[Phil Agnew]

Written by Francesca Gino, which essentially claimed.

[Joe Simmons]

That networking made people feel dirty. This study was a foundational piece in the field.

[Phil Agnew]

It was highly influential, it was heavily cited.

[Joe Simmons]

But Zoe read this and basically found.

[Phil Agnew]

The conclusion this idea that networking creates these feelings of dirtiness, she found that pretty implausible based on her own understanding of the topic.

[Joe Simmons]

So she accessed the data set used in Gino's study and began to analyze it.

[Phil Agnew]

What she found very quickly was inconsistencies in the results.

[Joe Simmons]

The numerical data and the word based.

[Phil Agnew]

Responses in the dataset didn't align. So, for example, while someone's score might say that they found networking to be very dirty or make them feel dirty.

[Joe Simmons]

The actual words they used to describe networking were quite positive.

[Phil Agnew]

Like saying, networking makes me feel empowered. It appeared to Zoid at some of the numerical data which really found this.

[Joe Simmons]

Result, like networking increased. This feeling of dirtiness may have been.

[Phil Agnew]

Manually altered to produce statistical outcomes.

[Joe Simmons]

The patterns in the data seemed highly irregular and they suggested that they were fabricated.

[Phil Agnew]

Zoe reported these concerns to her PhD supervisors, expecting a bit of guidance and support. But instead they dismissed her concerns and.

[Joe Simmons]

They feared potential repercussions for challenging a prominent academic. They actively discouraged her for pursuing the investigation further and even pressured her to drop it entirely.

[Zoe Zanni]

But Zoe, she refused to be silent.

[Phil Agnew]

She teamed up with an anonymous fellow researcher to dig deeper. And together they identified three additional data sets from other studies authored by Gino that exhibited similar signs of fabrication.

[Joe Simmons]

Realising her university wouldn't act, Zoe contacted.

[Phil Agnew]

Data Colada, a blog specializing in exposing research misconduct.

[Joe Simmons]

Datacollada is run by three academics who.

[Phil Agnew]

Really specialize in these issues and are.

[Joe Simmons]

Fantastic at statistical analysis and figuring out data integrity.

[Phil Agnew]

The researchers are Uri Simonsson, E.F. nielsen and Joe Simmons. These researchers, they verified Zoe's findings through rigorous validation.

[Joe Simmons]

They cross referenced the sources, they looked.

[Phil Agnew]

At the statistical anomalies, they analyzed the.

[Joe Simmons]

Metadata from the Excel files.

[Phil Agnew]

This is something really interesting you can do.

[Joe Simmons]

You can look at the calc change. And that reveals if someone has manually manipulated the data.

[Phil Agnew]

And they concluded after lots of research that Zoe was right. The data sets were likely fabricated and it pointed to deliberate fraud. Specifically, what they found were inconsistencies within the data set.

[Joe Simmons]

So, for example, the numerical and word data would be contradictory, meaning the two.

[Phil Agnew]

Types of data did not tell the same story.

[Joe Simmons]

So somebody saying that networking made them feel empowered, but then giving a score.

[Phil Agnew]

Of 10 out of 10 when asked, how dirty did it make you feel? There were also statistical irregularities. So the P values which are used to determine the significance of results were unusually low across multiple studies, suggesting that manipulation was used to make the result appear more robust than it actually was. And they also found these manipulated Excel files. Like I said, this is where the Excel file has been tampered with.

[Joe Simmons]

And you can see this by looking.

[Phil Agnew]

At the calc chain, which is a file property showing the history of the edits.

[Joe Simmons]

And they found in some cases that.

[Phil Agnew]

Someone had manually altered certain cells in a way that was consistent with deliberate fraud.

[Joe Simmons]

So Datacollada took these findings with Zoe to Harvard. And in 2021, Harvard Business School, who obviously were the employers of Gino, they.

[Phil Agnew]

Conducted a detailed investigation lasting 11 months and ultimately concluded that Gino had engaged in data fraud.

[Joe Simmons]

Harvard's investigation identified significant misconduct across four.

[Phil Agnew]

Papers spanning eight years.

