1 00:00:04,800 --> 00:00:07,600 Speaker 1: Welcome back to a Numbers Game with Ryan Gerdusky. I'm 2 00:00:07,640 --> 00:00:10,119 Speaker 1: your host. Thank you all for being here for another week. 3 00:00:10,320 --> 00:00:13,360 Speaker 1: If you find yourself bored, lonely, depressed, looking for something 4 00:00:13,400 --> 00:00:15,240 Speaker 1: to do, I just want to say I have the 5 00:00:15,280 --> 00:00:18,160 Speaker 1: idea for you. Please like and subscribe this podcast. Give 6 00:00:18,200 --> 00:00:20,200 Speaker 1: me a five star review. If you have the chance 7 00:00:20,239 --> 00:00:23,080 Speaker 1: of your time, you won't miss an episode. You'll learn something, 8 00:00:23,120 --> 00:00:26,159 Speaker 1: you'll be smarter. I try to bring it twice a 9 00:00:26,200 --> 00:00:28,960 Speaker 1: week every week, so I appreciate you all being here. 10 00:00:29,160 --> 00:00:31,360 Speaker 1: Please make sure you're here every time. And now I 11 00:00:31,400 --> 00:00:33,440 Speaker 1: want to talk to you and I want to ask 12 00:00:33,520 --> 00:00:35,600 Speaker 1: you if you were to sit there and think, what 13 00:00:35,680 --> 00:00:39,280 Speaker 1: was the most consequential thing the Supreme Court has done 14 00:00:39,400 --> 00:00:42,720 Speaker 1: in the last five years? Right, really since the end 15 00:00:42,720 --> 00:00:45,760 Speaker 1: of the first Trump term. Probably most of you, nine 16 00:00:45,880 --> 00:00:48,959 Speaker 1: out of ten would say abortion right Roe v. Wait, 17 00:00:49,040 --> 00:00:52,960 Speaker 1: Because it's the most politically engaging and toxic and energetic. 18 00:00:53,159 --> 00:00:54,840 Speaker 1: Depends on what side of the aisle you fall on. 19 00:00:54,880 --> 00:00:58,080 Speaker 1: It's where people pay attentions for. Politicians give a lot 20 00:00:58,080 --> 00:01:01,240 Speaker 1: of focus to. But to me, the decision, the most 21 00:01:01,280 --> 00:01:03,960 Speaker 1: important decision the Supreme Court made over the last five years, 22 00:01:04,000 --> 00:01:06,479 Speaker 1: the one that will have the biggest impact over most 23 00:01:06,480 --> 00:01:09,559 Speaker 1: of our lives was the Students for Fair Emission versus 24 00:01:09,600 --> 00:01:13,520 Speaker 1: Harvard case. That case examined the illegality of race based 25 00:01:13,520 --> 00:01:18,160 Speaker 1: emissions in higher education, ruling that it was unconstitutional that 26 00:01:18,200 --> 00:01:21,479 Speaker 1: the school at Harvard had discriminated against Asian applicants Asian 27 00:01:21,520 --> 00:01:25,200 Speaker 1: American applicants in favor of black and latinos. Now, unlike 28 00:01:25,240 --> 00:01:29,080 Speaker 1: the overturning of Row, which was a deeply divided issue, 29 00:01:29,200 --> 00:01:32,600 Speaker 1: ending race based admissions to colleges was extremely popular with 30 00:01:32,600 --> 00:01:36,000 Speaker 1: the American people. A poll from Pew Research in twenty 31 00:01:36,000 --> 00:01:40,000 Speaker 1: twenty three found that fifty percent of American adults disapproved 32 00:01:40,080 --> 00:01:43,000 Speaker 1: of universities considering race when it comes to emission. Just 33 00:01:43,120 --> 00:01:46,760 Speaker 1: thirty three percent approved, and there was a stark political divide. 34 00:01:47,040 --> 00:01:50,800 Speaker 1: Seventy four percent of Republicans opposed using race and fifty 35 00:01:50,920 --> 00:01:54,480 Speaker 1: four percent of Democrats approved of it, but overwhelmingly when 36 00:01:54,520 --> 00:01:57,000 Speaker 1: you ask independents and still a large chunk of Democrats, 37 00:01:57,120 --> 00:02:00,280 Speaker 1: they disapproved of it. Most of the country did, but 38 00:02:00,360 --> 00:02:04,040 Speaker 1: once the Harvard case happened, other ivy leagues and top 39 00:02:04,120 --> 00:02:07,200 Speaker 1: colleges began following student dropping race and ethnicity as a 40 00:02:07,280 --> 00:02:10,400 Speaker 1: requirement or part of their emissions process. According to The 41 00:02:10,400 --> 00:02:13,200 Speaker 1: New York Times, which reviewed sixty six colleges in the 42 00:02:13,240 --> 00:02:16,320 Speaker 1: aftermath that changes to emissions, they found that black and 43 00:02:16,400 --> 00:02:20,440 Speaker 1: Latino enrollment dropped by one percent overall in the top 44 00:02:20,520 --> 00:02:23,160 Speaker 1: schools and in the IVY leagues in one percent of 45 00:02:23,200 --> 00:02:26,520 Speaker 1: Hispanics one percent of blacks. MIT's enrollment dropped by ten 46 00:02:26,639 --> 00:02:30,640 Speaker 1: points among black applicants from sixteen percent to six. Amherst 47 00:02:30,760 --> 00:02:33,400 Speaker 1: University fell by twelve point five to three point six. 48 00:02:33,880 --> 00:02:36,519 Speaker 1: John Hopkins went from eleven point six black to four 49 00:02:36,520 --> 00:02:40,000 Speaker 1: point one black, and Columbia fell from twenty percent to 50 00:02:40,080 --> 00:02:44,320 Speaker 1: twelve percent. For Latino students in John Hopkins they fell 51 00:02:44,360 --> 00:02:47,400 Speaker 1: in half, from twenty five to twelve point five. NYU 52 00:02:47,440 --> 00:02:49,920 Speaker 1: went from twenty one percent Latino to thirteen percent the 53 00:02:49,960 --> 00:02:53,360 Speaker 1: incoming class I'm talking about. Cornell went from seventeen to 54 00:02:53,440 --> 00:02:56,600 Speaker 1: ten and Carnegie Mail went from fourteen to eight. The 55 00:02:56,680 --> 00:02:58,359 Speaker 1: share of Asian students went up in a number of 56 00:02:58,400 --> 00:03:01,440 Speaker 1: top schools, including Barnard and it in Columbia, and for 57 00:03:01,560 --> 00:03:05,079 Speaker 1: white students they also ticked up significantly. And the vein 58 00:03:05,160 --> 00:03:07,600 Speaker 1: of that case, the focus of that case was really 59 00:03:07,760 --> 00:03:10,920 Speaker 1: just on Asian emissions that became the victim of this 60 00:03:11,320 --> 00:03:15,919 Speaker 1: entire scandal of race based admissions. Now the case behind Harvard, Right, 61 00:03:15,960 --> 00:03:18,639 Speaker 1: they specifically said, as I said before, Asians were the 62 00:03:18,720 --> 00:03:22,320 Speaker 1: name target of this mass discrimination. And you saw this 63 00:03:22,360 --> 00:03:25,120 Speaker 1: in Harvard and John Hopkins and a number of universities 64 00:03:25,120 --> 00:03:26,960 Speaker 1: where right after the Supreme Court rule there was a 65 00:03:27,080 --> 00:03:31,160 Speaker 1: huge spike of Asian enrollment. But what about white applicants? Right? 66 00:03:31,360 --> 00:03:33,920 Speaker 1: What what if I were to tell you that white 67 00:03:34,000 --> 00:03:37,680 Speaker 1: students were more discriminated than other groups at the some 68 00:03:37,920 --> 00:03:40,120 Speaker 1: of the most major universities in some of the biggest 69 00:03:40,120 --> 00:03:42,800 Speaker 1: ivy leagues in this country. A study by Georgetown University 70 00:03:42,800 --> 00:03:46,200 Speaker 1: found that if college admission were based solely on test scores, 71 00:03:46,600 --> 00:03:49,680 Speaker 1: the proportion of Asian students a top colleges would actually 72 00:03:49,720 --> 00:03:54,440 Speaker 1: fall from eleven percent to ten percent. Conversely, white students 73 00:03:54,480 --> 00:03:58,240 Speaker 1: applicants would increase from sixty six percent to seventy five percent. 74 00:03:58,760 --> 00:04:03,080 Speaker 1: Affirmative action is anti white in many of these cases. 