2015: Urban Myths about Learning and Education, 1st Edition

Together with Casper Hulshof I wrote a popular book in Dutch on educational myths in 2013.

In 2015 a whole new, updated and upgraded version will be published internationally by Elsevier/Academic Press, written by myself, prof. Paul A. Kirschner and Casper Hulshof.

The manuscript has been sent in and… check here.


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Handy for in the classroom: “Drama in the teenage brain” from Frontiers for Young Minds

The open source Frontiers for Young Minds, with science edited for kids, by kids, is a great initiative. This article on adolescence and the teenage brain by Mills, Goddings & Blakemore is a good example of an article that can be used in the classroom and that can introduce pupils to scientific articles.

A little fragment, but do check the whole article:

“Scientists have begun to link these changes in thinking and behavior to changes occurring in the adolescent brain. We know that the brain is changing both in its function (how it processes information) and its physical structure (or anatomy). A number of studies have explored what is happening in the brain when we try to understand the thoughts, feelings and intentions of others. These studies use a technology called Magnetic Resonance Imaging (or MRI), which allows us to see what is happening in the living human brain. One MRI technique, functional MRI, uses powerful magnetic fields to detect the level of blood flow in the different regions of the brain. Areas activated during an activity need more oxygen to help them work, and this oxygen is transported around the body in red blood cells. Functional MRI measures how much blood is being sent to an area of the brain to determine whether that area is activated during an activity. The signal it measures is call the BOLD signal.

In our lab, we compared BOLD signal when people were reading emotional sentences [3]. Some emotions make you think about someone else’s opinion, e.g. embarrassment and guilt (see Figure 3). For example, you only feel guilty or embarrassed when you understand how someone else might be thinking about you; guilt and embarrassment are therefore social emotions. Other emotions don’t involve thinking about someone else’s opinion, e.g. disgust and fear.”

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Can the language used disclose scientific fraud? The Case of Diederik Stapel

I found this open source article on PLOSOne via @Peter_VP. There has been a lot of talks about scientific fraud and any researcher (and scientific journalist) should follow Retraction Watch. But while stats disclosed the fraud of Diederik Stapel, this paper by Markowitz and Hancock looked if the language being used in the fraudulent papers was any different than in the genuine papers written by Stapel. Therefor they limited their analysis to first-authored papers, in which Stapel was most responsible for the writing, resulting in 24 fraudulent papers producing a corpus of approximately 170,008 words that the researchers compared to a corpus of 25 genuine papers totaling 189,705 words.

What did they find? From their conclusion:

“The most distinct change was Stapel’s use of linguistic dimensions related to scientific writing in his fraudulent work. Stapel overproduced terms related to several important science genre dimensions, including words related to methods and investigation, suggesting that he had difficulty approximating the appropriate frequency of these dimensions when reporting on fake data. Although Stapel overproduced words related to methods and investigation, it was not the case that the fraudulent papers were more descriptive; in fact, he included substantially fewer adjectives in his fraudulent articles. Overall, Stapel used nearly three thousand fewer adjectives in his fake papers than in his genuine papers. This observation is consistent with deception research related to Reality Monitoring [26], [27], which asserts that descriptive recall of real experiences are more sensory and contextually driven, while recall of imagined experiences tend to reflect cognitions, rationalizations, and fewer detailed descriptions about perceptual information [6], [29]. Given that Stapel generally did not just manipulate datasets he collected, but instead fabricated them without ever collecting any information from participants, his descriptions should resemble recall of imagined experiences rather than modifications of real ones.

A second pattern related to the science genre was Stapel’s use of more language to emphasize the importance and relative differences of the results, but fewer words to downplay or hedge empirical findings. In particular, we observed significantly higher rates of linguistic amplifiers that express degrees of difference but lower rates of diminishers that attenuate or equivocate descriptions of results. Stapel also wrote with more certainty when describing his fake data, using nearly one-third more certainty terms than he did in the genuine articles. Words such as “profoundly,” “extremely,” and “considerably” frame the findings as having a substantial and dramatic impact. By describing false data with words that enhanced the results, Stapel presumably attempted to emphasize the novelty and strength of his findings, which ended up being “too good to be true” [9]. This pattern of language is also consistent with other forms of deception that involve persuading readers about quality, such as fake hotel reviews that include too many superlatives relative to real reviews [8].”