[Joe Simmons]

Findings included that 28% of responses were manually edited to fit a hypothesis that dummy responses had been added to final data sets. And some data actually highlighted in grey showcased this manual manipulation.

[Phil Agnew]

We'll get onto that in a bit. It's kind of a funny aspect of this story.

[Joe Simmons]

Anyway, the dean at Harvard acted on these studies. They placed Genome on administrative leave, and.

[Phil Agnew]

Datacollada published their findings to the world.

[Joe Simmons]

Datacollada's findings were published over a series of three blogs.

[Phil Agnew]

I've linked to their blog in the.

[Joe Simmons]

Show Notes of Today's show along with all of the other sources. I'll go into detail about each of these four papers because it's quite interesting.

[Phil Agnew]

To learn how this manipulation was done. So the first paper was the Sign at the Top study.

[Joe Simmons]

This is a 2012 study titled Signing.

[Phil Agnew]

At the beginning makes ethics salient and decreases dishonest self reports in comparison to signing at the end.

[Joe Simmons]

Now, this is a paper published by Francesca Gino, Dan Ariely, Nina Mazar, who's.

[Phil Agnew]

A previous guest on Nudge, Nax Bazerman and Lisa Xu. The hypothesis is that signing an honesty pledge at the top of the form instead of at the bottom increases honesty in self reports. This is something that I actually talked.

[Joe Simmons]

About on an earlier episode of Nudge. So there's in the study design, there's.

[Phil Agnew]

Basically two parts of the study.

[Joe Simmons]

One is a laboratory study held at the unc and the other was a.

[Phil Agnew]

Field experiment involving an insurance company.

[Joe Simmons]

So in the lab studies, participants solved puzzles and reported their own earnings with.

[Phil Agnew]

Financial incentives for correct answers. Honesty was assessed based on these self.

[Joe Simmons]

Reports, and they had a way of basically figuring out how honest people were.

[Phil Agnew]

In the field study, real world participants.

[Joe Simmons]

In a real insurance program reported their annual mileage after signing honesty pledges at the top or bottom of their form.

[Phil Agnew]

And they saw how honest they were. But fabrication was detected firstly in the field study. So the mileage data from the insurance company supposedly provided by Dan Ariely was clearly fabricated. Datacollada analyzed the distribution of the reportage.

[Joe Simmons]

Mileage which showed an unnatural uniformity between.

[Phil Agnew]

0 and 50,000 miles.

[Joe Simmons]

So basically what this data was showing.

[Phil Agnew]

Was that drivers were uniform in their driving, that there were as many people driving 1,000 miles as were driving 50,000.

[Joe Simmons]

Miles, which is of course wrong. Real world data will form a bell curve with most people driving around the.

[Phil Agnew]

Middle of that range.

[Joe Simmons]

But this histogram was flat and it's a clear indicator of manipulation.

[Phil Agnew]

When datacollada reached out to the insurance.

[Joe Simmons]

Company, that insurance company actually confirmed that their original data had no such effect. They did have a bell curve.

[Phil Agnew]

And that again suggested that this was.

[Joe Simmons]

Manipulated or fabricated data.

[Phil Agnew]

And then there's the lab study data.

[Joe Simmons]

Gino's lab study data contained many irregularities.

[Phil Agnew]

Such as outliers and and implausible consistencies across groups. Evidence of manual tampering was found in the datasets, again by looking at those calc tables. And again the fabricated values aligned just too perfectly with the desired hypothesis. So that's the first study.

[Zoe Zanni]

The second study, how inauthenticity produces feelings.

[Joe Simmons]

Of immorality and impurity.

