1 00:00:02,560 --> 00:00:06,320 Speaker 1: Welcome back to a Numbers game with Ryan Gerdusky. Happy Monday, everyone. 2 00:00:06,519 --> 00:00:09,160 Speaker 1: I hope you all enjoyed the last weeks of the 3 00:00:09,200 --> 00:00:13,560 Speaker 1: podcast on immigration. Jeffrey Epstein, It's definitely been very interesting 4 00:00:14,320 --> 00:00:18,360 Speaker 1: doing podcasting about politics and elections during a non election 5 00:00:18,520 --> 00:00:21,400 Speaker 1: season in the middle of the summer. There's only so 6 00:00:21,400 --> 00:00:23,279 Speaker 1: many polls about New York City that I could do 7 00:00:23,360 --> 00:00:27,760 Speaker 1: regularly to keep your interest, So I'm trying to expand 8 00:00:27,800 --> 00:00:31,600 Speaker 1: the conversation until we dig deep into election season, which 9 00:00:31,640 --> 00:00:35,240 Speaker 1: is coming up faster than you'd expect. We're headed towards 10 00:00:35,280 --> 00:00:37,199 Speaker 1: the end of July and in the final stretch of 11 00:00:37,240 --> 00:00:39,839 Speaker 1: the summer. I think a lot of people are looking 12 00:00:39,920 --> 00:00:42,120 Speaker 1: at what their summer goals. Where mine was to catch 13 00:00:42,159 --> 00:00:44,400 Speaker 1: up my summer reading. And a lot of times people 14 00:00:44,479 --> 00:00:47,760 Speaker 1: send me books for free from publishers, so I would 15 00:00:48,280 --> 00:00:50,640 Speaker 1: talk with them on my substack or my Twitter or 16 00:00:50,800 --> 00:00:53,600 Speaker 1: write about them or have my podcast. We're headed to 17 00:00:53,640 --> 00:00:55,760 Speaker 1: the end of July in the final stretch to achieve 18 00:00:55,760 --> 00:00:57,600 Speaker 1: your summer goals, and I'm trying to catch them my 19 00:00:57,600 --> 00:01:00,080 Speaker 1: summer reading. A lot of times publishers will send me 20 00:01:00,200 --> 00:01:03,960 Speaker 1: free books to promote on either the podcast or my 21 00:01:04,040 --> 00:01:07,480 Speaker 1: sub stack or my social media, and I spent way 22 00:01:07,520 --> 00:01:10,959 Speaker 1: too much time reading this book called Demon Copperhead this year. 23 00:01:11,040 --> 00:01:14,080 Speaker 1: It was very very good, but it was could have 24 00:01:14,080 --> 00:01:16,280 Speaker 1: been cut by one hundred feet pages at least. It 25 00:01:16,360 --> 00:01:19,160 Speaker 1: was very repetitive. Towards the end, I'm like, I don't 26 00:01:19,200 --> 00:01:22,360 Speaker 1: need this. And then I received a book called Class Matters. 27 00:01:22,480 --> 00:01:24,440 Speaker 1: Now I only read parts of it because I rejected 28 00:01:24,440 --> 00:01:26,800 Speaker 1: the premise entirely, but it was a book about how 29 00:01:27,040 --> 00:01:30,880 Speaker 1: higher education should wait economic class over race as a 30 00:01:31,000 --> 00:01:34,759 Speaker 1: part of the admissions process. The author elaments how some 31 00:01:34,800 --> 00:01:37,200 Speaker 1: schools should take in the top ten percent of all 32 00:01:37,280 --> 00:01:41,000 Speaker 1: high school graduating students, regardless of economic status, and that 33 00:01:41,040 --> 00:01:44,199 Speaker 1: would level the playing field for people who aren't financially off. 34 00:01:44,520 --> 00:01:47,720 Speaker 1: They can't take advantage of early admissions or special classes 35 00:01:47,840 --> 00:01:51,720 Speaker 1: or maybe some sports. That is a very dumb idea, 36 00:01:52,400 --> 00:01:55,200 Speaker 1: because there are plenty of school districts in this country 37 00:01:55,200 --> 00:01:58,760 Speaker 1: where the top ten percent of graduating class is still failing. 38 00:01:59,000 --> 00:02:02,600 Speaker 1: They still don't have meet the curriculum level of the 39 00:02:02,600 --> 00:02:06,760 Speaker 1: academic level in the curriculum of math and reading. You 40 00:02:06,760 --> 00:02:09,400 Speaker 1: wouldn't take them into Harvard just because they make the 41 00:02:09,440 --> 00:02:14,360 Speaker 1: top ten percent merit should be the only way we 42 00:02:14,360 --> 00:02:17,640 Speaker 1: weigh in college and missions. It just makes sense if 43 00:02:17,639 --> 00:02:19,920 Speaker 1: you want to keep American colleges the best in the 44 00:02:19,960 --> 00:02:22,840 Speaker 1: world and make sure that all the students begin at 45 00:02:22,919 --> 00:02:25,440 Speaker 1: equal footing. I mean, some will do better than others 46 00:02:25,480 --> 00:02:28,640 Speaker 1: based upon their skills, but you want to at least 47 00:02:28,639 --> 00:02:31,480 Speaker 1: start on a decent playing ground and not have the 48 00:02:31,560 --> 00:02:34,280 Speaker 1: ability for students who have been brought in for diversity 49 00:02:34,320 --> 00:02:36,680 Speaker 1: to fall so far behind and have to quit college. 50 00:02:36,720 --> 00:02:39,880 Speaker 1: It it hurts. It hurts them more than anybody else. 51 00:02:40,600 --> 00:02:42,600 Speaker 1: So I put the book down after a few chapters 52 00:02:42,639 --> 00:02:44,760 Speaker 1: and I read a story online that has to do 53 00:02:44,800 --> 00:02:46,600 Speaker 1: with merit, and I want to talk to you about it. 54 00:02:47,360 --> 00:02:50,440 Speaker 1: For those listeners from Southern California, you may have already 55 00:02:50,600 --> 00:02:53,040 Speaker 1: been aware of the story. For me, it was the 56 00:02:53,080 --> 00:02:55,440 Speaker 1: first time that I heard of it, and I actually 57 00:02:55,480 --> 00:02:57,520 Speaker 1: it was one of those stories that was so shocking 58 00:02:57,560 --> 00:03:00,720 Speaker 1: I had to do the research on my own because 59 00:03:00,800 --> 00:03:04,400 Speaker 1: I didn't believe it was actually true. It's the story 60 00:03:04,480 --> 00:03:07,240 Speaker 1: of the Southern California hospital called the Martin Luther King 61 00:03:07,320 --> 00:03:11,440 Speaker 1: Junior Drew Medical Center, and from its inception, the whole 62 00:03:11,520 --> 00:03:13,200 Speaker 1: point of it was to be a hospital for the 63 00:03:13,200 --> 00:03:17,520 Speaker 1: black community that hired almost all, if not entirely all 64 00:03:17,639 --> 00:03:20,679 Speaker 1: black people to run it. Black members members of the 65 00:03:20,919 --> 00:03:24,520 Speaker 1: black community to run it, although they weren't overtly involved 66 00:03:24,560 --> 00:03:27,359 Speaker 1: in racial hiring. In nineteen ninety six, the Los Angeles 67 00:03:27,360 --> 00:03:30,480 Speaker 1: County Civil Service Commission upheld a report finding that the 68 00:03:30,600 --> 00:03:35,240 Speaker 1: King Drew MLKA Junior Drew Medical Center had an unwritten 69 00:03:35,240 --> 00:03:38,680 Speaker 1: policy of favoring black candidates for leadership positions to the 70 00:03:38,720 --> 00:03:42,600 Speaker 1: exclusion of non black cabinets. This was highlighted in a 71 00:03:42,640 --> 00:03:45,400 Speaker 1: case involving doctors. And I'm going to mispronounce