UPDATE: via @ionicasmeets I also found this very critical review of this research in Dutch. Basic idea: the researchers knew what they were looking for and found some of the examples, some they didn’t. But they don’t really know why they didn’t.

The abstract (free access):

When scientists report false data, does their writing style reflect their deception? In this study, we investigated the linguistic patterns of fraudulent (N = 24; 170,008 words) and genuine publications (N = 25; 189,705 words) first-authored by social psychologist Diederik Stapel. The analysis revealed that Stapel’s fraudulent papers contained linguistic changes in science-related discourse dimensions, including more terms pertaining to methods, investigation, and certainty than his genuine papers. His writing style also matched patterns in other deceptive language, including fewer adjectives in fraudulent publications relative to genuine publications. Using differences in language dimensions we were able to classify Stapel’s publications with above chance accuracy. Beyond these discourse dimensions, Stapel included fewer co-authors when reporting fake data than genuine data, although other evidentiary claims (e.g., number of references and experiments) did not differ across the two article types. This research supports recent findings that language cues vary systematically with deception, and that deception can be revealed in fraudulent scientific discourse.

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Long-standing mystery of why human children grow so slowly addressed: a story about energy

It takes ages for a child to become an adult, this in contrary to most (other) animals. Why is this? New research has found a possible explanation. The findings support a long-standing hypothesis in anthropology that children grow so slowly, and are dependent for so long, because the human body needs to shunt a huge fraction of its resources to the brain during childhood, leaving little to be devoted to body growth.

From the press release:

The study shows that energy funneled to the brain dominates the human body’s metabolism early in life and is likely the reason why humans grow at a pace more typical of a reptile than a mammal during childhood.

Results of the study will be published the week of Aug. 25 in the journal Proceedings of the National Academy of Sciences.

“Our findings suggest that our bodies can’t afford to grow faster during the toddler and childhood years because a huge quantity of resources is required to fuel the developing human brain,” said Christopher Kuzawa, first author of the study and a professor of anthropology at Northwestern’s Weinberg College of Arts and Sciences. “As humans we have so much to learn, and that learning requires a complex and energy-hungry brain.”

Kuzawa also is a faculty fellow at the Institute for Policy Research at Northwestern.

The study is the first to pool existing PET and MRI brain scan data — which measure glucose uptake and brain volume, respectively — to show that the ages when the brain gobbles the most resources are also the ages when body growth is slowest. At 4 years of age, when this “brain drain” is at its peak and body growth slows to its minimum, the brain burns through resources at a rate equivalent to 66 percent of what the entire body uses at rest.

The findings support a long-standing hypothesis in anthropology that children grow so slowly, and are dependent for so long, because the human body needs to shunt a huge fraction of its resources to the brain during childhood, leaving little to be devoted to body growth. It also helps explain some common observations that many parents may have.

“After a certain age it becomes difficult to guess a toddler or young child’s age by their size,” Kuzawa said. “Instead you have to listen to their speech and watch their behavior. Our study suggests that this is no accident. Body growth grinds nearly to a halt at the ages when brain development is happening at a lightning pace, because the brain is sapping up the available resources.”

It was previously believed that the brain’s resource burden on the body was largest at birth, when the size of the brain relative to the body is greatest. The researchers found instead that the brain maxes out its glucose use at age 5. At age 4 the brain consumes glucose at a rate comparable to 66 percent of the body’s resting metabolic rate (or more than 40 percent of the body’s total energy expenditure).

“The mid-childhood peak in brain costs has to do with the fact that synapses, connections in the brain, max out at this age, when we learn so many of the things we need to know to be successful humans,” Kuzawa said.

“At its peak in childhood, the brain burns through two-thirds of the calories the entire body uses at rest, much more than other primate species,” said William Leonard, co-author of the study. “To compensate for these heavy energy demands of our big brains, children grow more slowly and are less physically active during this age range. Our findings strongly suggest that humans evolved to grow slowly during this time in order to free up fuel for our expensive, busy childhood brains.”