[Zoe Zanni]

The study basically suggested that when individuals act inauthentically, so contrary to their true selves, they experience feelings of immorality and impurity. And the research suggested a psychological link between authenticity and moral behaviour. Now, the reason this was retracted was due to Harvard Business School's investigation. But in the Data Collada blog titled data falsificada part 2, my class here Is Harvard, the authors and Data Collada start to highlight some of the problems with this paper. So the study involved 491 Harvard students who completed an online survey. The participants were asked to express an opinion on a campus issue and provided demographic information, including their year in the school. Subsequently, they then wrote an essay either supporting their own view or opposing their own view. So they were asked to support the view they had or oppose it. After this essay, the participants rated their desire for five cleansing products on a scale from one which is completely undesirable to seven completely desirable. The author's hypothesis was that arguing against one's own position would induce feelings of impurity and increase that desire for cleaning products. But there were problems with this dataset. Datacollada identified some peculiar responses in the demographic data, particularly in the year in school question. Now, while most students provide a typical answer like I'm a junior or 2016 or third year in school, 20 participants entered Harvard as an answer to their year in school question. This is a nonsensical response to that question, and these 20 entries were clustered within 35 rows in the data set and it suggested potential data t. The analysis revealed that those 20 entries significantly aligned with the study's hypothesis. In the condition where the participants argued against their own position, all 20 respondents with that Harvard answer to the year in school question rated the desirability of cleansing products at a maximum value of 7. In contrast, in the condition where the participants aligned in favour of their own position, these respondents provided low desirability scores. With only one exception, this pattern was highly statistically significant and disproportionately influenced the overall result, and it casted some very, very big doubts on the study's validity. These strange responses strongly aligned with the study's hypothesis and it suggested deliberate data manipulation. It undermined the results and is probably the reason the paper was retracted.

[Joe Simmons]

Now the third paper is titled Evil.

[Zoe Zanni]

Genius How Dishonesty Can Lead to Greater creativity. It's a 2014 paper and it claimed that engaging in dishonest behaviour could enhance creativity. The authors argued that breaking rules through dishonesty might encourage creative thinking by allowing individuals to think outside of conventional boundaries. Now again, this was another paper retracted by Harvard and in Data Colada's blog post, which is titled the Cheaters are Out of Order, we get a good idea of why this paper was retracted. So the study involved 178 participants recruited via Amazon Mechanical Turk. The participants first completed a virtual coin tosk task and this was designed to provide them with an opportunity to cheat. Subsequently, they engaged in a creativity task, specifically generating as many creative uses for a newspaper as possible within one minute. The hypothesis was that the individuals who cheated on the coin tosk task so said they got heads when they actually got tails would exhibit greater creativity as measured by the number of uses for the newspaper generated. The report findings were consistent with the author's hypothesis. The study reported that those who cheated on the coin toss task generated more uses for the newspaper. On average, those who cheated came up with 8.3 uses, whereas those who didn't cheat came up with only 6.5 uses for the newspaper. But there were problems. Datacollada identified peculiarities in the dataset sorting order. The data was almost perfectly sorted by two variables, cheated and number of responses. Among the 135 non cheaters, the data was perfectly sorted by the number of responses. However, among the 43 cheaters, 13 of the observations were out of sequence. This irregularity suggests that the dataset may have been manually altered with after sorting to produce the desired effect by inputting the out of sequence values to fit the expected order. Datacollada found that the significant relationship between cheating and creativity disappeared. This indicates that without the suspected data.

[Joe Simmons]

Manipulation there would have been no significant.

[Zoe Zanni]