his name. 72 00:03:45,440 --> 00:03:47,880 Speaker 1: You know, I can't speak when it's difficult, like this 73 00:03:48,160 --> 00:03:53,640 Speaker 1: doctor sub Marian, basal Submarian. That is absolutely not how 74 00:03:53,680 --> 00:03:55,680 Speaker 1: you said that name whatsoever. But go with me on it. 75 00:03:56,000 --> 00:03:59,000 Speaker 1: A non black emergency room physician who was not reinstated 76 00:03:59,040 --> 00:04:03,240 Speaker 1: despite evidence of discrimination. This suggests that historically there was 77 00:04:03,280 --> 00:04:06,400 Speaker 1: a focus on hiring and proning only black staff, though 78 00:04:06,440 --> 00:04:11,600 Speaker 1: this led to accusations of racial discrimination, which you know, 79 00:04:11,800 --> 00:04:17,760 Speaker 1: only goes so far for some people. Now. This because 80 00:04:17,800 --> 00:04:21,040 Speaker 1: it was a basically black only hospital for the black 81 00:04:21,040 --> 00:04:25,200 Speaker 1: community and employed almost only black staff, any criticism of 82 00:04:25,240 --> 00:04:28,320 Speaker 1: the hospital was deemed as racist, so they never acted. 83 00:04:28,720 --> 00:04:32,960 Speaker 1: People in administration and in elected government never acted on 84 00:04:33,040 --> 00:04:36,160 Speaker 1: fixing glarious, obvious problems in the hospital. And I'm going 85 00:04:36,200 --> 00:04:38,039 Speaker 1: to talk about some of them, but i want to 86 00:04:38,080 --> 00:04:40,520 Speaker 1: warn you some of this information is very disturbing and 87 00:04:40,640 --> 00:04:44,280 Speaker 1: very graphic. Within three years of opening, there were major 88 00:04:44,400 --> 00:04:47,240 Speaker 1: issues of incompetence by the staff. Many were showing up 89 00:04:47,320 --> 00:04:49,800 Speaker 1: drunk or high, many weren't showing up at all, and 90 00:04:49,839 --> 00:04:53,800 Speaker 1: the hospital pharmacy was regularly being rated by the people 91 00:04:53,800 --> 00:04:57,600 Speaker 1: who work there. The staff started calling the hospital Killer King. 92 00:04:58,279 --> 00:05:02,039 Speaker 1: That came from the staff. From nineteen ninety nine to 93 00:05:02,080 --> 00:05:04,680 Speaker 1: two thousand and four, the hospital was among the worst 94 00:05:04,680 --> 00:05:08,440 Speaker 1: in California. Two prominent accrediting groups said it was the 95 00:05:08,520 --> 00:05:12,599 Speaker 1: nation's most troubled hospital and received the lowest Postle rating 96 00:05:12,680 --> 00:05:17,040 Speaker 1: in two reviews from the Accreditation Council for Graduate Medical Education, 97 00:05:17,560 --> 00:05:20,279 Speaker 1: and students were no longer being sent there for training 98 00:05:20,320 --> 00:05:25,279 Speaker 1: programs in surgery and radiology. They spent twenty million dollars 99 00:05:25,320 --> 00:05:28,719 Speaker 1: on malpractice lawsuits in just five years, the most of 100 00:05:28,800 --> 00:05:32,799 Speaker 1: any hospital in California. It was so bad that people 101 00:05:32,800 --> 00:05:35,200 Speaker 1: would come in from minor injuries and end up dying. 102 00:05:35,680 --> 00:05:38,440 Speaker 1: Someone bled out in the emergency room for not being 103 00:05:38,520 --> 00:05:42,320 Speaker 1: treated for nearly an hour. The police had an unspoken 104 00:05:42,360 --> 00:05:44,840 Speaker 1: agreement that they would not send their partners there if 105 00:05:44,880 --> 00:05:48,599 Speaker 1: they were shot. Once, a nine year old girl from 106 00:05:48,600 --> 00:05:51,280 Speaker 1: a Guatemalan immigrant family was brought there after she was 107 00:05:51,360 --> 00:05:53,600 Speaker 1: hit by a car. She just had a few broken 108 00:05:53,640 --> 00:05:56,480 Speaker 1: teeths in some minor scrapes. They ended up putting her 109 00:05:56,520 --> 00:05:59,279 Speaker 1: on sedatives meant for a grown man. They put her 110 00:05:59,320 --> 00:06:02,400 Speaker 1: on a ventilator almost no oxygen. They ended up pulling 111 00:06:02,400 --> 00:06:04,360 Speaker 1: it from her without checking to see if she could 112 00:06:04,360 --> 00:06:07,080 Speaker 1: breathe on her own, and she died alone there because 113 00:06:07,120 --> 00:06:09,160 Speaker 1: no one looked at her looked after her for more 114 00:06:09,200 --> 00:06:12,040 Speaker 1: than a half an hour. Another time, a woman came 115 00:06:12,080 --> 00:06:14,960 Speaker 1: in for a hysterectomy and they infused her blood that 116 00:06:15,120 --> 00:06:18,440 Speaker 1: was positive for the AIDS virus. Another woman died in 117 00:06:18,440 --> 00:06:21,560 Speaker 1: the waiting room because nurses ignored her after she suffered 118 00:06:21,560 --> 00:06:24,680 Speaker 1: a perforated bow. She had been in the hospital six 119 00:06:24,800 --> 00:06:29,200 Speaker 1: previous times and the doctors had misdiagnosed her with gallstones. 120 00:06:29,680 --> 00:06:32,479 Speaker 1: It became so bad another patient called nine one one 121 00:06:32,640 --> 00:06:35,400 Speaker 1: talking about talking about that woman, and they refused to 122 00:06:35,440 --> 00:06:38,159 Speaker 1: take her because she was already in an er She 123 00:06:38,440 --> 00:06:40,920 Speaker 1: died there in the middle of the waiting room while 124 00:06:40,960 --> 00:06:44,560 Speaker 1: the cameras rolled watching her. A fifty three year woman 125 00:06:44,600 --> 00:06:46,800 Speaker 1: named Yolanda Bell died in two thousand and four after 126 00:06:46,880 --> 00:06:50,480 Speaker 1: receiving an overdose of blood thinners due to a nurse's 127 00:06:50,600 --> 00:06:53,240 Speaker 1: error in administering the drug. The nurse had turned out 128 00:06:53,240 --> 00:06:56,320 Speaker 1: failed to follow proper dosage protocols, and the error was 129 00:06:56,440 --> 00:06:59,640 Speaker 1: went unnoticed by the supervisors. In two thousand and four, 130 00:07:00,680 --> 00:07:04,640 Speaker 1: five patients died inside the hospital from MRSA due to 131 00:07:04,680 --> 00:07:08,080 Speaker 1: poor sterilization practices. Another time, a nurse turned off a 132 00:07:08,080 --> 00:07:10,800 Speaker 1: twenty eight year old's alarm for his vitals and reported 133 00:07:10,840 --> 00:07:13,960 Speaker 1: in medical charts that he was fine for hours after 134 00:07:14,000 --> 00:07:17,040 Speaker 1: he already died. Because they were so determined to make 135 00:07:17,080 --> 00:07:20,720 Speaker 1: sure the staff remained mostly black, many employees falsified records, 136 00:07:20,800 --> 00:07:24,600 Speaker 1: lacked medical licenses, and had criminal backgrounds. In two thousand 137 00:07:24,600 --> 00:07:26,360 Speaker 1: and four, a nurses aid was found to have a 138 00:07:26,360 --> 00:07:30,440 Speaker 1: prior felony conviction and lacked any certification, yet was allowed 139 00:07:30,480 --> 00:07:34,000 Speaker 1: to provide direct patient care. The aids were linked to 140 00:07:34,120 --> 00:07:37,160 Speaker 1: cases of patients suffering from injuries due to their improper handling. 