Abstract of the research (free access):

The high energetic costs of human brain development have been hypothesized to explain distinctive human traits, including exceptionally slow and protracted preadult growth. Although widely assumed to constrain life-history evolution, the metabolic requirements of the growing human brain are unknown. We combined previously collected PET and MRI data to calculate the human brain’s glucose use from birth to adulthood, which we compare with body growth rate. We evaluate the strength of brain–body metabolic trade-offs using the ratios of brain glucose uptake to the body’s resting metabolic rate (RMR) and daily energy requirements (DER) expressed in glucose-gram equivalents (glucosermr% and glucoseder%). We find that glucosermr% and glucoseder% do not peak at birth (52.5% and 59.8% of RMR, or 35.4% and 38.7% of DER, for males and females, respectively), when relative brain size is largest, but rather in childhood (66.3% and 65.0% of RMR and 43.3% and 43.8% of DER). Body-weight growth (dw/dt) and both glucosermr% and glucoseder% are strongly, inversely related: soon after birth, increases in brain glucose demand are accompanied by proportionate decreases in dw/dt. Ages of peak brain glucose demand and lowest dw/dtco-occur and subsequent developmental declines in brain metabolism are matched by proportionate increases in dw/dt until puberty. The finding that human brain glucose demands peak during childhood, and evidence that brain metabolism and body growth rate covary inversely across development, support the hypothesis that the high costs of human brain development require compensatory slowing of body growth rate.

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Study states that socioeconomic levels don’t predict longer-term academic success, cognitive skills do

According to a recent study by Dr. Tracy Alloway, assistant professor of psychology at the University of North Florida, not your socioeconomic status doesn’t predict your longer-term academic success, instead, your working memory – your ability to remember and process information – is a much better predictor of learning outcomes. I know a lot of people I respect won’t like this research a bit at first hand and will scrutinize it thoroughly but… bear a moment because it could be again a piece of an interesting puzzle.

From the press release (with some bold by me):

Socioeconomic status has long been linked with school success. The income-achievement gap is evident in kindergarten and accelerates over time, but does where you live affect grades and other cognitive skills?

Students from economically disadvantaged families often achieve lower test scores and are more likely to drop out of school, and cognitive factors like IQ and working memory — our ability to work with information — are also linked to school success.

In the study, Alloway, author of “The Working Memory Advantage,” compared the predictive power of both socioeconomic status and cognitive factors in longer-term school success. The study revealed that socioeconomic levels didn’t predict longer-term academic success, whereas cognitive skills, such as working memory, did.

Alloway recruited approximately 260 British children in kindergarten and tested their learning outcomes two years later. The children were selected from demographically representative schools, using free school meals, a poverty index used in England. The schools represented a range of low (7 to 13 percent), middle (15 to 25 percent) and high (34 to 45 percent) free school-meal rates in each local educational authority.

In kindergarten, children completed a range of cognitive tests, including verbal working memory, short-term memory and sentence memory; nonverbal IQ, like assembling puzzles; and phonological awareness, which is the measure of sensitivity of the phonological structure of words, including the ability to recognize and identify sounds and rhymes. Two years later, the same children took national achievement tests in reading, writing and math.

There were several key findings, including working memory scores predict learning scores two years later, but socioeconomic status didn’t predict learning scores two years later. “This result could possibly be due to other factors, such as educational opportunities, which have a greater influence as the student ages,” said Alloway.

Her study, published this month in the Journal of Experimental Child Psychology, also found that socioeconomic status didn’t affect working memory scores, but it did affect IQ scores. These findings are important because they establish that working memory is one of the most important building blocks for learning.

“A child’s ability to work with information is an important predictor of academic success. Even more exciting is that this important cognitive skill isn’t greatly affected by a child’s socio-economic background, suggesting that such tests measure learning potential, rather than educational opportunity or acquired knowledge,” said Alloway.

Overall, Alloway’s findings from the study indicate that where you live doesn’t have to determine a student’s academic success, and other factors, like working memory, play a more important role.