Difference between the cheaters and the non cheaters in terms of their creativity. The anomalies in the dataset sorting order and the disappearance of the significant results upon correcting these irregularities suggests more deliberate data manipulation. It undermines the credibility of the findings and raises significant concerns about this research. Now the final paper was titled why Moral Consequences of Networking with a Promotion or Prevention Focus this study examined the moral implications of professional networking. It suggested that those individuals who were primed with a promotion focus so those who wanted to seek advancement in their career might view networking as more morally acceptable compared with those with a prevention focus. Those aiming to avoid losses. Again, this was retracted by Harvard, but datacollada, in their final blog titled Forgetting the Words shed some light on why it might have been retracted. Now this study involved 599 participants who were assigned to one of three a promotion focus. So these were participants who wrote about their hopes and aspirations and were primed to be thinking about promotion. Again, priming is some real problems in behavioural science, so whenever you see a paper about priming, always question it and this is obviously one that you should question as well. Anyway, the first group were primed to be promotion focused. The second group were primed to be prevention focused. They wrote about their duties and their obligations, so this primed them to hopefully avoid losses. And then there was a control group. These were participants who wrote about their usual evening activities and that was designed not to prime them in any way. After the writing task, the participants imagined attending a network event and rated their feelings of moral impurity for example Feeling dirty, tainted or inauthentic on a seven point scale. The findings in this study reported that the participants in the prevention focused condition felt significantly more impure about networking events compared to those in the promotion focused condition. This aligned with the author's hypothesis. However, datacollada noticed some problems. The first was that there was an unusual distribution of scores in the prevention condition. There was an unexp abundance of participants with an average impurity score of exactly 2.0 or 3.0 suggested that many participants rated all seven impurity items uniformly with 2s or 3s. This pattern was not observed in the other conditions and is statistically improbable. There was also a mismatch between the ratings and the descriptive words that participants used. So participants provided words to describe their feelings about the networking event, and this is included within the data. And yet in several cases, there was a severe discrepancy between the words that they used to describe networking and their impurity rating. For example, some participants gave a rating which suggested they did feel dirty, saying 7 out of 7 on the scale saying I feel impure by attending networking events. But then they would use words like empowering or advancement when describing how they actually felt about networking. There was a real contradiction between the ratings they used and the descriptive words. These anomalies suggested that the data may have been manipulated to produce the hypothesized effect. Specifically altering those impurity ratings in the prevention condition to higher values would artificially create the reported differences between the conditions.

[Joe Simmons]

So datacollada had gone through all of.

[Phil Agnew]

These studies and caught the fraud. They used advanced statistical analysis, they cross.

[Joe Simmons]

Referenced with replications, they looked for data sets inconsistencies, and they actually took direct.

[Phil Agnew]

Reports from some of Gino's collaborators.

[Joe Simmons]

So other whistleblowers, other co authors came.

[Phil Agnew]

Out and have flagged concerns about the data handling.

[Joe Simmons]

Now, since Data Colada published these findings, all four of those Gino papers have been retracted.

[Phil Agnew]

Harvard's investigation placed Gino on administrative leave.

[Joe Simmons]

And suspended her without pay. And it's actually had wider implications. It's raised serious questions about the data integrity and accountability in behavioral science and.

[Phil Agnew]

Highlighted the role of external watchdogs like Data Colada.

[Joe Simmons]

And you think the story might end there? You would imagine that Gino would accept these claims, move on, try and clear her name by publishing better research.

[Zoe Zanni]

But that's not what happened.

[Phil Agnew]

After the break, we'll cover the court.

[Joe Simmons]

Case, the additional accusations of plagiarism and the final verdict. All of that coming up.

[Phil Agnew]

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[Zoe Zanni]

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[Phil Agnew]

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[Zoe Zanni]

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[Phil Agnew]

You get your podcasts. So far, datacollada's evidence has suggested that the data in Geno studies has been fabricated. And Harvard's own internal investig litigation has.

[Joe Simmons]

Placed her on leave. But Gino didn't agree.

[Phil Agnew]

She denied the wrongdoing. She's consistently denied the accusations, claiming that she did not commit academic fraud.

[Joe Simmons]

She created a website, Francesca vs. Harvard.

[Phil Agnew]

To present her side of the story. Yet while all this was happening, additional.

[Joe Simmons]

Allegations of plagiarism actually from Francesca Gino's.

[Phil Agnew]

Public books also emerged. And this further undermined her credibility. So these plagiarism allegations, they were identified by Erin Ackland, a Montreal based postdoc. She investigated Gino's work out of curiosity.

[Joe Simmons]

And the first instance of plagiarism was.

[Phil Agnew]

Found in the opening sentence that Erin examined. So the specific example is from Rebel Talent. This is the book Gino wrote. And Gino wrote that rule breaking does.

[Joe Simmons]

Not have to get us into trouble. If done correctly and in the right doses, it can in fact help us get ahead. Erin found that was direct plagiarism from.

[Phil Agnew]

Another site, Science Magazine, then took these.

[Joe Simmons]

Claims and did a major investigation into.

[Phil Agnew]

All of Gino's books. And they found multiple examples of plagiarism. So for example, in Rebel Talent, Gino.