75 00:04:04,000 --> 00:04:06,920 Speaker 1: A study by Zach Goldberg was released just last week 76 00:04:06,960 --> 00:04:09,600 Speaker 1: that found that racial discrimination wasn't just a thing for 77 00:04:09,640 --> 00:04:12,760 Speaker 1: the left wing colleges like Harvard, No, it's actually being 78 00:04:12,800 --> 00:04:17,560 Speaker 1: committed at the Naval Academy, the prime group facing discrimination 79 00:04:18,160 --> 00:04:21,440 Speaker 1: is not Asians, it's white men. Goldberg found that a 80 00:04:21,480 --> 00:04:24,280 Speaker 1: white applicant with the same exact test scores as a 81 00:04:24,320 --> 00:04:27,760 Speaker 1: black applicant is significantly less likely to get admitted to 82 00:04:27,800 --> 00:04:31,040 Speaker 1: the US Naval Academy. A black applicant with a five 83 00:04:31,080 --> 00:04:34,400 Speaker 1: percent chance of emissions, their odds would jump to fifty 84 00:04:34,480 --> 00:04:38,119 Speaker 1: percent if evaluate as being Black, They'd be fifteen percent 85 00:04:38,160 --> 00:04:40,599 Speaker 1: chance to get if they were Hispanic, and eighteen percent 86 00:04:40,720 --> 00:04:42,839 Speaker 1: chance to be in if they were Asian. A white 87 00:04:42,880 --> 00:04:46,040 Speaker 1: applicant with a twenty five percent baseline emission, because that's 88 00:04:46,120 --> 00:04:48,599 Speaker 1: you know, on average where their grades were well, they 89 00:04:48,640 --> 00:04:50,960 Speaker 1: would be eighty seven percent as likely if they were Black, 90 00:04:51,080 --> 00:04:53,280 Speaker 1: fifty two percent if they were Hispanic, and fifty nine 91 00:04:53,320 --> 00:04:56,440 Speaker 1: percent if they were Asian. Asians in the Naval Academy 92 00:04:56,480 --> 00:05:00,000 Speaker 1: actually had a higher level of chance of getting in, 93 00:05:00,080 --> 00:05:03,800 Speaker 1: and then even Hispanics when DEI was applied. Goldberg further 94 00:05:03,800 --> 00:05:06,560 Speaker 1: states that across five admission cycles, roughly two thirds of 95 00:05:06,600 --> 00:05:09,760 Speaker 1: black applicants and one third of Asian and Hispanic applicants 96 00:05:10,200 --> 00:05:13,320 Speaker 1: would not have been admitted had it been absent of 97 00:05:13,360 --> 00:05:17,360 Speaker 1: their race. Had been a completely blind emission no race 98 00:05:17,440 --> 00:05:21,800 Speaker 1: looked at whatsoever, just meritocracy, just great scores and ability 99 00:05:21,920 --> 00:05:25,640 Speaker 1: and all the rest of that. A tremendous amount of 100 00:05:25,680 --> 00:05:28,799 Speaker 1: non white applicants would have not gotten into the Naval 101 00:05:28,839 --> 00:05:33,520 Speaker 1: Academy by admitting students were less qualified to attend, but 102 00:05:33,760 --> 00:05:37,760 Speaker 1: brought greater racial diversity. It didn't improve testing standards, it 103 00:05:37,839 --> 00:05:42,040 Speaker 1: lowered them. Black and Hispanic students performed worse once they 104 00:05:42,040 --> 00:05:45,840 Speaker 1: were in the Naval Academy than the overall average. So 105 00:05:45,880 --> 00:05:48,680 Speaker 1: it's not like this is a liberal theory that they have. 106 00:05:49,400 --> 00:05:51,839 Speaker 1: Is if you get an applicant who maybe they're not 107 00:05:52,200 --> 00:05:56,159 Speaker 1: qualified because their SAT scores or you know, their scores 108 00:05:56,200 --> 00:05:58,520 Speaker 1: aren't high enough, but once they get in the academy, 109 00:05:58,720 --> 00:06:01,080 Speaker 1: they'll be around other smart people and they're going to surge. 110 00:06:01,120 --> 00:06:04,360 Speaker 1: They're going to do so so well. That has never 111 00:06:04,440 --> 00:06:06,600 Speaker 1: been proven to be true, and it is not true 112 00:06:06,600 --> 00:06:09,360 Speaker 1: in the Naval Academy, as black and Hispanic students who 113 00:06:09,440 --> 00:06:13,200 Speaker 1: had gotten in in part because of their race were 114 00:06:13,240 --> 00:06:17,200 Speaker 1: struggling with subjects like calculus and science, So it didn't 115 00:06:17,240 --> 00:06:21,080 Speaker 1: make the overall student population smarter. It just made them 116 00:06:21,080 --> 00:06:23,839 Speaker 1: more racially diverse. For the sake of racial diversity. The 117 00:06:23,920 --> 00:06:26,600 Speaker 1: degree to which white applicants are discriminated against at the 118 00:06:26,720 --> 00:06:29,440 Speaker 1: US Naval Academy is difficult for me to describe alone 119 00:06:29,480 --> 00:06:31,839 Speaker 1: because this is a very long article of very long 120 00:06:31,839 --> 00:06:34,240 Speaker 1: substack posts, well over one hundred pages. I did go 121 00:06:34,320 --> 00:06:36,560 Speaker 1: through it, but I got the man himself, the man 122 00:06:36,560 --> 00:06:38,839 Speaker 1: who did all the research, who could describe what's actually 123 00:06:38,880 --> 00:06:42,080 Speaker 1: happening at the US Naval Academy and other federal government 124 00:06:42,200 --> 00:06:46,480 Speaker 1: backed schools about diversity, equity, inclusion, and anti white discrimination. 125 00:06:47,000 --> 00:06:49,920 Speaker 1: Please just stay with me for his tour de force investigation. 126 00:06:50,320 --> 00:06:56,640 Speaker 1: Zach Goldberg's next Stay tuned. Zach Goldberg is an independent 127 00:06:56,680 --> 00:07:00,320 Speaker 1: researcher who has an absolutely incredible substack Zach Goldberg dot 128 00:07:00,360 --> 00:07:02,760 Speaker 1: substack dot com. I've been a fan of his for years. 129 00:07:02,800 --> 00:07:06,279 Speaker 1: I can't recommend his subsecond enough. Thank you for being here, Zach. 130 00:07:06,200 --> 00:07:07,599 Speaker 2: Thank you for having me on. Ryan. 131 00:07:08,040 --> 00:07:12,040 Speaker 1: So, Okay, first things first, go over your data. What 132 00:07:12,080 --> 00:07:15,760 Speaker 1: are the main finding showing that there's a discrimination against 133 00:07:15,920 --> 00:07:18,000 Speaker 1: whites at the US Naval Academy. 134 00:07:18,240 --> 00:07:20,560 Speaker 2: Yeah, I'll just before I get into that, I just 135 00:07:20,640 --> 00:07:23,600 Speaker 2: want to say, like, until this report, until this case, 136 00:07:23,920 --> 00:07:27,320 Speaker 2: the public really like the understanding of what was going 137 00:07:27,360 --> 00:07:29,360 Speaker 2: on behind the scenes of the Naval Academy or just 138 00:07:29,400 --> 00:07:32,800 Speaker 2: other service adam in general is really off limits. Okay. 139 00:07:33,040 --> 00:07:38,480 Speaker 2: And now with this lawsuit. During the discovery that case, 140 00:07:39,360 --> 00:07:42,120 Speaker 2: student of the Naval Academy was forced to hand over 141 00:07:42,840 --> 00:07:50,400 Speaker 2: some admission of data, and that data was Peter or Arcido, 142 00:07:51,440 --> 00:07:56,200 Speaker 2: who also worked on the Harvard case. And what RCAD 143 00:07:56,840 --> 00:08:02,680 Speaker 2: finds is rather, you know, in contrary to the Naval 144 00:08:02,680 --> 00:08:08,520 Speaker 2: Academy claims of their consideration of race being limited, nondeterminative, 145 00:08:09,040 --> 00:08:12,880 Speaker 2: you know, one factor among many other factors, the holistic 146 00:08:13,080 --> 00:08:20,840 Speaker 2: you know, consideration. What he finds is well, for instance, 147 00:08:21,240 --> 00:08:25,200 Speaker 2: if white applicant with a five percent chance of getting 148 00:08:25,200 --> 00:08:29,160 Speaker 2: into the Naval Academy would have almost a fifty five 149 00:08:29,240 --> 00:08:34,040 Speaker 2: percent chance if treated as black, twenty five percent chance. 