So the basic idea is that with looking at the working memory could be a better predictor of academic success rather than IQ because it’s less influenced by SES. This fragment of the conclusion is therefor relevant:

“There were two key findings in the current study. The first was the differential effect of SES on cognitive skills. Whereas SES levels were linked to disparate performance levels in tests that draw on long-term knowledge (IQ, phonological awareness, and sentence memory), there was no difference in working memoryperformance. The second finding indicated that SES levels did not predict longer term academic success, whereas cognitive skills such as working memory and phonological awareness did.”

Abstract of the research:

Learning outcomes are associated with a variety of environmental and cognitive factors, and the aim of the current study was to compare the predictive power of these factors in longitudinal outcomes. We recruited children in kindergarten and tested their learning outcomes 2 years later. In kindergarten, children completed tests of IQ, phonological awareness, and memory (sentence memory, short-term memory, and working memory). After 2 years, they took national assessments in reading, writing, and math. Working memory performance was not affected by socioeconomic status (SES), whereas IQ, phonological awareness, and sentence memory scores differed as a function of SES. A series of hierarchical regression analyses indicated that working memory and phonological awareness were better predictors of learning than any other factors tested, including SES. Educational implications include providing intervention during the early years to boost working memory and phonological awareness so as to prevent subsequent learning difficulties.

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Pupils with ADHD often make poorer decisions than their unaffected classmates. (study)

Still I hear the urban story that the person who ‘discovered’ stated that ADHD is a fictitious disease, this is not really what he meant. Despite ADHD is probably overdiagnosed (what he asserted), ADHD does exist. Pupils with ADHD often make poorer decisions than their unaffected classmates. Researchers have now discovered that different learning and decision-making mechanisms are responsible for these behaviors, and localized the underlying impairments in the brain.

From the press release:

Which shirt do we put on in the morning? Do we drive to work or take the train? From which takeaway joint do we want to buy lunch? We make hundreds of different decisions every day. Even if these often only have a minimal impact, it is extremely important for our long-term personal development to make decisions that are as optimal as possible. People with ADHD often find this difficult, however. They are known to make impulsive decisions, often choosing options which bring a prompt but smaller reward instead of making a choice that yields a greater reward later on down the line. Researchers from the University Clinics for Child and Adolescent Psychiatry, University of Zurich, now reveal that different decision-making processes are responsible for such suboptimal choices and that these take place in the middle of the frontal lobe.

Mathematical models help to understand the decision-making processes

In the study, the decision-making processes in 40 young people with and without ADHD were examined. Lying in a functional magnetic resonance imaging scanner to record the brain activity, the participants played a game where they had to learn which of two images carried more frequent rewards. In order to understand the impaired mechanisms of participants with ADHD better, learning algorithms which originally stemmed from the field of artificial intelligence were used to evaluate the data. These mathematical models help to understand the precise learning and decision-making mechanisms better. “We were able to demonstrate that young people with ADHD do not inherently have difficulties in learning new information; instead, they evidently use less differentiated learning patterns, which is presumably why sub-optimal decisions are often made,” says first author Tobias Hauser.

Multimodal imaging affords glimpses inside the brain

In order to study the brain processes that triggered these impairments, the authors used multimodal imaging methods, where the participants were examined using a combined measurement of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to record the electrical activity and the blood flow in the brain. It became apparent that participants with ADHD exhibit an altered functioning in the medial prefrontal cortex — a region in the middle of the frontal lobe. This part of the brain is heavily involved in decision-making processes, especially if you have to choose between several options, and in learning from errors. Although a change in activity in this region was already discovered in other contexts for ADHD, the Zurich researchers were now also able to pinpoint the precise moment of this impairment, which already occurred less than half a second after a feedback, i.e. at a very early stage.

Psychologist Tobias Hauser, who is now researching at the Wellcome Trust Centre for Neuroimaging, University College London, is convinced that the results fundamentally improve our understanding of the mechanisms of impaired decision-making behavior in people with ADHD. The next step will be to study the brain messenger substances. “If our findings are confirmed, they will provide key clues as to how we might be able to design therapeutic interventions in future,” explains Hauser.