141 00:07:37,840 --> 00:07:42,120 Speaker 1: Nurses were asking Janner's assistances to mix up IV medication. 142 00:07:42,920 --> 00:07:45,400 Speaker 1: Another time, a hospital hired a man with a long 143 00:07:45,520 --> 00:07:48,720 Speaker 1: history of academic and medical problems, and even though they 144 00:07:48,760 --> 00:07:52,440 Speaker 1: eventually fired him, he used his old medical idea to 145 00:07:52,440 --> 00:07:57,360 Speaker 1: gain access to the hospital instead of videotaping equipment on patients. Eventually, 146 00:07:57,400 --> 00:08:01,840 Speaker 1: he was arrested for a hide and police found one 147 00:08:01,960 --> 00:08:06,720 Speaker 1: hundred and forty tapes of him taping mail patients being 148 00:08:06,960 --> 00:08:10,280 Speaker 1: nude in the hospital. Some patients even reported the employees 149 00:08:10,320 --> 00:08:14,600 Speaker 1: were selling bootleg DVDs and peanuts in the hallways. The 150 00:08:14,680 --> 00:08:18,560 Speaker 1: lack of proper credentialing was a major factor in the 151 00:08:18,600 --> 00:08:21,800 Speaker 1: hospital's failure to pass federal inspections, and it resulted in 152 00:08:21,840 --> 00:08:25,000 Speaker 1: losing two hundred million dollars in federal funding in two 153 00:08:25,080 --> 00:08:28,760 Speaker 1: thousand and six. The issue of funding became a major 154 00:08:29,360 --> 00:08:32,240 Speaker 1: topic for activists, who constantly insisted that the hospital was 155 00:08:32,480 --> 00:08:35,480 Speaker 1: just underfunded. It was just about money. You see the 156 00:08:35,520 --> 00:08:38,800 Speaker 1: same cycle in the education field where they say these 157 00:08:38,800 --> 00:08:42,480 Speaker 1: failing schools in Chicago and Baltimore, they're just underfunded. But 158 00:08:42,520 --> 00:08:45,920 Speaker 1: in fact, the Killer King Hospital was the most well 159 00:08:45,960 --> 00:08:49,200 Speaker 1: funded in Los Angeles County per patient. So where did 160 00:08:49,240 --> 00:08:53,199 Speaker 1: the money go? They spent millions paying disabilities for employees. 161 00:08:53,240 --> 00:08:55,720 Speaker 1: Between nineteen ninety four and two thousand and four, there 162 00:08:55,720 --> 00:08:58,480 Speaker 1: were one hundred and twenty two employees who sued for 163 00:08:58,640 --> 00:09:02,079 Speaker 1: falling out of chairs. They paid three point two million 164 00:09:02,160 --> 00:09:06,319 Speaker 1: dollars on those claims. Another time, a cashier hadn't asked 165 00:09:06,320 --> 00:09:09,040 Speaker 1: her supervisor we have bridesmaid at her wedding, causing the 166 00:09:09,080 --> 00:09:11,440 Speaker 1: two to fight. Well, the cashier said had caused her 167 00:09:11,440 --> 00:09:13,120 Speaker 1: so much tress the hospital had to pay her two 168 00:09:13,280 --> 00:09:17,760 Speaker 1: hundred and sixteen thousand dollars in disability. Doctors were overcharging 169 00:09:17,840 --> 00:09:21,480 Speaker 1: the hospital. One neuroscientists made five hundred thousand dollars per 170 00:09:21,559 --> 00:09:24,760 Speaker 1: year and logged in twenty six working hours when he'd 171 00:09:24,760 --> 00:09:27,760 Speaker 1: only spent six in the hospital. Overall, the hospital spent 172 00:09:27,880 --> 00:09:30,600 Speaker 1: thirty four million dollars over a five year period on 173 00:09:30,640 --> 00:09:33,959 Speaker 1: employee injuries, with many employees habitually not showing up for 174 00:09:34,040 --> 00:09:37,720 Speaker 1: weeks or even months each year. When public officials raise 175 00:09:37,760 --> 00:09:40,760 Speaker 1: these concerned about the hospital, figures like Jesse Jackson and 176 00:09:40,800 --> 00:09:43,880 Speaker 1: Maxine Waters to cry that everyone was just racist. They 177 00:09:43,880 --> 00:09:47,040 Speaker 1: were constantly being racist towards the hospital for servicing and 178 00:09:47,120 --> 00:09:50,880 Speaker 1: hiring black people. Finally, the La Times reported what was 179 00:09:50,880 --> 00:09:52,520 Speaker 1: going on in the hospital in two thousand and four, 180 00:09:52,559 --> 00:09:55,120 Speaker 1: they wont to polsee her prize for doing so, and 181 00:09:55,160 --> 00:09:58,120 Speaker 1: the hospital closed in two thousand and seven. But it 182 00:09:58,160 --> 00:10:03,800 Speaker 1: gets better. Twenty after the George Floyd riots, the LA 183 00:10:03,920 --> 00:10:07,400 Speaker 1: Times apologize for their reporting that close that hospital down. 184 00:10:08,440 --> 00:10:12,160 Speaker 1: This is why merit matters. People should only be hired 185 00:10:12,200 --> 00:10:14,600 Speaker 1: for jobs or admitted for schools if they have the 186 00:10:14,679 --> 00:10:17,920 Speaker 1: knowledge and skills, not because they're wealthy or they're poor, 187 00:10:18,200 --> 00:10:21,800 Speaker 1: or have certain sexual preferences or different anatomies or skin colors. 188 00:10:22,320 --> 00:10:25,200 Speaker 1: But sadly, since twenty twelve and the death of George Floyd, 189 00:10:25,320 --> 00:10:28,720 Speaker 1: many medical institutions have watered down merit as a condition 190 00:10:29,120 --> 00:10:33,280 Speaker 1: getting their school more diverse. If anything, we're going to 191 00:10:33,280 --> 00:10:36,400 Speaker 1: have more hospitals like Killer King in the future. Despite 192 00:10:36,440 --> 00:10:39,280 Speaker 1: many companies backing away from DEI practices that were so 193 00:10:39,400 --> 00:10:43,680 Speaker 1: obviously bad for businesses, medical institutions have not done the same. 194 00:10:44,440 --> 00:10:47,000 Speaker 1: My next guest has written about this phenomena in a 195 00:10:47,080 --> 00:10:50,080 Speaker 1: nauseum about how medical institutions in the medical field is 196 00:10:50,200 --> 00:10:52,960 Speaker 1: changing for the worst because of the need to promote 197 00:10:52,960 --> 00:10:56,000 Speaker 1: diversity at all costs. You won't want to miss this 198 00:10:56,040 --> 00:11:02,240 Speaker 1: interview coming up next. Heather MacDonald is a fellow at 199 00:11:02,280 --> 00:11:04,840 Speaker 1: the Manhattan Institute and the author of many great books, 200 00:11:04,840 --> 00:11:07,320 Speaker 1: including When Race Trump's a Merit. Heather, Thank you for 201 00:11:07,360 --> 00:11:07,840 Speaker 1: being here. 202 00:11:08,040 --> 00:11:09,320 Speaker 2: It's a pleasure, Ryan, Thank you. 203 00:11:09,520 --> 00:11:12,680 Speaker 1: Heather. Tell me about how the death of George Floyd 204 00:11:12,920 --> 00:11:16,719 Speaker 1: and the following racial wreckoning really changed the way our 205 00:11:16,760 --> 00:11:19,000 Speaker 1: medical schools admitted applicants. 