150 00:08:34,080 --> 00:08:36,000 Speaker 2: If a white Daftan had a twenty five percent chance 151 00:08:36,040 --> 00:08:38,679 Speaker 2: to get a game, he would have, you know, an 152 00:08:38,720 --> 00:08:41,080 Speaker 2: over eighty five percent chance of getting in it treated 153 00:08:41,120 --> 00:08:43,160 Speaker 2: as black. Now I mentioned I see a lot blacks 154 00:08:43,200 --> 00:08:46,640 Speaker 2: because they the preferences for them are the strongest. But 155 00:08:47,520 --> 00:08:51,880 Speaker 2: unlike Harvard, were Asians were also getting you know, we're 156 00:08:51,920 --> 00:08:55,800 Speaker 2: also disadvantage in the emission process. Here are Asians also 157 00:08:55,880 --> 00:08:59,560 Speaker 2: going to boost so whites are whites, and the class 158 00:08:59,600 --> 00:09:03,960 Speaker 2: of Apple didn't declare a race whatsoever, which is interesting 159 00:09:04,000 --> 00:09:08,560 Speaker 2: because they're just as disadvantages as white you know once 160 00:09:08,679 --> 00:09:12,280 Speaker 2: are the I guess the victims here of you know 161 00:09:12,559 --> 00:09:17,160 Speaker 2: discrimination or these race based emissions apprentices. And just a 162 00:09:17,280 --> 00:09:20,520 Speaker 2: one more final data point is that if not for 163 00:09:20,679 --> 00:09:26,800 Speaker 2: racial preferences, destimated that over seventy percent of black admits 164 00:09:26,920 --> 00:09:30,360 Speaker 2: would not have getten gotten in. For Hispanics and Asians, 165 00:09:31,200 --> 00:09:34,880 Speaker 2: for the preferences are significance, but not as much as blacks, 166 00:09:34,880 --> 00:09:37,520 Speaker 2: as it's a brown like one and three whenever. 167 00:09:38,080 --> 00:09:40,839 Speaker 1: So then to round that up, if an applicant was 168 00:09:40,920 --> 00:09:44,920 Speaker 1: white and had you know, they had a decent grade 169 00:09:44,920 --> 00:09:46,520 Speaker 1: point average or whatever, but they would only have a 170 00:09:46,600 --> 00:09:49,400 Speaker 1: five percent chance and fifty percent chance if they were black, 171 00:09:49,440 --> 00:09:52,240 Speaker 1: and significantly higher chances if they were both Asian and Hispanic. 172 00:09:52,840 --> 00:09:57,160 Speaker 1: And there would be an immense fall off for applicants 173 00:09:57,200 --> 00:10:00,199 Speaker 1: if it was just equally based on grades, which is 174 00:10:00,240 --> 00:10:03,440 Speaker 1: what a study by I think it was Georgetown University found. 175 00:10:03,520 --> 00:10:05,160 Speaker 1: If you I mentioned at the top of the show 176 00:10:05,480 --> 00:10:08,559 Speaker 1: George and University said it there was if everything was 177 00:10:08,600 --> 00:10:11,439 Speaker 1: based on race, their discrimination against whites would show that 178 00:10:11,480 --> 00:10:14,440 Speaker 1: it is actually higher than any other group including agents. 179 00:10:14,720 --> 00:10:17,720 Speaker 1: Now you mentioned the Harvard case, which is obviously where 180 00:10:17,960 --> 00:10:20,360 Speaker 1: a lot of this stuff information is forced to come 181 00:10:20,400 --> 00:10:23,559 Speaker 1: out from in all different universities. How is this and 182 00:10:23,720 --> 00:10:26,800 Speaker 1: you mentioned this different as different any other way aside 183 00:10:26,840 --> 00:10:30,600 Speaker 1: from justice, not just asians and whites, it's just whites. 184 00:10:32,559 --> 00:10:34,760 Speaker 2: Well, I guess if you were to, like I know, 185 00:10:34,880 --> 00:10:38,520 Speaker 2: in the Harvard case, our CD of sous analysis, he 186 00:10:38,640 --> 00:10:42,000 Speaker 2: had also a decile analysis where he's looking at the 187 00:10:42,120 --> 00:10:47,640 Speaker 2: admission rates. I the admissions indexed Descyle, So you have 188 00:10:47,840 --> 00:10:49,920 Speaker 2: ten deciles, and he looked, okay, what is the rate 189 00:10:50,040 --> 00:10:55,040 Speaker 2: for different advocates in this decile. Now with Harvard, you 190 00:10:55,280 --> 00:10:59,760 Speaker 2: see obviously at the bottom decile, know, nobody's really getting 191 00:11:00,840 --> 00:11:05,120 Speaker 2: and then you see outside of the bottom descol you 192 00:11:05,280 --> 00:11:08,319 Speaker 2: really see just a kind of a linear increase in 193 00:11:08,480 --> 00:11:12,800 Speaker 2: the black mission rates, and you really even up to 194 00:11:13,040 --> 00:11:15,959 Speaker 2: the final doestyle where I think, wats, maybe you're like 195 00:11:16,120 --> 00:11:19,160 Speaker 2: ninety percent vereas whites. I think, for a while, since 196 00:11:19,160 --> 00:11:21,599 Speaker 2: I read the cancer seen that data for whites, I 197 00:11:21,679 --> 00:11:24,320 Speaker 2: think maybe the next they get to in the top 198 00:11:24,400 --> 00:11:27,360 Speaker 2: decile is like fifty percent. In this case with the 199 00:11:27,440 --> 00:11:33,400 Speaker 2: naval academy. The greatest displays are not at the well 200 00:11:33,520 --> 00:11:36,200 Speaker 2: in both cases, they are the displies are at the bottom. 201 00:11:36,840 --> 00:11:39,199 Speaker 2: In this case, they tend to be greatest towards the 202 00:11:39,360 --> 00:11:42,480 Speaker 2: middle regions of middlessiles of the distribution. 203 00:11:43,160 --> 00:11:44,120 Speaker 1: That's fascinating. 204 00:11:44,920 --> 00:11:49,640 Speaker 2: Yeah, and uh, well, because once you get to the top, 205 00:11:50,120 --> 00:11:52,679 Speaker 2: I mean, everybody's well still, once you get to the 206 00:11:52,720 --> 00:11:55,640 Speaker 2: top descil, there's still about a fifteen point gap, let's say, 207 00:11:56,320 --> 00:11:58,560 Speaker 2: or maybe even let me look at the numbers again. 208 00:11:59,080 --> 00:12:01,240 Speaker 2: Whites are also getting and I think like around seventy 209 00:12:01,240 --> 00:12:03,360 Speaker 2: percent of the time, Blacks are beginning in like ninety 210 00:12:03,400 --> 00:12:06,120 Speaker 2: percent of the time. So still but not as much 211 00:12:06,160 --> 00:12:08,080 Speaker 2: as you see in the Harbor case. But there's still 212 00:12:08,559 --> 00:12:10,560 Speaker 2: substantial differences even in the top. 213 00:12:10,679 --> 00:12:13,920 Speaker 1: Does administrations defend this move? I mean, I saw something 214 00:12:13,920 --> 00:12:17,280 Speaker 1: about military readiness, But is that that that the military, 215 00:12:17,280 --> 00:12:20,080 Speaker 1: if it's diverses, more ready for combat? But is there 216 00:12:20,160 --> 00:12:21,400 Speaker 1: any evidence behind that? 217 00:12:22,800 --> 00:12:26,040 Speaker 2: Uh? Well, that's the thing is that rather than really 218 00:12:26,120 --> 00:12:29,640 Speaker 2: rebutting this analysis with data, they went all in, or 219 00:12:29,800 --> 00:12:34,160 Speaker 2: when I say they, I mean the the DOJ Underminding 220 00:12:34,480 --> 00:12:39,360 Speaker 2: who was defending the USNA in this case. They went 221 00:12:39,440 --> 00:12:42,240 Speaker 2: all in on a diversity defense. Uh, and a lot 222 00:12:42,280 --> 00:12:45,160 Speaker 2: of the arguments really just copied straight out of other 223 00:12:45,640 --> 00:12:52,319 Speaker 2: firm compacts, a firm demanding cases and uh. But what 224 00:12:52,559 --> 00:12:55,800 Speaker 2: really was was really no about is whereas these cases 225 00:12:55,840 --> 00:13:00,199 Speaker 2: we're talking about getting the benefit the educational benefits of 226 00:13:00,360 --> 00:13:04,360 Speaker 2: a diverse class, now we're talking about the military benefits, 227 00:13:04,400 --> 00:13:09,800 Speaker 2: the lethality benefits. So the claim is that a racially 228 00:13:09,960 --> 00:13:18,840 Speaker 2: diverse officer corps enhances military effectiveness, unique lethality, it promotes cohesion. 