Abstract of the research:

Importance  Attention-deficit/hyperactivity disorder (ADHD) has been associated with deficient decision making and learning. Models of ADHD have suggested that these deficits could be caused by impaired reward prediction errors (RPEs). Reward prediction errors are signals that indicate violations of expectations and are known to be encoded by the dopaminergic system. However, the precise learning and decision-making deficits and their neurobiological correlates in ADHD are not well known.

Objective  To determine the impaired decision-making and learning mechanisms in juvenile ADHD using advanced computational models, as well as the related neural RPE processes using multimodal neuroimaging.

Design, Setting, and Participants  Twenty adolescents with ADHD and 20 healthy adolescents serving as controls (aged 12-16 years) were examined using a probabilistic reversal learning task while simultaneous functional magnetic resonance imaging and electroencephalogram were recorded.

Main Outcomes and Measures  Learning and decision making were investigated by contrasting a hierarchical Bayesian model with an advanced reinforcement learning model and by comparing the model parameters. The neural correlates of RPEs were studied in functional magnetic resonance imaging and electroencephalogram.

Results  Adolescents with ADHD showed more simplistic learning as reflected by the reinforcement learning model (exceedance probability, Px = .92) and had increased exploratory behavior compared with healthy controls (mean [SD] decision steepness parameter β: ADHD, 4.83 [2.97]; controls, 6.04 [2.53]; P = .02). The functional magnetic resonance imaging analysis revealed impaired RPE processing in the medial prefrontal cortex during cue as well as during outcome presentation (P < .05, family-wise error correction). The outcome-related impairment in the medial prefrontal cortex could be attributed to deficient processing at 200 to 400 milliseconds after feedback presentation as reflected by reduced feedback-related negativity (ADHD, 0.61 [3.90] μV; controls, −1.68 [2.52] μV; P = .04).

Conclusions and Relevance  The combination of computational modeling of behavior and multimodal neuroimaging revealed that impaired decision making and learning mechanisms in adolescents with ADHD are driven by impaired RPE processing in the medial prefrontal cortex. This novel, combined approach furthers the understanding of the pathomechanisms in ADHD and may advance treatment strategies.


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Funny on Sunday: Recruitment ad of the year

(Ps: no, the link doesn’t work, sadly enough)

Found this via this tweet: https://twitter.com/junayed_/status/502047840563376128

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Interesting read: Why nurture is just as important as nature for understanding genetics

Genes are truly becoming the new ‘brain’ in and outside education, it seems. But while some might think that this is a case for nature in the old debate, Claire Howarth explains this isn’t the case. The influence of genetics on our health and behaviour is not fixed but depends on complex interactions with the environment. 2 fragments to make you read the whole article:

“One of the most striking findings from genetics research is that the influence of genes isn’t fixed. Even though our DNA sequence remains the same, the impact our genes have on us can alter with age and with the different environments we experience. Epigenetics, where the environment can change the expression of a gene without changing DNA, is only a small part of a whole field of science looking at changes in heritability due to interactions between genes and environment.”

“One of the main mechanisms behind the increasing role of genetics as we get older is choice: we have more control overwhat we’re exposed to. We can choose whether to have a doughnut for lunch, whether to visit the library, or whether to cycle to work. These environments don’t just happen to us. To some extent we control, select and create our experiences and exposures. And because our genes can influence these choices too, we find ourselves in places and situations that in a sense draw out our genetic potential.”

And now, read on on Conversation.com.

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Will at Work Learning: Learning Styles Challenge — Year Eight — Now at $5,000

Found this challenge via @J3ro3nJ: Will at Work Learning: Learning Styles Challenge — Year Eight — Now at $5,000.

“As of today, the Learning Styles Challenge payout is rising from $1000 to $5000! That is, if any person or group creates a real-world learning intervention that takes learning styles into account–and proves that such an intervention produces better learning results than a non-learning-styles intervention, they’ll be awarded $5,000!”

Do check some of the earlier posts on Learning Styles:

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Study: Children’s social skills may be declining as digital media use rises (but I do have some doubts)

A quite alarming study, it seems, but I do have some doubts. To understand why, first read this excerpt from the press release:

The psychologists studied two sets of sixth-graders from a Southern California public school: 51 who lived together for five days at the Pali Institute, a nature and science camp about 70 miles east of Los Angeles, and 54 others from the same school. (The group of 54 would attend the camp later, after the study was conducted.)