206 00:11:19,440 --> 00:11:22,880 Speaker 3: Well, the medical profession went through the usual mass psychosis 207 00:11:22,920 --> 00:11:26,000 Speaker 3: that every elite profession went through after the George Floyd 208 00:11:26,080 --> 00:11:32,600 Speaker 3: race riots, and every medical association declared itself guilty of 209 00:11:32,679 --> 00:11:38,120 Speaker 3: and responsible for white racism, and concluded that medical standards, 210 00:11:38,280 --> 00:11:43,600 Speaker 3: in medical school admissions, in medical licensing, anything that produced 211 00:11:43,720 --> 00:11:50,079 Speaker 3: any lack of absolute proportional representation among black medical students 212 00:11:50,120 --> 00:11:54,839 Speaker 3: among black doctors must be the product of systemic racism. 213 00:11:55,559 --> 00:12:02,080 Speaker 3: So what the already existing set of double standards throughout 214 00:12:02,120 --> 00:12:02,960 Speaker 3: medical education. 215 00:12:03,080 --> 00:12:06,520 Speaker 2: It was already bad. It got even worse, Ryan, And. 216 00:12:06,440 --> 00:12:11,200 Speaker 3: So schools started setting aside MCAT requirements for blacks, started 217 00:12:11,240 --> 00:12:17,360 Speaker 3: admitting black college students with grotesquely lower MCAT scores than 218 00:12:17,400 --> 00:12:21,640 Speaker 3: their wide and Asian peers. And that pressure to set 219 00:12:21,679 --> 00:12:25,040 Speaker 3: aside standards has continued throughout the. 220 00:12:25,000 --> 00:12:26,599 Speaker 2: Medical licensing process. 221 00:12:26,920 --> 00:12:35,000 Speaker 3: It occurs throughout medical hiring for faculty within hospitals, deciding 222 00:12:35,040 --> 00:12:38,040 Speaker 3: who gets to be deans of medical schools. There is 223 00:12:38,240 --> 00:12:43,560 Speaker 3: absolutely no priority placed any longer on color blind merit. 224 00:12:43,640 --> 00:12:46,400 Speaker 3: It's all about achieving racial proportionality. 225 00:12:46,600 --> 00:12:49,719 Speaker 1: Now, none of that has changed the Harvard case, not so. 226 00:12:49,840 --> 00:12:51,640 Speaker 2: Far, not that I'm aware of. 227 00:12:51,760 --> 00:12:57,280 Speaker 3: These schools are deeply committed to the idea that science 228 00:12:57,600 --> 00:13:03,480 Speaker 3: and medicine in particular is a enterprise. And you also 229 00:13:03,640 --> 00:13:08,880 Speaker 3: have professional organizations declaring that science is racist. 230 00:13:09,559 --> 00:13:12,280 Speaker 2: And the medical curriculum is being changed. 231 00:13:12,880 --> 00:13:16,319 Speaker 3: Over fifty percent of the top fifty medical schools now 232 00:13:16,400 --> 00:13:20,720 Speaker 3: require a course in structural racism. Well, of course, learning 233 00:13:20,920 --> 00:13:26,040 Speaker 3: time is zero sum. Every hour that medical students spend 234 00:13:26,200 --> 00:13:31,600 Speaker 3: on the phony racism of intersectionality is an hour spent 235 00:13:31,760 --> 00:13:34,400 Speaker 3: not learning how to save a life that comes through 236 00:13:34,760 --> 00:13:38,680 Speaker 3: the emergency room door after being battered in a near 237 00:13:38,760 --> 00:13:40,000 Speaker 3: fatal car crash. 238 00:13:40,160 --> 00:13:44,319 Speaker 1: Yeah, and there's been many times for decades where the 239 00:13:44,360 --> 00:13:50,760 Speaker 1: idea that Black Americans have lower standards of health, lower 240 00:13:50,800 --> 00:13:56,120 Speaker 1: life expectancy, higher coren abilities, that is inherently racist. In 241 00:13:56,120 --> 00:13:59,800 Speaker 1: my monologue, I talk about the MLK Hospital in California, 242 00:14:00,280 --> 00:14:04,640 Speaker 1: which was had an endemic series of preventable deaths because 243 00:14:04,679 --> 00:14:09,000 Speaker 1: they were hiring people without a medical license. In many cases, 244 00:14:11,120 --> 00:14:14,760 Speaker 1: what is the I mean we saw in the case 245 00:14:14,760 --> 00:14:19,680 Speaker 1: of the MLK hospital it was horrific ramifications. Is that 246 00:14:19,800 --> 00:14:25,000 Speaker 1: kind of level of a medical maulpractice for lack of 247 00:14:25,040 --> 00:14:27,280 Speaker 1: a better term, but is that kind of Is that 248 00:14:27,320 --> 00:14:30,200 Speaker 1: going to be seen at more hospitals, more medical professions 249 00:14:30,280 --> 00:14:33,120 Speaker 1: under this type of DEI standards when it comes to 250 00:14:33,200 --> 00:14:37,240 Speaker 1: admitting students come doctors well quite appropriately. 251 00:14:37,360 --> 00:14:41,560 Speaker 3: The more case of all or cases in the field 252 00:14:41,680 --> 00:14:46,080 Speaker 3: of racial preferences generally arose out of a medical school 253 00:14:47,240 --> 00:14:50,200 Speaker 3: racial quota back in the seventies, and that was Baki 254 00:14:50,280 --> 00:14:53,800 Speaker 3: versus Regions of California. A white medical student who had 255 00:14:53,920 --> 00:14:59,480 Speaker 3: very good GPA and MCAT scores was not admitted to 256 00:14:59,560 --> 00:15:03,400 Speaker 3: the Universe of California Davis Medical School, and he found 257 00:15:03,400 --> 00:15:06,760 Speaker 3: out that the UC Davis Medical School had set asides 258 00:15:07,120 --> 00:15:14,200 Speaker 3: for black students with woefully low qualifications, and he appealed 259 00:15:14,200 --> 00:15:17,320 Speaker 3: this and said his civil rights constitutional rights were being 260 00:15:17,400 --> 00:15:20,600 Speaker 3: violated because he was being penalized for the color of 261 00:15:20,600 --> 00:15:25,520 Speaker 3: his skin, and that led to the awful Supreme Court 262 00:15:25,600 --> 00:15:29,200 Speaker 3: decision by Justice Powell that said well, you can have 263 00:15:29,560 --> 00:15:33,360 Speaker 3: racial considerations in college admissions and medical school admissions if 264 00:15:33,360 --> 00:15:36,360 Speaker 3: it's in the name of student diversity. Famously, one of 265 00:15:36,400 --> 00:15:41,840 Speaker 3: the black students that was admitted when Baki was not 266 00:15:42,400 --> 00:15:47,000 Speaker 3: went on as a practicing obstetrician and generated an absolutely 267 00:15:47,040 --> 00:15:51,560 Speaker 3: horrific record of medical mal practice. This is of course 268 00:15:52,200 --> 00:15:57,800 Speaker 3: a nearly taboo topic after George Floyd and even before 269 00:15:58,120 --> 00:16:03,200 Speaker 3: it became a professional suicide to study any reason for 270 00:16:03,320 --> 00:16:07,520 Speaker 3: racial disparities and outcomes other than racism. So it was 271 00:16:07,600 --> 00:16:11,640 Speaker 3: once possible to study black and white driving habits in 272 00:16:11,760 --> 00:16:15,920 Speaker 3: order to explain why, on dark highways, when everybody's going 273 00:16:16,040 --> 00:16:19,840 Speaker 3: seventy miles an hour and the police officer cannot possibly 274 00:16:19,920 --> 00:16:23,320 Speaker 3: see the race of a driver, why blacks are nevertheless 275 00:16:23,320 --> 00:16:28,120 Speaker 3: stopped at higher rates. It was once possible to study driving, 276 00:16:28,280 --> 00:16:32,400 Speaker 3: and the two studies