Check out my vitae if you want to find out about the editor. I am a retired economics professor, the former Dalton Chair in Business Economics and Public Policy at Indiana University's Kelley School of Business, Yale BA/MA '80 and MIT PhD '84, previously at UCLA, visiting scholar at Harvard Law, Yale Law, Harvard Economics, Oxford Economics (Nuffield College), Chicago Business, and Tokyo Economics. I am also politically conservative, in case you are unscholarly enough to dislike associating with those of us on the right.
If this takes off, I may decide to pretty up this page, but for now I will leave it in stark, high-information format. THIS IS STILL UNDER CONSTRUCTION AND BETA TESTING.
I might also include articles which have been so denounced as working papers that they have no chance of being published elsewhere. These are more dubious, since they have not gone successfully through refereeing, but their getting cancelled is a sign that they are worth taking seriously.
Retraction. "The author's institution, the University of Pittsburgh Medical Center (UPMC), has notified the Editor‐in‐Chief that the article contains many misconceptions and misquotes and that together those inaccuracies, misstatements, and selective misreading of source materials void the paper of its scientific validity."
See also: "Affirmative action in Medicine : A forbidden debate?" Dr. Anish Koka's Newsletter (April 10, 2023).
"Explosive performance and memory space growth in computing machines, along with recent specialization of deep learning models have radically boosted the role of images in semantic pattern recognition. In the same way that a textual post on social media reveals individual characteristics of its author, facial images may manifest some personality traits. This work is the first milestone in our attempt to infer personality traits from facial images. With this ultimate goal in mind, here we explore a new level of image understanding, inferring criminal tendency from facial images via deep learning. In particular, two deep learning models, including a standard feedforward neural network (SNN) and a convolutional neural network (CNN) are applied to discriminate criminal and non-criminal facial images. Confusion matrix and training and test accuracies are reported for both models, using tenfold cross-validation on a set of 10,000 facial images. The CNN was more consistent than the SNN in learning to reach its best test accuracy, which was 8% higher than the SNN's test accuracy. Next, to explore the classifier's hypothetical bias due to gender, we controlled for gender by applying only male facial images. No meaningful discrepancies in classification accuracies or learning consistencies were observed, suggesting little to no gender bias in the classifier. Finally, dissecting and visualizing convolutional layers in CNN showed that the shape of the face, eyebrows, top of the eye, pupils, nostrils, and lips are taken advantage of by CNN in order to classify the two sets of images."
"The authors have retracted this article because they did not seek approval from their ethics committee before undertaking this study that uses human biometric data. Both authors agree with this retraction."
"During the past decade there has been a dramatic increase in adolescents and young adults (AYA) complaining of gender
dysphoria. One infuential if controversial explanation is that the increase refects a socially contagious syndrome: Rapid Onset
Gender Dysphoria (ROGD). We report results from a survey of parents who contacted the website ParentsofROGDKids.com
because they believed their AYA children had ROGD. Results focused on 1655 AYA children whose gender dysphoria reportedly began between ages 11 and 21 years, inclusive. These youths were disproportionately (75%) natal female. Natal males
had later onset (by 1.9 years) than females, and they were much less likely to have taken steps toward social gender transition
(65.7% for females versus 28.6% for males). Pre-existing mental health issues were common, and youths with these issues
were more likely than those without them to have socially and medically transitioned. Parents reported that they had often felt
pressured by clinicians to afrm their AYA child’s new gender and support their transition. According to the parents, AYA
children’s mental health deteriorated considerably after social transition. We discuss potential biases of survey responses from
this sample and conclude that there is presently no reason to believe that reports of parents who support gender transition are
more accurate than those who oppose transition. To resolve controversies regarding ROGD, it is desirable that future research
includes data provided by both pro- and anti-transition parents, as well as their gender dysphoric AYA children."
Retraction Watch tells the story. See also Michael Bailey's July 10, 2023 article.
"Abolish the #TechToPrisonPipeline: Crime prediction technology reproduces injustices and causes real harm," (Jun 23, 2020); and its signatories.