[Joe Simmons]

Gives a description of Milan's fashion quarter. She names different streets luxury boutiques, describes.

[Phil Agnew]

Ivy covered walls and miniature fountains.

[Joe Simmons]

And yet in 2014, four years before the book, there is an identical description.

[Phil Agnew]

Of Milan's fashion district, including the same street names and specific imagery like ivory covered walls on an English language travel website about Milan.

[Joe Simmons]

And then there is Gino and Ariely's.

[Phil Agnew]

Book chapter from 2016 from the book Dishonesty Explained. And that too had evidence of plagiarism. So Gino's text said specifically, and I'll.

[Joe Simmons]

Read it out, compliance with these values.

[Phil Agnew]

And norms results in internal rewards, for example feeling good about yourself, whereas non compliance results in internal punishment, for example.

[Joe Simmons]

Feeling bad about yourself. That's what Gino published in this 2016 book. But in 2014, Jasper Beneveld published his thesis and it said this compliance to.

[Phil Agnew]

These values and norms results in internal.

[Joe Simmons]

Rewards, for example feeling good about yourself, whereas non compliance results in internal punishment, for example feeling bad about yourself. That is direct plagiarism.

[Phil Agnew]

And again, in this example, there was.

[Joe Simmons]

No references to Jasper's work.

[Phil Agnew]

And then there's again another example from Rebel Talent.

[Joe Simmons]

This is the Toy Story 2 story. Now Gino summarizes the Toy Story 2 story with near verbatim language used describing.

[Phil Agnew]

Woody's arm injury, his kidnapping and his meeting with Jesse.

[Joe Simmons]

And Rector magazine in 2017 gave an.

[Phil Agnew]

Almost identical plot summary with the same.

[Joe Simmons]

Key details and phrases like doomed to.

[Phil Agnew]

Spend the rest of their lives separated from children by thick glass. Again, it looks like direct plagiarism.

[Joe Simmons]

There's her book sidetracked in 2013.

[Phil Agnew]

In here she has an example of.

[Joe Simmons]

Ken Olson and the dec. She recounts.

[Phil Agnew]

This story of this company, mentioning its dominance, the rise of Sun Microsystems, and.

[Joe Simmons]

The use of the Unix operating system.

[Phil Agnew]

And yet there is a forbes article.

[Joe Simmons]

From 2011 that provides an almost identical narrative.

[Phil Agnew]

And finally, there's again another example from the book chapter I mentioned in Dishonesty.

[Joe Simmons]

Explained, and it summarizes the Milgram Experiment.

[Phil Agnew]

Gino describes the Milgram experiment, including the progression of shocks, signs of distress, and.

[Joe Simmons]

Conclusion about authority and obedience. And yet a paper from 2008 contains.

[Phil Agnew]

The exact same descriptions and conclusions, using.

[Joe Simmons]

Highly similar language and citing the same references.

[Phil Agnew]

These examples, according to Science and really, anybody who reads that blog article which I've linked to in the show notes well.

[Joe Simmons]

It demonstrates both direct textual overlaps, structural similarities, and a consistent pattern of plagiarism. Now, all of this plagiarism raised additional.

[Phil Agnew]

Questions about the integrity of her larger.

[Joe Simmons]

Body of work, including her wider academic research. It suggests a pattern of taking liberties with facts and content, not citing people properly.

[Phil Agnew]

And it has some contextual irony as well.

[Joe Simmons]

Gino's books were marketed as guides for innovative thinking, creativity, breaking rules. And yet she wasn't following those rules herself. She was copying and plagiarising and not using her own innovative thinking to write her work. Now, while all of these allegations of plagiarism was going on, Francesca Gino filed.

[Phil Agnew]

A lawsuit against Harvard Business School and the three whistleblowers from the blog Data Colada. The lawsuit accused the whistleblowers of defamation.

[Joe Simmons]

And causing reputational harm by alleging fraud in her work.

[Phil Agnew]

And she also challenged Harvard's handling of the allegations against her. Francesca Gino's lawsuit against Harvard and Data.

[Joe Simmons]

Colada initially sought $25 million in damages.

[Phil Agnew]

Specifically, she claimed defamation. She argued that the public blog post.