229 00:13:19,679 --> 00:13:22,520 Speaker 2: Now what's the evidence for that? Well, the truth of 230 00:13:22,600 --> 00:13:25,120 Speaker 2: the matter is is the evidence is so barren on 231 00:13:25,240 --> 00:13:32,240 Speaker 2: this front that our expert diversity witnesses or expert they 232 00:13:32,400 --> 00:13:37,520 Speaker 2: had to resort even to well just citing papers that 233 00:13:37,640 --> 00:13:39,800 Speaker 2: had really nothing to do They had nothing to do 234 00:13:39,840 --> 00:13:43,600 Speaker 2: with raciel diversity whatsoever, and something to do with diversity 235 00:13:43,800 --> 00:13:47,439 Speaker 2: just in a different context outside of a military context, 236 00:13:47,880 --> 00:13:50,360 Speaker 2: didn't relate to race diversity. So they're kind of grasping 237 00:13:50,400 --> 00:13:54,360 Speaker 2: a trust, trying to just citation paths and hoping people 238 00:13:54,400 --> 00:13:56,319 Speaker 2: don't actually look at the citations. But when you look 239 00:13:56,320 --> 00:13:59,240 Speaker 2: at the citations, you see there's really nothing there. There's 240 00:13:59,320 --> 00:14:04,240 Speaker 2: no to take evidence, And in truth, that's not their 241 00:14:04,320 --> 00:14:08,840 Speaker 2: fault because the pertative Defense themselves admitted that we've actually 242 00:14:09,040 --> 00:14:15,040 Speaker 2: never studied the relationship between racial diversity and military effectiveness. 243 00:14:15,120 --> 00:14:19,880 Speaker 2: We don't know whether variations in position for units more 244 00:14:19,960 --> 00:14:23,200 Speaker 2: diverse units performed better and less diverse years. 245 00:14:23,280 --> 00:14:25,080 Speaker 1: I'm they never study that. 246 00:14:25,160 --> 00:14:25,880 Speaker 2: That is crazy. 247 00:14:25,920 --> 00:14:29,520 Speaker 1: I think in a race of society like America, you 248 00:14:29,560 --> 00:14:31,520 Speaker 1: would have studied, someone would have studied that. In the military, 249 00:14:31,560 --> 00:14:32,320 Speaker 1: they never studied that. 250 00:14:33,200 --> 00:14:36,600 Speaker 2: That's what's incredible is they've had decades to which to 251 00:14:36,880 --> 00:14:40,640 Speaker 2: produce such data, and you think, if you know, well, 252 00:14:40,840 --> 00:14:43,760 Speaker 2: that's the thing. Either they actually have done the studies 253 00:14:44,960 --> 00:14:48,800 Speaker 2: and the studies don't support the narrative, or they just 254 00:14:48,960 --> 00:14:53,440 Speaker 2: don't care about the data whatsoever. They they are they're 255 00:14:53,560 --> 00:14:56,680 Speaker 2: more comfortable. This isn't kind of it's the thing. We 256 00:14:56,840 --> 00:15:00,400 Speaker 2: want federal policy to be evidence based, it to be 257 00:15:00,640 --> 00:15:04,400 Speaker 2: based on rigorous analysis, and in this case, it seems 258 00:15:04,480 --> 00:15:09,040 Speaker 2: like they are more comfortable just you know, advancing the 259 00:15:09,120 --> 00:15:13,280 Speaker 2: ideological uh, you know, moral priorities over absolute seeing. Does 260 00:15:13,360 --> 00:15:18,200 Speaker 2: this actually make sense empirically, because on one hand, the 261 00:15:18,280 --> 00:15:20,960 Speaker 2: Department of Defense, in the Naval Academy, they say, listen, 262 00:15:21,480 --> 00:15:26,440 Speaker 2: we're not doing this for racial diversity. Sake, racial diversity 263 00:15:26,640 --> 00:15:28,880 Speaker 2: is merely a means to an end, and that end 264 00:15:29,760 --> 00:15:37,280 Speaker 2: is improve military effectiveness. But if that's the case, then 265 00:15:37,680 --> 00:15:40,520 Speaker 2: you know, and they also plan to value all sorts 266 00:15:40,560 --> 00:15:43,120 Speaker 2: of diversity, not just race. You know, they say they 267 00:15:43,240 --> 00:15:46,160 Speaker 2: value all sorts of diversity, but when you actually look 268 00:15:46,200 --> 00:15:49,240 Speaker 2: at the models, you know, like economics, socio economic diversity, 269 00:15:49,280 --> 00:15:51,880 Speaker 2: it gets no way, it gets in effect, it gets 270 00:15:52,000 --> 00:15:57,520 Speaker 2: almost no way whatsoever. It's all race. So it's hard 271 00:15:57,640 --> 00:16:01,000 Speaker 2: not to walk away with the conclusion that, uh, they 272 00:16:01,040 --> 00:16:02,840 Speaker 2: don't really care the e this is a lot. 273 00:16:02,760 --> 00:16:05,880 Speaker 1: Of this is right right right, Well that's why you 274 00:16:05,920 --> 00:16:09,240 Speaker 1: see those commercials you know of like it used to 275 00:16:09,280 --> 00:16:11,080 Speaker 1: be to join the military, be the best you could be, 276 00:16:11,160 --> 00:16:13,480 Speaker 1: and now it's like, you know, join the military. We 277 00:16:13,520 --> 00:16:17,520 Speaker 1: have girls here too. Speaking of seeing is there was 278 00:16:17,560 --> 00:16:20,240 Speaker 1: there a sex discrimination with white women? 279 00:16:21,080 --> 00:16:25,880 Speaker 2: Yes, yes, well I mean I didn't see the data 280 00:16:26,160 --> 00:16:29,560 Speaker 2: like you know, inter wrapt sex by race, But what 281 00:16:29,680 --> 00:16:33,760 Speaker 2: I can say is that, uh, females or women are 282 00:16:34,280 --> 00:16:37,160 Speaker 2: definitely have It's not as much as the race preference. 283 00:16:37,200 --> 00:16:41,160 Speaker 2: It's maybe like you know, a fraction of difference given 284 00:16:41,280 --> 00:16:44,640 Speaker 2: to blacks, but women are definitely getting a leg up 285 00:16:45,240 --> 00:16:50,080 Speaker 2: in the admissions process, which is this piece wasn't about 286 00:16:50,120 --> 00:16:51,160 Speaker 2: gender discrimination, so this. 287 00:16:51,320 --> 00:16:54,800 Speaker 1: Wasn't the no. I just definitely I just got curious 288 00:16:54,920 --> 00:16:57,080 Speaker 1: reading the information. I just was like, I wonder if 289 00:16:57,120 --> 00:16:58,920 Speaker 1: this is only about white men and not white women 290 00:16:58,960 --> 00:17:01,240 Speaker 1: as well. But I didn't know, and I didn't see it. 291 00:17:01,320 --> 00:17:03,160 Speaker 1: I don't know if you had read it somewhere. 292 00:17:03,360 --> 00:17:06,120 Speaker 2: Yeah, well, I would imagine just based on the coefficient 293 00:17:06,480 --> 00:17:10,600 Speaker 2: that the disadvantage just for being white for but is 294 00:17:10,680 --> 00:17:13,960 Speaker 2: kind of offset for I mean, I guess what I'm 295 00:17:14,000 --> 00:17:18,040 Speaker 2: saying is that the white woman will get due to 296 00:17:18,119 --> 00:17:21,359 Speaker 2: their there's their sex, a boost over white men, but 297 00:17:21,640 --> 00:17:24,320 Speaker 2: probably not the boost that black women are getting, right, 298 00:17:24,680 --> 00:17:28,480 Speaker 2: I got you. You know, obviously the raw data, you know, 299 00:17:28,840 --> 00:17:32,640 Speaker 2: it's it's a privileged information, so you know, I can't 300 00:17:32,680 --> 00:17:35,560 Speaker 2: really run the analysis on that, but that would be 301 00:17:36,200 --> 00:17:38,359 Speaker 2: just looking at the coefficients and seeing uh you know, 302 00:17:38,520 --> 00:17:42,240 Speaker 2: the models. You know, that would be my prediction at least. 303 00:17:42,320 --> 00:17:45,520 Speaker 1: So all of this white man make up the majority 304 00:17:45,560 --> 00:17:50,560 Speaker 1: of military. And how did how does this affect recruitment? 305 00:17:53,280 --> 00:17:57,280 Speaker 2: Well that's the thing is that, uh you know, in 306 00:17:57,400 --> 00:18:00,960 Speaker 2: terms of this turning off recruitment, Well, there's a recruitment 307 00:18:01,040 --> 00:18:03,560 Speaker 2: I guess crisis across the board, or at least but 308 00:18:03,600 --> 00:18:04,600 Speaker 2: the recruitment. 