that got through the racism screen 277 00:16:32,880 --> 00:16:36,440 Speaker 3: found that blacks speed at twice the rate of wides, 278 00:16:36,440 --> 00:16:39,680 Speaker 3: and it speeds over ninety miles per hour even higher. Still, 279 00:16:39,920 --> 00:16:44,720 Speaker 3: it was once possible to study outcomes medical malpractice outcomes 280 00:16:45,360 --> 00:16:51,360 Speaker 3: and found that those were correlated with diversity variables. But 281 00:16:51,440 --> 00:16:55,120 Speaker 3: that's now of course completely off limits. So it's going 282 00:16:55,200 --> 00:16:58,360 Speaker 3: to be hard to get this data. Schools refuse to 283 00:16:58,440 --> 00:17:02,400 Speaker 3: collect it. They refuse to look at the outcomes of 284 00:17:02,560 --> 00:17:07,960 Speaker 3: students that they've admitted with a standard deviation below White 285 00:17:07,960 --> 00:17:12,600 Speaker 3: and Asian students in MCAT scores that have been licensed 286 00:17:12,640 --> 00:17:17,000 Speaker 3: with the standard deviation below of licensing scores. They refuse 287 00:17:17,119 --> 00:17:19,840 Speaker 3: to see how are those students. Are they actually ending 288 00:17:19,920 --> 00:17:24,560 Speaker 3: up practicing? Are they what is their medical malpackage. We're 289 00:17:24,600 --> 00:17:26,760 Speaker 3: just not going to be allowed that data, but for 290 00:17:26,920 --> 00:17:30,400 Speaker 3: certain it will happen unless we're nihilistic enough to think 291 00:17:30,440 --> 00:17:35,800 Speaker 3: that all standards of achievement and accomplishment are totally arbitrary. 292 00:17:36,160 --> 00:17:38,400 Speaker 2: There's got to be a reason. Let me give you. 293 00:17:38,400 --> 00:17:41,440 Speaker 3: You know, I've been speaking in general terms, Ryan about 294 00:17:41,480 --> 00:17:43,880 Speaker 3: the extent of these test score gaps. 295 00:17:44,160 --> 00:17:45,600 Speaker 2: Let me give you an example. 296 00:17:46,600 --> 00:17:50,760 Speaker 3: If you look at white MCAT scores, the average MCAT 297 00:17:50,920 --> 00:17:54,680 Speaker 3: score for whites is at the seventy first percentile. That's 298 00:17:54,720 --> 00:17:58,440 Speaker 3: a technical term that merely means that the average score 299 00:17:58,520 --> 00:18:03,080 Speaker 3: for whites is better than seventy one percent of all 300 00:18:03,200 --> 00:18:05,399 Speaker 3: other white test takers. 301 00:18:05,440 --> 00:18:08,760 Speaker 2: The average black m CAT. 302 00:18:08,560 --> 00:18:12,840 Speaker 3: Score is at the thirty fifth percentile, meaning that it 303 00:18:12,920 --> 00:18:18,800 Speaker 3: is worth five percent of all other of other test scores. 304 00:18:18,880 --> 00:18:21,560 Speaker 2: Let me excuse me. For whites, it's better than all 305 00:18:21,680 --> 00:18:22,680 Speaker 2: other test scores. 306 00:18:22,960 --> 00:18:26,720 Speaker 3: For blacks, it means that there it's better than only thirty. 307 00:18:26,440 --> 00:18:30,480 Speaker 2: Four percent of other m CAT takers. 308 00:18:30,920 --> 00:18:37,320 Speaker 3: And yet, nevertheless, a set of mediocre m cats and 309 00:18:37,480 --> 00:18:41,800 Speaker 3: GPAs that would for a white student would be virtually 310 00:18:41,960 --> 00:18:48,080 Speaker 3: automatically disqualifying. If you presented this profile of low m cats, 311 00:18:48,600 --> 00:18:52,080 Speaker 3: low GPA, a white student would have only an eight 312 00:18:52,160 --> 00:18:56,200 Speaker 3: percent chance of getting admitted to medical schools. So basically, 313 00:18:56,240 --> 00:18:59,679 Speaker 3: forget about it, you're too you're too unqualified. 314 00:18:59,000 --> 00:18:59,320 Speaker 2: To get in. 315 00:18:59,680 --> 00:19:05,000 Speaker 3: If a black student presents the identical rock bottom m 316 00:19:05,080 --> 00:19:09,600 Speaker 3: cats and GPAs, he faces a fifty six percent chance 317 00:19:09,680 --> 00:19:13,800 Speaker 3: of being admitted. A black student with those low m 318 00:19:13,880 --> 00:19:16,960 Speaker 3: cats has a seven times higher chance of being admitted 319 00:19:16,960 --> 00:19:19,639 Speaker 3: than a white and a nine times higher chance of 320 00:19:19,640 --> 00:19:23,200 Speaker 3: being admitted than an Asian. So we are definitely admitting 321 00:19:23,359 --> 00:19:27,640 Speaker 3: students that are not qualified. And what's happening is they're 322 00:19:27,720 --> 00:19:30,200 Speaker 3: not passing the licensing exam. 323 00:19:30,320 --> 00:19:33,320 Speaker 2: So what do we do? We throw out the grades. 324 00:19:33,240 --> 00:19:37,800 Speaker 3: The step one, the first step of medical licensing. Blacks 325 00:19:37,840 --> 00:19:40,080 Speaker 3: were doing so poorly on it that they were not 326 00:19:40,320 --> 00:19:45,439 Speaker 3: qualifying for their favored residencies, And so the AAMC that 327 00:19:45,520 --> 00:19:48,600 Speaker 3: administers this test said, no problem, let's get rid of 328 00:19:48,600 --> 00:19:52,720 Speaker 3: the standards. We'll just go past fail. So nobody admitting 329 00:19:52,800 --> 00:19:56,720 Speaker 3: students to hospital residencies will know where blacks stand on 330 00:19:56,840 --> 00:19:59,440 Speaker 3: the curve. This is going to happen. 331 00:19:59,160 --> 00:19:59,720 Speaker 2: More and more. 332 00:20:00,119 --> 00:20:02,800 Speaker 3: If you are in an emergency room and a black 333 00:20:02,880 --> 00:20:06,119 Speaker 3: doctor walks through the door, be very very scared. He 334 00:20:06,240 --> 00:20:08,640 Speaker 3: may be the best doctor around, he may be there 335 00:20:08,680 --> 00:20:09,960 Speaker 3: because of the color of his skin. 336 00:20:10,160 --> 00:20:11,720 Speaker 1: Well, that's what I's going to ask you, is the 337 00:20:11,840 --> 00:20:15,560 Speaker 1: licensing I mean, to go through medical school is one 338 00:20:15,600 --> 00:20:17,960 Speaker 1: thing which is very rigorous and I couldn't do it, 339 00:20:18,000 --> 00:20:20,439 Speaker 1: so I'm not you know, I'm not putting anyone down 340 00:20:20,480 --> 00:20:22,320 Speaker 1: who can go through it. But then there is the 341 00:20:22,400 --> 00:20:27,280 Speaker 1: licensing exams afterwards, which have enormous fail rates. And so 342 00:20:28,119 --> 00:20:29,800 Speaker 1: are they like I saw it in a lot of 343 00:20:29,800 --> 00:20:33,639 Speaker 1: times in law school after they go through After applicants 344 00:20:33,680 --> 00:20:36,840 Speaker 1: who've been there because of a DEI standard are going 345 00:20:36,840 --> 00:20:39,840 Speaker 1: through law school, they are not able to pass bars 346 00:20:40,000 --> 00:20:42,560 Speaker 1: at enormously high rates. That must be the same case 347 00:20:42,600 --> 00:20:43,399 Speaker 1: of the MCATs. 