The camp doesn’t allow students to use electronic devices — a policy that many students found to be challenging for the first couple of days. Most adapted quickly, however, according to camp counselors.

At the beginning and end of the study, both groups of students were evaluated for their ability to recognize other people’s emotions in photos and videos. The students were shown 48 pictures of faces that were happy, sad, angry or scared, and asked to identify their feelings. (note by me, this sound a bit Mehrabian, doesn’t it :) )

They also watched videos of actors interacting with one another and were instructed to describe the characters’ emotions. In one scene, students take a test and submit it to their teacher; one of the students is confident and excited, the other is anxious. In another scene, one student is saddened after being excluded from a conversation.

The children who had been at the camp improved significantly over the five days in their ability to read facial emotions and other nonverbal cues to emotion, compared with the students who continued to use their media devices.

Researchers tracked how many errors the students made when attempting to identify the emotions in the photos and videos. When analyzing the photos, for example, those at the camp made an average of 9.41 errors at the end of the study, down from 14.02 at the beginning. The students who didn’t attend the camp recorded a significantly smaller change. For the videos, the students who went to camp improved significantly, while the scores of the students who did not attend camp showed no change. The findings applied equally to both boys and girls.

You can’t learn nonverbal emotional cues from a screen in the way you can learn it from face-to-face communication,” said lead author Yalda Uhls, a senior researcher with the UCLA’s Children’s Digital Media Center, Los Angeles. “If you’re not practicing face-to-face communication, you could be losing important social skills.”

So, I do have my thoughts about the approach. I understand why they chose the way they did the research (one group of children putting on a strict no-media diet, compared with a non-group), still other elements can play a role. The fact that the children from the test group were together at a camp, living for 5 days with peers is also a huge difference from students who lived just with their parents at the same time. It would have been interesting if there was also a control group of pupils attending camp being allowed to use their technological tools, imho. Also both groups didn’t quite match, actually, if you look at the data. I think this research is interesting to inspire further research, but I wouldn’t say that the findings are carved in stone yet. Social interaction at a 5 day outdoor camp can lead to a better understanding of emotions could have been also a correct title and explanation.

Abstract of the research (open access):

A field experiment examined whether increasing opportunities for face-to-face interaction while eliminating the use of screen-based media and communication tools improved nonverbal emotion–cue recognition in preteens. Fifty-one preteens spent five days at an overnight nature camp where television, computers and mobile phones were not allowed; this group was compared with school-based matched controls (n = 54) that retained usual media practices. Both groups took pre- and post-tests that required participants to infer emotional states from photographs of facial expressions and videotaped scenes with verbal cues removed. Change scores for the two groups were compared using gender, ethnicity, media use, and age as covariates. After five days interacting face-to-face without the use of any screen-based media, preteens’ recognition of nonverbal emotion cues improved significantly more than that of the control group for both facial expressions and videotaped scenes. Implications are that the short-term effects of increased opportunities for social interaction, combined with time away from screen-based media and digital communication tools, improves a preteen’s understanding of nonverbal emotional cues.

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Khan-Academy turns extremely blank slate with #youcanlearnanything

It’s a great message and a great video, this new campaign movie by Khan Academy:

But… is it correct? Well, the point of view that anyone can learn anything is an extreme nurture point of view, while we now for quite a while know it’s not that simple. (I myself have tried to learn German for ages, it just didn’t work.). Do notice that the video doesn’t actually say to what extent everybody can learn anything, but they do suggest that everybody has a potential Einstein inside. Well, even with 10000 hours of practice not everybody will become an expert in any giving field.

Well, that’s just not the case. Our nature-side does have a big influence, not as deterministic as some might think, but the harsh reality is that is does play an important role. And The Blank Slate by Steven Pinker is maybe 12 years old, it’s still a mustread book.

More correct is that everybody should be able to try to learn anything they want, but this just doesn’t sound that sexy, I’m afraid.

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