309 00:18:04,240 --> 00:18:07,119 Speaker 1: Crisis across the board. People always sort of the recruitment crisis. 310 00:18:07,160 --> 00:18:09,920 Speaker 1: The recruitment crisis primarily during the Biden years was that 311 00:18:10,160 --> 00:18:11,800 Speaker 1: white men were not joining the military. 312 00:18:11,920 --> 00:18:12,320 Speaker 2: Absolutely. 313 00:18:12,359 --> 00:18:15,600 Speaker 1: Where the decline in military enrollment came from was not 314 00:18:15,720 --> 00:18:18,040 Speaker 1: among black or Hispanic men. It was among white men 315 00:18:18,359 --> 00:18:22,440 Speaker 1: who left joining the military and droves in a significant way. 316 00:18:23,680 --> 00:18:27,080 Speaker 2: Yeah, I mean, there is some evidence that some of 317 00:18:27,160 --> 00:18:31,120 Speaker 2: that is d I related. And what's interesting is one 318 00:18:31,160 --> 00:18:35,680 Speaker 2: of the their own studies that USNA produced during the report, 319 00:18:35,720 --> 00:18:40,399 Speaker 2: which is this climate study we're just looking at, you know, 320 00:18:41,160 --> 00:18:43,640 Speaker 2: it's it's kind of measuring that the temperature. In terms 321 00:18:43,680 --> 00:18:48,359 Speaker 2: of diversity climates, you know, is there focus on race? 322 00:18:48,600 --> 00:18:53,080 Speaker 2: Is there not enough focus on race in the uh, 323 00:18:53,480 --> 00:18:56,680 Speaker 2: you know, the worst diversity climates. You have a lot 324 00:18:56,760 --> 00:18:59,320 Speaker 2: of I mean it's not just whites but other whites, 325 00:18:59,480 --> 00:19:06,760 Speaker 2: I mean probably substantial number ones saying that there's too 326 00:19:06,840 --> 00:19:10,600 Speaker 2: much of an emphasis. And because that data is linked 327 00:19:10,680 --> 00:19:15,800 Speaker 2: to retention data subsequent retention, you could see that a 328 00:19:15,920 --> 00:19:19,280 Speaker 2: lot of whites are more likely to leave, and a 329 00:19:19,359 --> 00:19:21,960 Speaker 2: lot of those whites that leave are ones had said that, 330 00:19:22,240 --> 00:19:24,240 Speaker 2: you know, there's just too much of an emphasis on 331 00:19:25,160 --> 00:19:28,600 Speaker 2: race and DEI and diversity training and that what have you. 332 00:19:28,960 --> 00:19:31,240 Speaker 1: Because if this is about recruitment, then what does it 333 00:19:31,320 --> 00:19:36,520 Speaker 1: also say about people getting promotions in the military. Would 334 00:19:36,560 --> 00:19:39,440 Speaker 1: a white applicant be less inclined to get a promotion? 335 00:19:41,400 --> 00:19:47,120 Speaker 2: Well, here's the thing is that they're the affirmative action here, 336 00:19:47,119 --> 00:19:50,240 Speaker 2: at least as far as the Department of Defense is 337 00:19:50,280 --> 00:19:54,959 Speaker 2: willing to admit, occurs you know, at the front end, 338 00:19:55,119 --> 00:20:00,280 Speaker 2: you know, meaning at the admission gate. Now, once they 339 00:20:01,280 --> 00:20:06,359 Speaker 2: graduate in these academies, uh, and they they get assigned 340 00:20:06,600 --> 00:20:12,600 Speaker 2: to a naval community and they kind of enter the 341 00:20:12,680 --> 00:20:16,119 Speaker 2: officer pipeline. Now, what's really frustrated the Navy is that 342 00:20:16,880 --> 00:20:22,520 Speaker 2: the promotion rates among especially blacks, tends to be very 343 00:20:22,600 --> 00:20:25,800 Speaker 2: low at a certain above a certain rank. It just 344 00:20:25,840 --> 00:20:31,479 Speaker 2: stabates completely. Now that's not due to discrimination. I mean, 345 00:20:31,560 --> 00:20:35,159 Speaker 2: this has been studied. And the bottom line is is 346 00:20:35,240 --> 00:20:37,280 Speaker 2: that you know, and this is kind of what's interesting 347 00:20:37,320 --> 00:20:39,680 Speaker 2: about racial preferences, that they're kind of self defeating, or 348 00:20:39,760 --> 00:20:42,560 Speaker 2: at least they they're self restricting in the sense of 349 00:20:43,400 --> 00:20:45,960 Speaker 2: you know, being the ability to affect the bottom line here, 350 00:20:46,040 --> 00:20:50,560 Speaker 2: which is the diversity of the least diverse military, you know, 351 00:20:50,680 --> 00:20:56,600 Speaker 2: naval communities. And one of the strongest predictors of promotion 352 00:20:57,040 --> 00:20:59,720 Speaker 2: is how you did you know you're acting, your performance 353 00:21:00,080 --> 00:21:04,520 Speaker 2: the naval ga It's not something that people that decide 354 00:21:04,520 --> 00:21:10,000 Speaker 2: on promotions directly consider, but the traits associated with performance, 355 00:21:10,440 --> 00:21:14,080 Speaker 2: you know, your academic performance, your midlity. This is predictive 356 00:21:14,800 --> 00:21:23,240 Speaker 2: of promotion, and so racial preferences. I admitting lesser qualified 357 00:21:23,359 --> 00:21:26,359 Speaker 2: applicants setting them up cannot be promoted to have a 358 00:21:26,480 --> 00:21:30,000 Speaker 2: less likelihood of being promoted once they get to the 359 00:21:30,119 --> 00:21:35,000 Speaker 2: officer pipeline. Now their could in this kind of story 360 00:21:35,240 --> 00:21:38,760 Speaker 2: is that if they want, if they really want to 361 00:21:38,840 --> 00:21:41,200 Speaker 2: increase the diversity, you know, up and down the ranks, 362 00:21:41,800 --> 00:21:46,720 Speaker 2: they could lower the promotion standards or lower the accession 363 00:21:46,840 --> 00:21:52,800 Speaker 2: standards to certain elite naval communities, which produce an outside 364 00:21:52,880 --> 00:21:57,720 Speaker 2: share of the higher ranks, but obviously comes with risks. Says, 365 00:21:57,840 --> 00:22:01,000 Speaker 2: now you know you want willing to study? 366 00:22:01,680 --> 00:22:04,399 Speaker 1: If they're not willing to study, If diversity makes the 367 00:22:04,520 --> 00:22:08,440 Speaker 1: military better, what are the chances they've studied? If diversity 368 00:22:08,600 --> 00:22:13,200 Speaker 1: makes lowing of lowering the standards makes it. Word, don't 369 00:22:13,280 --> 00:22:14,840 Speaker 1: I don't know. I mean, I don't know that answer, 370 00:22:14,880 --> 00:22:17,240 Speaker 1: but that is this is the military. This is what 371 00:22:17,440 --> 00:22:20,560 Speaker 1: you're this is the area of the government that probably 372 00:22:20,600 --> 00:22:23,800 Speaker 1: has the most amount of trust of anyone else. 373 00:22:24,440 --> 00:22:29,240 Speaker 2: Yeah, and that's it's worrisome how the extent that the 374 00:22:29,359 --> 00:22:32,480 Speaker 2: military and I just this is kind of fresh off 375 00:22:32,520 --> 00:22:35,840 Speaker 2: the press because I just spent last night I life 376 00:22:35,960 --> 00:22:41,960 Speaker 2: just looking at every single uh National Defense Appropriation Act, 377 00:22:43,000 --> 00:22:45,840 Speaker 2: which started in May seven. Before then it was the 378 00:22:45,920 --> 00:22:49,920 Speaker 2: Defense of Appropriations anyways, and you look through that and 379 00:22:50,160 --> 00:22:52,880 Speaker 2: it's just clear over time that the extent that it's 380 00:22:53,080 --> 00:22:57,560 Speaker 2: becoming a vehicle for advancing DEI. No, it's mostly it 381 00:22:57,600 --> 00:23:02,440 Speaker 2: eventually started, Okay, we're just funning missiles, new airplanes, and 382 00:23:02,680 --> 00:23:09,040 Speaker 2: over time it's kind of been commandeered. I don't want 383 00:23:09,080 --> 00:23:11,080 Speaker 2: to just single out members of the Black Caucus, but 384 00:23:11,560 --> 00:23:14,879 Speaker 2: they're they're definitely very active on inserting UH, you know, 385 00:23:15,080 --> 00:23:18,800 Speaker 2: DEI related provisions of the bill. It started with contracting 386 00:23:19,600 --> 00:23:23,240 Speaker 2: in the early nineties and support early colleges, and then 387 00:23:23,320 --> 00:23:26,360 Speaker 2: the two thousands you start to see a real entrenchment 388 00:23:26,480 --> 00:23:30,639 Speaker 2: and institutional than of the DEI, especially over the past 389 00:23:31,040 --> 00:23:39,440 Speaker 2: ten years. And that's relevant because when defending these preferences 390 00:23:39,480 --> 00:23:42,920 Speaker 2: are defending or actually the ruling in favor of the government. 