348 00:20:43,680 --> 00:20:46,800 Speaker 3: Yes, it's absolutely the same case, so that it's a 349 00:20:46,800 --> 00:20:51,400 Speaker 3: waste of resources. You're taking slots that are precious and 350 00:20:51,480 --> 00:20:54,160 Speaker 3: giving it to people who have a very high rate 351 00:20:54,520 --> 00:20:57,000 Speaker 3: of failing out. Let's hope they do fail out and 352 00:20:57,080 --> 00:21:00,800 Speaker 3: that the system doesn't figure out more ways to keep 353 00:21:00,800 --> 00:21:01,359 Speaker 3: them along. 354 00:21:01,480 --> 00:21:03,680 Speaker 2: You know, professors are under pressure. 355 00:21:04,840 --> 00:21:09,959 Speaker 3: There's studies that have shown that black residents get consistently lower, 356 00:21:10,119 --> 00:21:15,199 Speaker 3: far lower scores ratings from the faculty advisors than white 357 00:21:15,240 --> 00:21:18,760 Speaker 3: and Asian residents, and the only allowable explanation for that 358 00:21:19,680 --> 00:21:24,720 Speaker 3: is racism. And we need to retrain the supervisors Stanley 359 00:21:24,760 --> 00:21:28,439 Speaker 3: Goldfarb and nephrologists at the University of Pennsylvania Medical School. 360 00:21:28,760 --> 00:21:31,240 Speaker 2: When the studies came out, it showed. 361 00:21:30,920 --> 00:21:35,120 Speaker 3: These disparities and ratings, and the study only allowed three 362 00:21:35,160 --> 00:21:39,640 Speaker 3: possible explanations racism in the ratings, racism in the doctors, 363 00:21:39,720 --> 00:21:44,879 Speaker 3: or racism systemically throughout society. And Goldfarb, who's you know, 364 00:21:45,000 --> 00:21:47,320 Speaker 3: is not putting up with any of this. Bsad Well, 365 00:21:47,359 --> 00:21:50,320 Speaker 3: maybe the black residents were just worse at being residents. 366 00:21:50,600 --> 00:21:53,439 Speaker 2: He became even more of a parah. 367 00:21:53,480 --> 00:21:57,120 Speaker 3: People called him human garbage and the University of Pennsylvania 368 00:21:57,200 --> 00:22:02,359 Speaker 3: Dean set out numerous emails, you know, apologizing for the 369 00:22:02,400 --> 00:22:06,880 Speaker 3: trauma that Goldfarb's tweeted paused and setting up all sorts 370 00:22:06,880 --> 00:22:10,720 Speaker 3: of counseling sessions and whatnot, so anybody who speaks the 371 00:22:10,760 --> 00:22:14,280 Speaker 3: truth will be canceled. Has happened to an editor at 372 00:22:14,280 --> 00:22:18,800 Speaker 3: the Journal of American Medical Association JAMA who dared to 373 00:22:18,880 --> 00:22:22,360 Speaker 3: say that maybe systemic racism was not a problem in medicine. 374 00:22:22,440 --> 00:22:25,160 Speaker 3: He was disappeared, He lost his job, and the editor 375 00:22:25,200 --> 00:22:28,680 Speaker 3: of editor in chief of JAMMA lost his job, even 376 00:22:28,720 --> 00:22:29,040 Speaker 3: though he. 377 00:22:29,000 --> 00:22:31,360 Speaker 2: Had nothing to do with the podcast. 378 00:22:31,600 --> 00:22:33,840 Speaker 3: And of course, the editor in chief of JAMA was 379 00:22:33,880 --> 00:22:38,840 Speaker 3: replaced by a black female who specializes in systemic racism 380 00:22:39,080 --> 00:22:43,080 Speaker 3: and who promised to bring more diverse voices into medical publishing. 381 00:22:43,320 --> 00:22:45,959 Speaker 2: This matters. Quality of medical leadership matters. 382 00:22:46,000 --> 00:22:50,439 Speaker 3: We have got diversity quota hires now running cancer centers 383 00:22:50,480 --> 00:22:54,000 Speaker 3: across the country, whether it's Memorial Sloan Kettering, whether it's 384 00:22:54,119 --> 00:22:56,720 Speaker 3: University of Chicago, whether it's Cleveland Clinic. 385 00:22:57,040 --> 00:22:59,040 Speaker 2: It matters what their qualifications are. 386 00:22:59,080 --> 00:23:02,240 Speaker 3: It matters who's running medical journals, because this still is 387 00:23:02,240 --> 00:23:06,960 Speaker 3: Although the journals are dedicated to publishing vast homes of 388 00:23:07,400 --> 00:23:11,800 Speaker 3: virtually indistinguishable black studies rhetoric, they should also be publishing 389 00:23:11,800 --> 00:23:15,760 Speaker 3: cutting edge research, and every article that they're publishing on 390 00:23:15,840 --> 00:23:19,960 Speaker 3: the phantom problem of systemic racism is one less article 391 00:23:20,280 --> 00:23:25,600 Speaker 3: on actual scientific research that will allow scientists to pool 392 00:23:25,680 --> 00:23:30,440 Speaker 3: their intellectual resources to finally solve Alzheimer's disease, to finally 393 00:23:30,480 --> 00:23:35,000 Speaker 3: solve cancer. Instead, the medical profession is dedicated to solving 394 00:23:35,080 --> 00:23:38,160 Speaker 3: a non problem, which is systemic racism. 395 00:23:38,359 --> 00:23:39,800 Speaker 2: And let me also say this, if. 396 00:23:39,640 --> 00:23:42,640 Speaker 3: You care about black lives, you should care about honesty 397 00:23:42,720 --> 00:23:46,320 Speaker 3: and medicine. The medical profession is staking everything on the 398 00:23:46,359 --> 00:23:51,879 Speaker 3: proposition that disparities in health outcomes is due to doctor racism, 399 00:23:52,160 --> 00:23:54,840 Speaker 3: and so it's putting all of its marvels in training 400 00:23:54,880 --> 00:23:58,399 Speaker 3: doctors against their own racism. But if the reason for 401 00:23:58,480 --> 00:24:04,480 Speaker 3: disparities in health outcome is actual behavioral disparities, no attention 402 00:24:04,600 --> 00:24:07,000 Speaker 3: is going into that. We know, you cannot talk about 403 00:24:07,080 --> 00:24:10,280 Speaker 3: obesity because that would have a disparate impact on blacks. 404 00:24:10,520 --> 00:24:12,919 Speaker 2: You cannot talk about behavioral change. 405 00:24:13,320 --> 00:24:18,040 Speaker 3: The Scientific American had a whole issue devoted to systemic racism, 406 00:24:18,080 --> 00:24:20,960 Speaker 3: the science of racism that declared, if you talk to 407 00:24:20,960 --> 00:24:25,160 Speaker 3: black people about obesity, you are a racist doctor. If 408 00:24:25,200 --> 00:24:28,120 Speaker 3: it turns out that disparities in health outcomes and they 409 00:24:28,119 --> 00:24:32,520 Speaker 3: are real Blacks do have shorter lifespans if those disparities 410 00:24:32,560 --> 00:24:35,840 Speaker 3: are due to higher rates of smoking, higher rates of 411 00:24:35,920 --> 00:24:40,080 Speaker 3: drug use, less compliance with doctors orders, higher rates guns 412 00:24:40,240 --> 00:24:46,760 Speaker 3: of shootings, of involvement in crime, you know, poor dietary habits, 413 00:24:46,880 --> 00:24:50,600 Speaker 3: and doctors are now forbidden to talk about that. Guess 414 00:24:50,600 --> 00:24:52,280 Speaker 3: whose health is going to suffer further? 415 00:24:52,640 --> 00:24:53,080 Speaker 2: Blacks? 