[Joe Simmons]

Tarnished her standing in academia and beyond. She claimed for emotional distress and financial losses, saying that the accusation had led to emotional harm and substantial economic losses.

[Phil Agnew]

Including the suspension of her tenured position at Harvard Business School, which she claims.

[Joe Simmons]

Cost lost opportunities and diminished her future earnings.

[Phil Agnew]

And she made a discrimination allegation against Harvard.

[Joe Simmons]

Gino argued that Harvard treated her more harshly than other faculty members accused of misconduct, implying potential gender bias in the handling of her case. Now, what's interesting is you can really.

[Phil Agnew]

Clearly see what side the public was.

[Joe Simmons]

On, because while all this was happening.

[Phil Agnew]

A GoFundMe was set up to help Datacollada cover the legal costs of being.

[Joe Simmons]

Sued for $25 million. The campaign received overwhelming support, quickly surpassing its fundraising goal, and quite clearly showed that datacollada was being supported by the public and that the public really wouldn't.

[Phil Agnew]

Want to stand up for these lawsuits.

[Joe Simmons]

Against whistleblowers who are quite clearly trying to do the right thing anyway.

[Phil Agnew]

This case rumbled on for many months. But then in March 2024, before the case was closed, Harvard released their report.

[Joe Simmons]

This was following some legal motions and.

[Phil Agnew]

Pressure from organizations like the New Yorker. And this was Harvard's initial investigation into.

[Joe Simmons]

The allegations of data fraud by Francesca Gino.

[Phil Agnew]

It's 1,300 pages long and it indicated a really comprehensive and thorough investigation. The report essentially confirmed the evidence of data manipulation in Gino's work.

[Joe Simmons]

It highlighted the anomalies and inconsistent data.

[Phil Agnew]

Sets and suspicious P values, including the.

[Joe Simmons]

Signs of manual tampering.

[Phil Agnew]

The things that Data Colada mentioned. Now, in Gino's defense to this, she claimed that she was manipulated by others.

[Joe Simmons]

Even accusing a former co author, which Pete Gido in his fantastic YouTube video on this topic says, maybe Nina Mazar.

[Phil Agnew]

But these defenses were deemed as implausible by Harvard.

[Joe Simmons]

This report further damaged her credibility and.

[Phil Agnew]

Was also praised for its rigor and transparency. It set a precedent for how institutions should handle academic misconduct.

[Joe Simmons]

And finally, in October 11, 2024, a.

[Phil Agnew]

Federal judge dismissed the defamation claims filed.

[Joe Simmons]

By Harvard Business School against Francesca Gino. The court ruled that the criticisms that Data Colada made were protected by the First Amendment, and they emphasised the importance.

[Phil Agnew]

Of open critique in scientific process. However, the judge did allow Gino's breach.

[Joe Simmons]

Of contract claims against Harvard to proceed. So there are still some parts of.

[Phil Agnew]

Her lawsuit which remain active.

[Joe Simmons]

But the court dismissed the defamation claims against Data Colada. And it's really reinforced the view that.

[Phil Agnew]

The Data Colada research has acted in.

[Joe Simmons]

Good faith and well within their rights.

[Phil Agnew]

To critique publicly available research data.

[Joe Simmons]

Gino's reputation has been irreparably damaged. The plagiarism in her books has further discredited her academic integrity. The case has really highlighted the importance.

[Phil Agnew]

Of protecting whistleblowers and maintaining rigorous standards.

[Joe Simmons]

And it's raised some scary questions about.

[Phil Agnew]

How much we can trust science.

[Joe Simmons]

Freakonomics, who did a fantastic episode on their podcast about this scandal, which of.

[Phil Agnew]

Course I've linked to in the show.

[Joe Simmons]

Notes, had some interesting reflections. Max Brazerman, who was one of Gino's.

[Phil Agnew]

Co authors in one of the papers.

[Joe Simmons]

I talked about earlier, he acknowledged that.

[Phil Agnew]

He was a bit naive and trusting colleagues. But he does say that trust in.

[Joe Simmons]

Science is crucial for societal progress.