391 00:23:43,680 --> 00:23:48,080 Speaker 2: The judge in the case, Richard D. Bennett, he cited 392 00:23:48,680 --> 00:23:51,640 Speaker 2: a lot of the these so called commissions that came 393 00:23:51,680 --> 00:23:54,159 Speaker 2: out of these d these diversity commission reports that came 394 00:23:54,200 --> 00:23:57,240 Speaker 2: out of these NDA permissions that were stuck in. You know, 395 00:23:57,280 --> 00:24:00,679 Speaker 2: build a military Leadership Diversity commission to make recommendation, how 396 00:24:00,720 --> 00:24:04,840 Speaker 2: do we diversify? So the judge said, of that, like, see, 397 00:24:04,960 --> 00:24:09,919 Speaker 2: this has bipartisan consensus. You know, everybody believes that. Now. 398 00:24:10,040 --> 00:24:13,760 Speaker 2: You know, diversity is a national security imperative. Republic ANAVI 399 00:24:13,960 --> 00:24:16,960 Speaker 2: is endorsed by Congress, and it's like, no, this commission, 400 00:24:17,000 --> 00:24:19,520 Speaker 2: this report that you speak to was snuck in by 401 00:24:20,560 --> 00:24:24,480 Speaker 2: Ben Cardon of Maryland with the support of a couple 402 00:24:24,560 --> 00:24:27,600 Speaker 2: members of the Black Caucus. You know, most people. 403 00:24:28,160 --> 00:24:30,480 Speaker 1: One time I was when I was in a green 404 00:24:30,600 --> 00:24:32,719 Speaker 1: room for a television show. I was doing a new 405 00:24:32,760 --> 00:24:36,040 Speaker 1: show and I was with a military combat bet colonel 406 00:24:36,240 --> 00:24:38,600 Speaker 1: who said to me, and he said, one of the 407 00:24:38,680 --> 00:24:41,000 Speaker 1: most there was something I've never looked into us. I 408 00:24:41,040 --> 00:24:42,200 Speaker 1: don't know if it's true. I don't know if I 409 00:24:42,280 --> 00:24:43,880 Speaker 1: believe it, but he said to me. Something that's stuck 410 00:24:43,920 --> 00:24:46,240 Speaker 1: with me since he has said it. He said, most 411 00:24:46,320 --> 00:24:49,240 Speaker 1: parts of the military are just a jobs program that 412 00:24:49,359 --> 00:24:51,840 Speaker 1: we do not need the military size or force that 413 00:24:52,040 --> 00:24:54,720 Speaker 1: not forced, but the size that we do of standing army, 414 00:24:54,840 --> 00:24:58,119 Speaker 1: because it is just specifically for jobs we don't like. 415 00:24:58,480 --> 00:25:00,760 Speaker 1: He said, most people will never see combat, will never 416 00:25:00,880 --> 00:25:04,639 Speaker 1: do anything to promote national security. It is just to 417 00:25:04,760 --> 00:25:07,679 Speaker 1: higher people. And I've always thought of that whenever these 418 00:25:07,720 --> 00:25:10,639 Speaker 1: conversations come up of was he right. I don't know 419 00:25:10,760 --> 00:25:13,000 Speaker 1: the answer to it, but I think about it consotly. 420 00:25:13,720 --> 00:25:16,200 Speaker 1: Is now, this is the federal government making this kind 421 00:25:16,240 --> 00:25:20,600 Speaker 1: of DEI decision. And this predates Biden, is clear? 422 00:25:21,480 --> 00:25:24,959 Speaker 2: Yeah, I mean the racial preferences, Yes, they do predate Biden. 423 00:25:25,200 --> 00:25:27,080 Speaker 1: Yeah, this wasn't a new thing. 424 00:25:28,280 --> 00:25:31,840 Speaker 2: Yeah, listen. And this is what's incredible is that even 425 00:25:32,000 --> 00:25:36,440 Speaker 2: during the Trump one, you know, the first Trump term, Yeah, 426 00:25:37,400 --> 00:25:41,040 Speaker 2: I mean Trump's second is Mark Esper. This is the one. 427 00:25:41,080 --> 00:25:45,120 Speaker 2: A few weeks after Florida incidents, he produced a memorandum saying, 428 00:25:45,600 --> 00:25:48,920 Speaker 2: we need to establish a diversity Inclusion Board within the Pentagon. 429 00:25:49,080 --> 00:25:51,280 Speaker 2: You know that is going to make recommendations about how 430 00:25:51,359 --> 00:25:52,840 Speaker 2: to increase the diversity inc. 431 00:25:53,600 --> 00:25:55,600 Speaker 1: I actually never heard of that before. That is I, 432 00:25:55,880 --> 00:25:59,879 Speaker 1: I don't believe you. I did not know that that happened. 433 00:26:00,760 --> 00:26:03,639 Speaker 2: That Trump had no idea or just maybe his priority 434 00:26:03,680 --> 00:26:06,560 Speaker 2: he is focusing on other things the time. It was 435 00:26:06,640 --> 00:26:07,760 Speaker 2: a hot moment. 436 00:26:07,920 --> 00:26:12,560 Speaker 1: Yeah we had COVID going on in re election yet Yeah, 437 00:26:12,760 --> 00:26:14,119 Speaker 1: but like, uh, you know in. 438 00:26:14,160 --> 00:26:16,280 Speaker 2: High tech there's a lot of things going on, even 439 00:26:16,320 --> 00:26:20,440 Speaker 2: among Republican presidents. And part of the reason is is 440 00:26:20,520 --> 00:26:23,840 Speaker 2: because you know, they don't call it discrimination against whites, 441 00:26:23,960 --> 00:26:26,240 Speaker 2: or they don't call or you know, or they don't 442 00:26:26,280 --> 00:26:29,199 Speaker 2: say racial preferences specifically, they kind of you know, use 443 00:26:29,320 --> 00:26:31,800 Speaker 2: very laundered language, you know, and some of these bills 444 00:26:31,920 --> 00:26:33,600 Speaker 2: that make this seem like it's all about that is 445 00:26:34,200 --> 00:26:35,320 Speaker 2: the functrating. 446 00:26:34,880 --> 00:26:40,080 Speaker 1: Thing about di conversations is like, I have no opposition 447 00:26:40,440 --> 00:26:43,159 Speaker 1: for funds that go for poor people, whether they are 448 00:26:43,440 --> 00:26:46,560 Speaker 1: black or Hispanic or white. But that is not what 449 00:26:46,760 --> 00:26:52,200 Speaker 1: DEI is. DEI is anti white discrimination primarily. That's really 450 00:26:52,400 --> 00:26:55,520 Speaker 1: what DEI is. It's not it's not, oh, we give 451 00:26:55,680 --> 00:26:58,520 Speaker 1: extra money to poor black kids in this area of 452 00:26:58,560 --> 00:27:00,359 Speaker 1: the country or that of the country that I don't 453 00:27:00,400 --> 00:27:03,960 Speaker 1: care about, no one does. It's when you purposely discriminate 454 00:27:04,080 --> 00:27:08,160 Speaker 1: or lower standards in order to prevent one group from 455 00:27:08,200 --> 00:27:09,959 Speaker 1: succeeding in order for another group to succeed. 