416 00:24:53,720 --> 00:24:56,959 Speaker 1: Yeah, no, And we see those a lot of those 417 00:24:57,000 --> 00:25:00,160 Speaker 1: same problems in white communities, poor white communities as well. 418 00:25:00,200 --> 00:25:02,040 Speaker 1: I always told people in Texas, if you live in 419 00:25:02,080 --> 00:25:04,840 Speaker 1: East Texas or West Texas, the most republican parts of Texas, 420 00:25:04,920 --> 00:25:07,680 Speaker 1: you live on average about twenty years shorter than Austin 421 00:25:07,800 --> 00:25:11,639 Speaker 1: or the more liberal parts. So it's not a singular 422 00:25:11,800 --> 00:25:14,080 Speaker 1: race thing. It's a thing with a lot of things 423 00:25:14,119 --> 00:25:15,960 Speaker 1: to do with poverty and other things. But they've made 424 00:25:16,000 --> 00:25:18,200 Speaker 1: an entire race thing. And I'll say one other thing 425 00:25:19,200 --> 00:25:22,600 Speaker 1: is that when you water down the standard, someone who 426 00:25:22,680 --> 00:25:27,840 Speaker 1: is an excellent black doctor like Ben Carson, their achievements 427 00:25:27,840 --> 00:25:31,920 Speaker 1: still matter, but they kind of the assumptions that they're 428 00:25:31,960 --> 00:25:37,200 Speaker 1: there because they are so brilliant in their field is 429 00:25:37,320 --> 00:25:40,320 Speaker 1: water down as well. So so many companies have walked 430 00:25:40,320 --> 00:25:44,720 Speaker 1: away from DEI policies in the last two years. The 431 00:25:44,800 --> 00:25:48,679 Speaker 1: medical industry seems to be opposed to those same changes, 432 00:25:48,760 --> 00:25:53,800 Speaker 1: especially the medical journals that I've read. Is there any solution? 433 00:25:53,960 --> 00:25:56,640 Speaker 1: And then, lastly, also is there any way to achieve 434 00:25:56,720 --> 00:25:59,600 Speaker 1: diversity while upholding meritocracy. 435 00:26:00,119 --> 00:26:03,000 Speaker 3: There was a bill that was introduced by Republicans in 436 00:26:03,040 --> 00:26:07,200 Speaker 3: Congress in twenty twenty four that would end federal funding 437 00:26:07,240 --> 00:26:12,840 Speaker 3: of medical schools that are committed to DEI That went nowhere, 438 00:26:12,880 --> 00:26:17,360 Speaker 3: but it was recently reintroduced in May. Certainly, cutting off 439 00:26:17,400 --> 00:26:22,119 Speaker 3: the federal spigot would help, and Trump is also trying 440 00:26:22,160 --> 00:26:26,960 Speaker 3: to change the appalling use of taxpayer dollars through the 441 00:26:27,040 --> 00:26:31,119 Speaker 3: National Institutes of Health and National Science Foundation to fund 442 00:26:32,000 --> 00:26:39,639 Speaker 3: research in why engineering is a biased profession and against 443 00:26:39,720 --> 00:26:43,280 Speaker 3: blacks and how we need so we're wasting vast amounts 444 00:26:43,320 --> 00:26:46,400 Speaker 3: of money. Cutting off the federal spigot would certainly help. 445 00:26:46,680 --> 00:26:49,400 Speaker 3: And no, there is no way at present to have 446 00:26:50,600 --> 00:26:52,880 Speaker 3: diversity and meritocracy at the same time. 447 00:26:52,960 --> 00:26:54,320 Speaker 2: You have to have one or the other. 448 00:26:54,640 --> 00:27:01,399 Speaker 3: Any institution that has declared itself undying flty to diversity 449 00:27:01,440 --> 00:27:04,920 Speaker 3: has told you that has discarded meritocracy. The skills gap 450 00:27:05,000 --> 00:27:06,080 Speaker 3: is simply too large. 451 00:27:06,400 --> 00:27:06,680 Speaker 2: Ryan. 452 00:27:07,040 --> 00:27:10,080 Speaker 3: The solution is to close the skills gap. It begins 453 00:27:10,280 --> 00:27:15,359 Speaker 3: early on. Sixty six percent of black twelfth graders do 454 00:27:15,480 --> 00:27:19,640 Speaker 3: not possess even partial mastery of the most basic twelfth 455 00:27:19,680 --> 00:27:23,520 Speaker 3: grade math skills like doing arithmetic or reading a graph. 456 00:27:23,800 --> 00:27:27,000 Speaker 3: That's the number of black twelfth graders who are advanced 457 00:27:27,000 --> 00:27:30,920 Speaker 3: in math nationally is too small to show up statistically, 458 00:27:30,960 --> 00:27:32,280 Speaker 3: so it's essentially zero. 459 00:27:32,720 --> 00:27:34,159 Speaker 2: Given those skills gaps. 460 00:27:34,200 --> 00:27:38,240 Speaker 3: The idea that absent racism phantom racism, we would have 461 00:27:38,440 --> 00:27:42,960 Speaker 3: thirteen percent Black representation in medical student bodies and medical 462 00:27:43,040 --> 00:27:44,240 Speaker 3: faculties is absurd. 463 00:27:44,560 --> 00:27:45,399 Speaker 2: The skills are not. 464 00:27:46,840 --> 00:27:49,760 Speaker 3: So that the community has to get its act together 465 00:27:49,840 --> 00:27:53,280 Speaker 3: and say we are going to be fanatically involved in 466 00:27:53,920 --> 00:27:56,200 Speaker 3: our children's schooling. 467 00:27:56,520 --> 00:27:58,000 Speaker 2: We have to monitor homework. 468 00:27:58,040 --> 00:27:59,960 Speaker 3: We have to make sure that black kids aren't running 469 00:28:00,400 --> 00:28:02,960 Speaker 3: running the streets and getting involved in gangs. 470 00:28:03,240 --> 00:28:05,800 Speaker 2: We have to get rid of the racial impact standard 471 00:28:05,840 --> 00:28:06,960 Speaker 2: in school discipline. 472 00:28:07,040 --> 00:28:10,760 Speaker 3: So if black students are raising hell and Trump one 473 00:28:10,800 --> 00:28:14,240 Speaker 3: of his executive orders did try to restore color blind 474 00:28:14,359 --> 00:28:17,520 Speaker 3: is to school discipline and not penalized schools who had 475 00:28:17,520 --> 00:28:20,840 Speaker 3: to higher rates of discipline for black students. And we 476 00:28:20,880 --> 00:28:23,240 Speaker 3: need to get back to basics and education and give 477 00:28:23,359 --> 00:28:24,760 Speaker 3: up the whole equity agenda. 478 00:28:25,040 --> 00:28:27,359 Speaker 1: Yeah, you know, it's so funny. I don't know if 479 00:28:27,359 --> 00:28:29,400 Speaker 1: you've heard you Have you heard of the miracle in Mississippi. 480 00:28:29,520 --> 00:28:31,520 Speaker 1: I've written about it a little bit, but it's about 481 00:28:31,760 --> 00:28:36,080 Speaker 1: Mississippi stopped allowing third graders to advance to fourth grade 482 00:28:36,080 --> 00:28:39,440 Speaker 1: if they could not meet reading curriculum, and Mississippi is 483 00:28:39,440 --> 00:28:43,120 Speaker 1: now the number four in the nation for fourth grade reading. 484 00:28:43,320 --> 00:28:48,200 Speaker 1: Fiiton did not not increasing the cost of schools and 485 00:28:48,280 --> 00:28:51,320 Speaker 1: for black students. I think it is number two nationwide 486 00:28:51,360 --> 00:28:54,520 Speaker 1: for black students overall because they refused to allow kids 487 00:28:54,560 --> 00:28:56,600 Speaker 1: to fail forward. But the idea of failing forward in 488 00:28:56,640 --> 00:29:00,080 Speaker 1: the early grades leads to all the subsequent consequences in 489 00:29:00,080 --> 00:29:02,520 Speaker 1: the later grades when they want to achieve these miracle 490 00:29:02,680 --> 00:29:05,880 Speaker 1: levels of diversity across different institutions, and it's happened in 491 00:29:05,920 --> 00:29:09,800 Speaker 1: engineering and in law firms and unfortunate medical fields. Heather, 492 00:29:09,880 --> 00:29:12,400 Speaker 1: where can people go to read more from you? Because 493 00:29:12,480 --> 00:29:15,440 Speaker 1: you write great things for the Manhattan Institute, But where 494 00:29:15,440 --> 00:29:16,880 Speaker 1: can people get your information from? 495 00:29:16,880 --> 00:29:18,120 Speaker 2: Oh? Thank you? Ron Well. 496 00:29:18,440 --> 00:29:21,120 Speaker 3: The Best Summer is my latest book, when raised Trump's 497 00:29:21,160 --> 00:29:25,600 Speaker 3: Merit that has a kind of thing of the atrocity 498 00:29:25,600 --> 00:29:29,480 Speaker 3: of diversity in stem science fields, in medicine, but also 499 00:29:29,800 --> 00:29:31,200 Speaker 3: in policing. 