[Phil Agnew]

That said the episode notes that there's a bit of a believability crisis in social sciences due to this fraud.

[Joe Simmons]

Danny Kahneman was quoted in expressing a shift from believing surprising findings to immediately approaching them with skepticism. Joe Simmons, one of the three Data Colada authors, has described the incentive structure for academia as broken, and he advocates.

[Phil Agnew]

For systematic changes to prevent future data fraud.

[Joe Simmons]

But there's a paradox in all of this. The paradox is that by exposing all these flaws, all these problems with science and peer reviews and and books by prominent researchers, that exposing these things temporarily.

[Zoe Zanni]

Harms trust in science.

[Joe Simmons]

But the paradox is that it ultimately strengthens the field.

[Phil Agnew]

Science earns trust not by claiming authority, but by showcasing self correction.

[Joe Simmons]

And I think that's the point I.

[Phil Agnew]

Want to end on today, which is.

[Joe Simmons]

A is a positive point.

[Phil Agnew]

It's this idea that if science can.

[Joe Simmons]

Self correct itself like it has, we.

[Phil Agnew]

Should look at a scandal like this.

[Joe Simmons]

Not with of fear and as a reason to sort of disregard the whole.

[Phil Agnew]

World of behavioral science, but as instead.

[Joe Simmons]

As a way to trust it. More in his book Nexus Yvel Noah.

[Phil Agnew]

Harari talks about self correcting mechanisms.

[Joe Simmons]

He says self correcting mechanisms are necessary in order for society and civilization to progress. He gives a few examples the economic example like market supply and demand, free.

[Phil Agnew]

Markets are able to adjust imbalances through pricing. When demand exceeds supply, the prices rise, reducing demand and incentivizing more production.

[Joe Simmons]

It's a great example of a of.

[Phil Agnew]

A self correcting mechanism. Democracy is a self correcting mechanism. It allows for self correction through elections, enabling citizens to replace their leaders or policies. In a democratic election, unpopular leaders can.

[Joe Simmons]

Be voted out and this can address the problems, the grievances and governance. And I think this Francesca Gina case.

[Phil Agnew]

Is an example of self correcting. It's a positive example of how whistleblowers can call out misconduct, can call out fraud, and ultimately improve all of our.

[Joe Simmons]

Understandings of social sciences, behavioral science and marketing psychology. Failure and setbacks aren't the problem. It's the ability to learn from these mistakes that is important. And that is it for today's episode.

[Phil Agnew]

Thank you so much for listening.

[Joe Simmons]

Again, this was one of these newer Friday episodes I'm releasing, which are far more conversational.

[Phil Agnew]

They're meant to be quite short, but.

[Joe Simmons]

This episode had a lot to cover.

[Phil Agnew]

So it's a little bit longer than the future Friday ones will be.

[Joe Simmons]

I really hope you enjoyed today's episode. As I mentioned, I've cited quite a lot of different sources and used a lot of different YouTube videos, including Pete Judo's great work and the Freakonomics podcast episode. I've cited all those in the show.

[Phil Agnew]

Notes and I do encourage you to go and listen to those or watch.

[Joe Simmons]

Those if you want to learn more. As always, please do subscribe to my newsletter. I send a newsletter every Friday which correlates the best behavioral science I've found and doesn't include any papers that have been been retracted. And this episode I think will turn.

[Phil Agnew]

Into a YouTube video as well. So if you are interested in watching.

[Joe Simmons]

Nudge on YouTube, do go and search.

[Phil Agnew]

For Nudge podcast on there.

[Joe Simmons]

I do take some of these episodes.

[Phil Agnew]

And add some useful visuals alongside to really tell the story a little bit better. As always, please do subscribe to the Nudge newsletter. Go to nudgepodcast.com and click Newsletter to view that.

[Joe Simmons]

If you want to get in touch, you can reach out to me on LinkedIn. I'm Phil Agnew on there. Or just subscribe to my newsletter and.

[Phil Agnew]

I send you an automated email to.

[Joe Simmons]

Say hello and you can respond to the that. That is all for today folks. I will see you again on Monday for another episode. I really hope you enjoyed today's show.

[Phil Agnew]

Cheers.