456 00:27:11,080 --> 00:27:14,479 Speaker 2: Yeah, and to your point, I mean this came up 457 00:27:14,520 --> 00:27:18,400 Speaker 2: in the case. You know, it's because obviously, in order 458 00:27:18,520 --> 00:27:23,199 Speaker 2: to pass the narrowly tailored standard of strict scrutiny, they 459 00:27:23,520 --> 00:27:25,200 Speaker 2: have to show that Navy has to show that they've 460 00:27:25,240 --> 00:27:29,080 Speaker 2: exhausted all race neutral alternatives, and one race neutral alternative 461 00:27:29,119 --> 00:27:34,240 Speaker 2: alternative is socioeconomic preferences, you know, just giving a leg 462 00:27:34,359 --> 00:27:38,800 Speaker 2: up for students were regardless of the race, and you know, 463 00:27:40,240 --> 00:27:42,840 Speaker 2: the deed of emissions, you know, was asked why they 464 00:27:43,000 --> 00:27:49,440 Speaker 2: didn't utilize sees space preferences instead, and his response was really, well, 465 00:27:49,520 --> 00:27:54,960 Speaker 2: just remarkable, remarkably, I guess, just really disgusting. He said that, well, 466 00:27:55,920 --> 00:27:58,040 Speaker 2: we determined that if we did that, there'd be a 467 00:27:58,119 --> 00:28:01,680 Speaker 2: lot of quote unquote non diverse applicants who would also benefit, 468 00:28:02,560 --> 00:28:05,040 Speaker 2: which is translation, you know, there'd be too many poor 469 00:28:06,119 --> 00:28:06,879 Speaker 2: white applicants. 470 00:28:08,119 --> 00:28:10,359 Speaker 1: Is that happened North Carolina when they were trying to 471 00:28:10,440 --> 00:28:12,960 Speaker 1: make the univers system less DEI and they said let's 472 00:28:12,960 --> 00:28:14,480 Speaker 1: just do it by income. They said, well, there's a 473 00:28:14,520 --> 00:28:16,239 Speaker 1: lot of poor whites that would benefit from it too. 474 00:28:17,040 --> 00:28:19,880 Speaker 1: My last question is do you believe there's any other 475 00:28:20,000 --> 00:28:21,840 Speaker 1: areas of the thorough government that are doing this exact 476 00:28:21,880 --> 00:28:22,719 Speaker 1: same thing right now? 477 00:28:24,119 --> 00:28:28,160 Speaker 2: I believe every other I mean, the other firm service 478 00:28:28,200 --> 00:28:31,440 Speaker 2: academy cases are are still well, they might be headed 479 00:28:31,640 --> 00:28:35,000 Speaker 2: for mooded cases, but I know they're they're probably going 480 00:28:35,080 --> 00:28:38,400 Speaker 2: on there. In terms of other areas of the federal government, 481 00:28:40,400 --> 00:28:43,480 Speaker 2: it wouldn't surprise me. I mean, I only know the 482 00:28:43,560 --> 00:28:47,440 Speaker 2: data related to this case. But what is particularly shocking 483 00:28:47,560 --> 00:28:51,160 Speaker 2: and which this ruling in this case upholds essentially, is 484 00:28:51,240 --> 00:28:54,880 Speaker 2: that the government, if they just wants to support or 485 00:28:54,920 --> 00:28:59,520 Speaker 2: advance a DII effort, you know, a discriminatory policy, essentially, 486 00:28:59,560 --> 00:29:02,520 Speaker 2: the judge it just allows so that they only have 487 00:29:02,600 --> 00:29:06,160 Speaker 2: to invoke national security, you know, right, because the judge 488 00:29:06,240 --> 00:29:11,000 Speaker 2: really just completely total competes relates into the government interests. 489 00:29:11,000 --> 00:29:12,320 Speaker 2: You know, it's just national security. 490 00:29:13,840 --> 00:29:16,200 Speaker 1: With everything. Is that the say national security enough times, 491 00:29:16,240 --> 00:29:17,960 Speaker 1: you kind of get your way unless you're rually gelling 492 00:29:17,960 --> 00:29:19,680 Speaker 1: a rank or president you say nine eleven enough times, 493 00:29:19,680 --> 00:29:21,720 Speaker 1: you don't get your way, but otherwise you do get 494 00:29:21,760 --> 00:29:22,160 Speaker 1: your way. 495 00:29:22,960 --> 00:29:27,600 Speaker 2: Yes, So because of this president, you can imagine featured 496 00:29:27,600 --> 00:29:33,600 Speaker 2: administrations justifying you know, such preferences or policies in other 497 00:29:34,440 --> 00:29:39,240 Speaker 2: domains government if it's not already going on, you know, 498 00:29:39,360 --> 00:29:41,640 Speaker 2: and they could just evoke national security and come up 499 00:29:41,680 --> 00:29:44,840 Speaker 2: with all some sort of talking baby rationale of how 500 00:29:44,960 --> 00:29:48,400 Speaker 2: this relates, you know, by furthering diversity. This furthers our 501 00:29:48,440 --> 00:29:51,600 Speaker 2: bottom line in terms of this compelling national goal. 502 00:29:52,080 --> 00:29:56,240 Speaker 1: Yeah, you are absolutely, Can I say brilliant I have 503 00:29:56,400 --> 00:30:01,120 Speaker 1: been people probably have read Zach's work without knowing it. 504 00:30:01,320 --> 00:30:06,680 Speaker 1: Zach that study about the mainstream media's application of woke terminology, 505 00:30:06,920 --> 00:30:08,720 Speaker 1: that was Zach Like. There were so many times where 506 00:30:08,760 --> 00:30:12,200 Speaker 1: I'm like, oh, Zach's works being cited again over and 507 00:30:12,320 --> 00:30:16,800 Speaker 1: over and over again. You really deserve way more credit 508 00:30:17,040 --> 00:30:19,360 Speaker 1: than you have given so far because your work really 509 00:30:19,560 --> 00:30:22,920 Speaker 1: is top tier. And this this fun thing you've you've 510 00:30:22,960 --> 00:30:27,240 Speaker 1: done on the Naval Academy is it is a tour 511 00:30:27,320 --> 00:30:30,920 Speaker 1: de force. Really what is brilliant and it is on yourself. 512 00:30:31,040 --> 00:30:32,640 Speaker 1: Where can people go to get your stuff? 513 00:30:33,920 --> 00:30:36,920 Speaker 2: Well, my substract is now used to be Zach's newsletter. 514 00:30:37,080 --> 00:30:42,320 Speaker 2: Now it's called Unwoke by the Numbers hopefully catchy enough. 515 00:30:42,280 --> 00:30:45,640 Speaker 1: To remember Unwoke by the Numbers or. 516 00:30:45,640 --> 00:30:48,960 Speaker 2: Maybe Zach's snowsletter is easier. But anyways, one woke by 517 00:30:49,000 --> 00:30:55,200 Speaker 2: the numbers Twitter handle isa G nine three. 518 00:30:55,160 --> 00:30:58,560 Speaker 1: Two that should be shortened down, but as that's the 519 00:30:59,240 --> 00:31:04,000 Speaker 1: nine to Zach zac Ch and you can find your 520 00:31:04,160 --> 00:31:09,640 Speaker 1: substack Zach, Zach zac h remember the age, Zachgoldberg dot 521 00:31:09,760 --> 00:31:13,520 Speaker 1: substack dot dot com. I also dot Hepa dot com, 522 00:31:13,840 --> 00:31:17,720 Speaker 1: Zach Goldberg dot substock dot com. Zach, You're this is 523 00:31:18,000 --> 00:31:20,920 Speaker 1: incredible work. If you produce something else, I'd love to 524 00:31:20,960 --> 00:31:25,080 Speaker 1: have you on this podcast again. You're just I mean, brilliant, brilliant, brilliance. 525 00:31:25,120 --> 00:31:26,280 Speaker 2: You want to say one final thing? 526 00:31:26,440 --> 00:31:26,880 Speaker 1: Just sure? 527 00:31:27,360 --> 00:31:28,960 Speaker 2: I mean, first of all, thanks for having me on. 528 00:31:29,120 --> 00:31:33,160 Speaker 2: Because the current moment, everybody's focused on time of seaportations, 529 00:31:33,200 --> 00:31:35,320 Speaker 2: what have you. You know, it's hard to get through 530 00:31:35,440 --> 00:31:39,160 Speaker 2: about the story are now I mean, wasn't great, but uh, 531 00:31:39,280 --> 00:31:43,720 Speaker 2: you know, everybody celebrating the post opening is premature. All 532 00:31:43,800 --> 00:31:46,280 Speaker 2: these policies are going to easily be snapped back in place, 533 00:31:46,360 --> 00:31:48,920 Speaker 2: so it's important. And that's what I'm when I get 534 00:31:48,920 --> 00:31:50,400 Speaker 2: off a few of them, to have to be talking 535 00:31:50,440 --> 00:31:54,440 Speaker 2: with somebody who's trying to insert a racial preferences. Ben 536 00:31:54,560 --> 00:31:58,240 Speaker 2: and Service academies into the twenty twenty six National Defense 537 00:31:58,240 --> 00:32:00,600 Speaker 2: Appropriation Acts. That'd be great be able to come on 538 00:32:00,680 --> 00:32:01,840 Speaker 2: the show and talk more about that. 539 00:32:02,280 --> 00:32:04,560 Speaker 1: I should introduce you a few Congressmen and Senators that 540 00:32:04,640 --> 00:32:06,880 Speaker 1: I know and try to get that going because. 