500 00:29:32,120 --> 00:29:33,200 Speaker 2: And in the arts. 501 00:29:33,920 --> 00:29:35,960 Speaker 3: And you know, the hope now is that Trump will 502 00:29:35,960 --> 00:29:38,640 Speaker 3: get a handle on this, but believe me, they're dug 503 00:29:38,720 --> 00:29:40,680 Speaker 3: in and they're going to try and keep a hold 504 00:29:40,880 --> 00:29:44,360 Speaker 3: of their hatred for Western civilization and excellence as long 505 00:29:44,400 --> 00:29:45,120 Speaker 3: as possible. 506 00:29:45,240 --> 00:29:47,760 Speaker 1: Well, thank you for being on this podcast. I appreciate it, 507 00:29:47,840 --> 00:29:48,360 Speaker 1: my pleasure. 508 00:29:48,440 --> 00:29:49,000 Speaker 2: Ryan, Thank you. 509 00:29:49,360 --> 00:29:54,640 Speaker 1: Hey, we'll be right back after this and now for 510 00:29:54,720 --> 00:29:57,080 Speaker 1: the Ask Me Anything segment. If you wanty part of 511 00:29:57,080 --> 00:29:59,800 Speaker 1: the Ask Me Anything segment, email me Ryan at Numbers 512 00:29:59,800 --> 00:30:04,120 Speaker 1: Game podcast dot com. That's Ryan at Numbers gamepodcast dot com. 513 00:30:04,160 --> 00:30:07,200 Speaker 1: I think this segment is quickly becoming the favorite of 514 00:30:07,240 --> 00:30:09,240 Speaker 1: the show and I get a lot of emails now 515 00:30:09,240 --> 00:30:12,720 Speaker 1: and I love answering them. This question comes from Leah Schutz. 516 00:30:13,280 --> 00:30:15,640 Speaker 1: She asked about the state of Colorado. She says that 517 00:30:15,680 --> 00:30:18,520 Speaker 1: her family moved from Texas to Colorado for work, and 518 00:30:18,560 --> 00:30:21,080 Speaker 1: she asks if her new state is becoming more purple. 519 00:30:21,560 --> 00:30:26,720 Speaker 1: The short answer is simply no. Colorado is probably going 520 00:30:26,760 --> 00:30:28,840 Speaker 1: to become one of the bluest states in the country 521 00:30:29,000 --> 00:30:31,720 Speaker 1: over a very short period of time. Its trajectory is 522 00:30:31,840 --> 00:30:35,800 Speaker 1: very obvious. Since twenty sixteen, the US overall moved four 523 00:30:35,840 --> 00:30:38,760 Speaker 1: points to the right nationally, with obviously some states like 524 00:30:38,800 --> 00:30:42,440 Speaker 1: Florida or New York moving more than four points. Colorado 525 00:30:42,560 --> 00:30:45,280 Speaker 1: has moved six points to the left during the same 526 00:30:45,360 --> 00:30:48,240 Speaker 1: time period. It did move two point five points to 527 00:30:48,280 --> 00:30:51,120 Speaker 1: the right between twenty twenty and twenty twenty four, but 528 00:30:51,200 --> 00:30:53,760 Speaker 1: that's why. As the country as a whole moves six 529 00:30:53,800 --> 00:30:56,440 Speaker 1: points to the right, so it's moving to the left 530 00:30:56,520 --> 00:30:59,640 Speaker 1: respective of the rest of the country, and part of 531 00:30:59,680 --> 00:31:01,920 Speaker 1: this date has moved to the right. The city of 532 00:31:01,960 --> 00:31:05,400 Speaker 1: Denver moved to the right pretty substantially. It moved four 533 00:31:05,440 --> 00:31:08,760 Speaker 1: points overall between twenty twenty and twenty twenty four, but 534 00:31:08,880 --> 00:31:12,160 Speaker 1: other areas, like for Collins, the suburbs of Colorado Springs, 535 00:31:12,160 --> 00:31:15,480 Speaker 1: and the areas between Boulder and Denver are moving hard 536 00:31:15,520 --> 00:31:18,720 Speaker 1: to the left. In twenty sixteen, November of twenty sixteen, 537 00:31:19,000 --> 00:31:24,400 Speaker 1: Democrats had a seven thousand person voter advantage over Republicans 538 00:31:24,400 --> 00:31:27,120 Speaker 1: in the state. They now have over one hundred and 539 00:31:27,400 --> 00:31:30,840 Speaker 1: ten thousand. Over the course of those eight years. From 540 00:31:31,280 --> 00:31:35,080 Speaker 1: November twenty sixteen to November twenty twenty four, Democrats' advantage 541 00:31:35,080 --> 00:31:39,400 Speaker 1: has been pretty substantially growing, and then the independent numbers 542 00:31:39,440 --> 00:31:42,480 Speaker 1: have grown by almost a million between the same time, 543 00:31:42,520 --> 00:31:46,920 Speaker 1: and the independence in Colorado lean to the left. So 544 00:31:47,880 --> 00:31:49,520 Speaker 1: in the long and short of it, no, it's not 545 00:31:49,600 --> 00:31:51,680 Speaker 1: moving to be more purple, it's moving to be more left. 546 00:31:51,720 --> 00:31:53,400 Speaker 1: It's one of the states that I would not be 547 00:31:53,520 --> 00:31:58,680 Speaker 1: shocked in the next decade has very very few elected 548 00:31:58,720 --> 00:32:01,560 Speaker 1: Republicans from it. I mean, I wouldn't be shocked if 549 00:32:01,600 --> 00:32:04,120 Speaker 1: one day there's only one or two Republican congressmen from 550 00:32:04,160 --> 00:32:06,880 Speaker 1: the entire state of Colorado. I think right now they 551 00:32:06,880 --> 00:32:10,760 Speaker 1: have four. So yeah, Unfortunately, that's just the way the 552 00:32:10,760 --> 00:32:15,320 Speaker 1: state's moving. And maybe demographics will shift and Hispanics will 553 00:32:15,360 --> 00:32:19,880 Speaker 1: continue to vote more right and other people move more right. 554 00:32:19,920 --> 00:32:23,440 Speaker 1: But a lot of people from Texas and California who 555 00:32:23,520 --> 00:32:26,080 Speaker 1: are left wing and want to be around left wing 556 00:32:26,120 --> 00:32:30,720 Speaker 1: people have seen Colorado as their new home. So that's 557 00:32:30,800 --> 00:32:32,480 Speaker 1: just the way that's Colorado is not going to be 558 00:32:32,520 --> 00:32:36,000 Speaker 1: able to be the state that makes the next Republican present. 559 00:32:36,040 --> 00:32:38,400 Speaker 1: I think I wouldn't be shocked if New Mexico voted 560 00:32:38,480 --> 00:32:42,440 Speaker 1: Republican before Colorado. Anyway, that's the show. Thank you so much. 561 00:32:42,840 --> 00:32:45,160 Speaker 1: Be back on Thursday. I will talk about polling and 562 00:32:45,240 --> 00:32:47,960 Speaker 1: will go into election season again. I promise you all. 563 00:32:48,280 --> 00:32:49,680 Speaker 1: Thank you see them