541 00:32:06,760 --> 00:32:07,560 Speaker 2: This would be awesome. 542 00:32:07,920 --> 00:32:10,360 Speaker 1: Really at Gilbert, thank you for being here. I really 543 00:32:10,480 --> 00:32:13,760 Speaker 1: really appreciate it. Thanks you having you're listening to It's 544 00:32:13,760 --> 00:32:16,160 Speaker 1: The Numbers Game with Ryan Gerdowski. We'll be right back 545 00:32:16,200 --> 00:32:22,520 Speaker 1: after this message for the Ask Me Anything segment where 546 00:32:22,560 --> 00:32:24,360 Speaker 1: you can email me about any somebody that I know 547 00:32:24,400 --> 00:32:27,320 Speaker 1: the matter, I know about, I know anything degree of 548 00:32:27,400 --> 00:32:29,440 Speaker 1: information about, or I could do the research for. Please 549 00:32:29,480 --> 00:32:31,320 Speaker 1: don't email it with sports because I don't know much 550 00:32:31,320 --> 00:32:34,320 Speaker 1: about it, but anything else email me Ryan at Numbers 551 00:32:34,400 --> 00:32:38,280 Speaker 1: Gamepodcast dot com. That is Ryan at Numbers Game Podcast 552 00:32:38,520 --> 00:32:41,920 Speaker 1: plural Numbers Numbers Gamepodcast dot com. I really look forward 553 00:32:41,960 --> 00:32:45,120 Speaker 1: it really helps his podcast by submitting questions on anything. 554 00:32:45,320 --> 00:32:48,040 Speaker 1: I got a question from a guy named Sam today 555 00:32:48,080 --> 00:32:50,600 Speaker 1: who said, who do you think is the worst member 556 00:32:50,680 --> 00:32:53,880 Speaker 1: of worst Republican member of Congress. This is the easiest, 557 00:32:54,680 --> 00:32:56,840 Speaker 1: easiest question in the world to answer. It is Maria 558 00:32:56,920 --> 00:33:01,840 Speaker 1: Salazar from Florida's I think thirty six district thirty seventh 559 00:33:01,880 --> 00:33:04,480 Speaker 1: district one of the two. I didn't look that up 560 00:33:04,480 --> 00:33:04,880 Speaker 1: before him. 561 00:33:04,920 --> 00:33:05,320 Speaker 2: I should have. 562 00:33:05,560 --> 00:33:11,280 Speaker 1: Marie Salazar is a former television host who very very 563 00:33:11,440 --> 00:33:13,840 Speaker 1: very well and it's forgotten now, but it did happen 564 00:33:13,880 --> 00:33:16,120 Speaker 1: a lot during the twenty sixteen election. She was kind 565 00:33:16,120 --> 00:33:18,920 Speaker 1: of always attacking Trump and saying that she was slated 566 00:33:18,960 --> 00:33:21,080 Speaker 1: to have interviews and he canceled because he was afraid 567 00:33:21,080 --> 00:33:22,440 Speaker 1: of her. And I think they did sit down at 568 00:33:22,480 --> 00:33:24,440 Speaker 1: one point, but she was on Bill O'Reilly at the time, 569 00:33:24,640 --> 00:33:29,720 Speaker 1: bashing Trump. Anyway, she ran for office in twenty twenty 570 00:33:29,920 --> 00:33:33,240 Speaker 1: and one. She flipped a Democratic seat, which many people 571 00:33:33,320 --> 00:33:35,520 Speaker 1: did not believe that was going to be Republican. But 572 00:33:35,640 --> 00:33:38,200 Speaker 1: as the coalitions has changed and as her district, which 573 00:33:38,200 --> 00:33:40,959 Speaker 1: is very heavily Hispanic has become more Republican, it's now 574 00:33:41,000 --> 00:33:43,080 Speaker 1: a super Republican seat. It's a seat that Trump won 575 00:33:43,120 --> 00:33:45,680 Speaker 1: by double digits, and this used to be a Democratic seat. 576 00:33:45,880 --> 00:33:49,080 Speaker 1: It is in South Florida, is in Miami proper, and 577 00:33:49,280 --> 00:33:51,600 Speaker 1: she is represented now. She had won her last election 578 00:33:51,720 --> 00:33:54,360 Speaker 1: in a landslide. This one wakes up morning, noon, and 579 00:33:54,480 --> 00:33:57,880 Speaker 1: night thinking about amnesty for illegal aliens, how to increase 580 00:33:57,960 --> 00:34:01,360 Speaker 1: legal immigration, how to give to make sure people aren't deported. 581 00:34:01,880 --> 00:34:05,000 Speaker 1: She's horrible. She's horrible on the issue. She's actually not 582 00:34:05,120 --> 00:34:08,200 Speaker 1: been super loud lately. But I have a story when 583 00:34:08,920 --> 00:34:12,320 Speaker 1: the last Congress was going on and Trump wasn't in office. 584 00:34:12,360 --> 00:34:16,000 Speaker 1: This is when Biden was in office, Stephen Miller, Trump's advisor, 585 00:34:16,040 --> 00:34:18,880 Speaker 1: Steven Miller, was giving a presentation. I heard this remember 586 00:34:18,920 --> 00:34:20,520 Speaker 1: of Congress, so I wasn't in the room, but this 587 00:34:20,600 --> 00:34:22,520 Speaker 1: came from a good source. Steven Miller was giving a 588 00:34:22,600 --> 00:34:25,600 Speaker 1: presentation and Maria Salis are attended, even though she was 589 00:34:25,640 --> 00:34:28,720 Speaker 1: not a member of the Republican Study Committee, and began 590 00:34:28,880 --> 00:34:32,759 Speaker 1: yelling and berating him about hating Hispanics and hating Latinos 591 00:34:32,800 --> 00:34:36,680 Speaker 1: and making all these attack and accusations against him. And 592 00:34:36,920 --> 00:34:38,880 Speaker 1: apparently he handled it very very well. He was a 593 00:34:38,920 --> 00:34:41,759 Speaker 1: perfect gentleman, calmed her down. She left the room and 594 00:34:41,880 --> 00:34:46,760 Speaker 1: immediately started calling reporters and bashing the Republican Study Committee 595 00:34:46,760 --> 00:34:49,759 Speaker 1: and Stephen Miller. There is a number of times where 596 00:34:49,880 --> 00:34:52,319 Speaker 1: people I know have been around her and she has 597 00:34:52,840 --> 00:34:55,960 Speaker 1: shown signs of being very unhinged, very crazy, and her 598 00:34:56,040 --> 00:34:59,640 Speaker 1: policies are terrible, and if there was a Republican primary her, 599 00:35:00,160 --> 00:35:02,200 Speaker 1: it's worth it because her seat is a super safe 600 00:35:02,239 --> 00:35:05,719 Speaker 1: Republican seat now, unlike more moderate members. So I don't 601 00:35:05,719 --> 00:35:08,640 Speaker 1: necessarily always agree with Brian Fitzpatrick. He still represents a 602 00:35:08,719 --> 00:35:10,960 Speaker 1: Kamala Harris district, although it has moved to the right. 603 00:35:11,320 --> 00:35:12,719 Speaker 1: He's fine for the seat that he holds. I have 604 00:35:12,840 --> 00:35:16,120 Speaker 1: no issue with a moderate Republican holding a holding a seat. 605 00:35:16,200 --> 00:35:18,839 Speaker 1: Murkowski is not great. Murkowski is a perfect example. He's 606 00:35:18,920 --> 00:35:22,520 Speaker 1: Murkowski from Alaska who begs Trump for things for Alaska. 607 00:35:22,600 --> 00:35:23,920 Speaker 1: He always gives it, and then she goes to the 608 00:35:23,960 --> 00:35:27,000 Speaker 1: media and attacks him. Maria Salazar is out there for 609 00:35:27,080 --> 00:35:30,960 Speaker 1: illegal immigrants. That's her prime constituency are non citizens. So 610 00:35:31,600 --> 00:35:33,440 Speaker 1: have a big problem with that. And if there's any 611 00:35:33,520 --> 00:35:35,960 Speaker 1: Republican who should not be in office in the House 612 00:35:36,040 --> 00:35:39,600 Speaker 1: Representatives is Maria Salazar from Florida. Thank you again for 613 00:35:39,719 --> 00:35:42,440 Speaker 1: listening for this episode of A numbers Game. Please like 614 00:35:42,520 --> 00:35:45,680 Speaker 1: and subscribe on the iHeartRadio app, Apple podcast, wherever you 615 00:35:45,719 --> 00:35:48,239 Speaker 1: get your podcast. We'll see you on Thursday. Thank you 616 00:35:48,360 --> 00:35:48,719 Speaker 1: so much.