1 00:00:11,697 --> 00:00:14,977 Speaker 1: You're listening to The Buck Sexton Show podcast, make sure 2 00:00:14,977 --> 00:00:18,017 Speaker 1: you subscribe to the podcast on the iHeartRadio app or 3 00:00:18,017 --> 00:00:22,577 Speaker 1: wherever you get your podcasts. Team. Welcome to the Freedom 4 00:00:22,617 --> 00:00:27,377 Speaker 1: on Thursday, May twelfth edition of the program. I'm heading 5 00:00:27,417 --> 00:00:31,057 Speaker 1: to Chicago, so I'll be out tomorrow and it'll be 6 00:00:31,097 --> 00:00:33,097 Speaker 1: the first time I've ever been in Chicago, so I'm 7 00:00:33,257 --> 00:00:36,817 Speaker 1: kind of excited and looking forward to a weekend there. 8 00:00:36,857 --> 00:00:41,737 Speaker 1: But we'll talk about the situation of crime and Black 9 00:00:41,777 --> 00:00:46,537 Speaker 1: Lives Matter and why this is on my mind right now, 10 00:00:47,257 --> 00:00:51,777 Speaker 1: the Black Lives Matter movement, the aftermath of it, and 11 00:00:51,897 --> 00:00:55,177 Speaker 1: the data, the statistics about crime in Chicago, how all 12 00:00:55,177 --> 00:00:59,777 Speaker 1: of this is related. And there's actually a fascinating sub 13 00:00:59,817 --> 00:01:04,217 Speaker 1: stack from Barry Weiss where a guest writer deals with 14 00:01:04,257 --> 00:01:07,297 Speaker 1: this exact issue of why we're in the crime situation 15 00:01:07,337 --> 00:01:10,297 Speaker 1: room right now, and I will address that with you 16 00:01:10,377 --> 00:01:13,417 Speaker 1: here in just a moment. But I gotta tell you, 17 00:01:13,457 --> 00:01:17,457 Speaker 1: if you've ever considered becoming a real estate investor, go 18 00:01:17,617 --> 00:01:19,497 Speaker 1: check out my friends that done for your real estate. 19 00:01:19,537 --> 00:01:22,777 Speaker 1: They take you through every step of the process from 20 00:01:22,857 --> 00:01:25,777 Speaker 1: beginning to end, from figuring out what city you want 21 00:01:25,777 --> 00:01:29,097 Speaker 1: to invest in, what property, getting a loan, getting a 22 00:01:29,137 --> 00:01:32,177 Speaker 1: management company, tenant in place, and then free cash will 23 00:01:32,177 --> 00:01:34,097 Speaker 1: come to you every month as you build up equity 24 00:01:34,137 --> 00:01:36,897 Speaker 1: in that house over time, and if you pick the 25 00:01:36,977 --> 00:01:39,777 Speaker 1: right market the right house, you could see you dramatic 26 00:01:39,977 --> 00:01:42,857 Speaker 1: gains over a period of time. This is slow and steady. 27 00:01:42,897 --> 00:01:46,177 Speaker 1: This is not fast, fly by night stuff. This is 28 00:01:46,257 --> 00:01:49,657 Speaker 1: preparing for the long term making smart real estate decisions. 29 00:01:49,697 --> 00:01:51,657 Speaker 1: That's what Done for Your Real Estate teaches you to do. 30 00:01:52,057 --> 00:01:54,537 Speaker 1: Go to Done for You Buck dot com to see 31 00:01:54,577 --> 00:01:58,297 Speaker 1: how this works. That's done for You Buck dot com. 32 00:01:58,817 --> 00:02:02,377 Speaker 1: I have bought multiple properties with them personally, and I'm 33 00:02:02,377 --> 00:02:05,417 Speaker 1: going to be buying more properties with the advice of 34 00:02:05,497 --> 00:02:07,177 Speaker 1: Done for Your real Estate could have Done for you 35 00:02:07,897 --> 00:02:11,697 Speaker 1: Buck dot com. So I'm heading a chego and people 36 00:02:11,737 --> 00:02:14,897 Speaker 1: are asking me why. I say, well, it's because I 37 00:02:14,977 --> 00:02:18,377 Speaker 1: have never been and I'm forty years old and I've 38 00:02:18,417 --> 00:02:23,377 Speaker 1: been to Baghdad and Cobble and all over Europe and 39 00:02:23,417 --> 00:02:27,697 Speaker 1: into South and Central America and over to Southeast Asia, 40 00:02:27,817 --> 00:02:30,377 Speaker 1: and I've never been to Chicago. So I just kind 41 00:02:30,377 --> 00:02:32,377 Speaker 1: of felt like, you know what, I want to go 42 00:02:32,457 --> 00:02:34,257 Speaker 1: check this place out. I want to have my first 43 00:02:34,257 --> 00:02:37,457 Speaker 1: experience there. And one of the fun things about being 44 00:02:37,457 --> 00:02:39,657 Speaker 1: able to reach so many folks on social media is 45 00:02:39,697 --> 00:02:42,697 Speaker 1: that when you have a new experience you're going to 46 00:02:42,777 --> 00:02:44,657 Speaker 1: have in a city, in a place, you say, hey, 47 00:02:44,697 --> 00:02:48,297 Speaker 1: tell me about tell me about what I should do 48 00:02:48,337 --> 00:02:50,497 Speaker 1: when I'm there. You know, what do you recommend? What 49 00:02:50,537 --> 00:02:54,257 Speaker 1: do you think is a good first weekend in Chicago? 50 00:02:54,297 --> 00:02:56,417 Speaker 1: I tinderate people are talking about going to the river 51 00:02:56,897 --> 00:02:59,617 Speaker 1: and being on one of those boats. A lot of 52 00:02:59,697 --> 00:03:03,537 Speaker 1: pizza suggestions. I mean, I have Celiac disease, so that's 53 00:03:03,577 --> 00:03:05,857 Speaker 1: not really gonna help me very much, but it's a 54 00:03:05,937 --> 00:03:09,497 Speaker 1: nice idea. I appreciate it. And some steakhouses that's definitely 55 00:03:09,577 --> 00:03:12,617 Speaker 1: my jams. Stuff like that, right. I can't tell you, though, 56 00:03:12,617 --> 00:03:15,737 Speaker 1: how many people would I ask for advice on what 57 00:03:15,777 --> 00:03:19,977 Speaker 1: to do in Chicago? Make some comment, have some comment 58 00:03:20,057 --> 00:03:23,897 Speaker 1: about how keep your head down, were a bulletproof vest, 59 00:03:24,097 --> 00:03:26,617 Speaker 1: you better be armed. You know. There's a lot of 60 00:03:26,657 --> 00:03:29,897 Speaker 1: that going on, and I gotta tell you it's sad 61 00:03:29,937 --> 00:03:31,977 Speaker 1: when you think about it. I mean, as just as 62 00:03:31,977 --> 00:03:34,977 Speaker 1: an American, I've never even been there, but the fact 63 00:03:34,977 --> 00:03:36,737 Speaker 1: that this is what and a lot of them are 64 00:03:36,817 --> 00:03:41,777 Speaker 1: Chicago residents, and this is the third largest city in America. 65 00:03:42,337 --> 00:03:44,377 Speaker 1: What is it about two and a half million people, 66 00:03:45,337 --> 00:03:49,857 Speaker 1: and the commentary about the city isn't Oh, it's amazing. 67 00:03:49,857 --> 00:03:53,657 Speaker 1: I mean, if you ask a Frenchman, particularly a Parisian, 68 00:03:54,337 --> 00:03:56,257 Speaker 1: about Paris, they're going to tell you that it's the 69 00:03:56,297 --> 00:03:58,937 Speaker 1: greatest city in the world. You ask a Londoner about London, 70 00:03:58,977 --> 00:04:00,537 Speaker 1: they're gonna tell you that it's the greatest city in 71 00:04:00,537 --> 00:04:03,577 Speaker 1: the world. I've asked some Chicago in now. Granted a 72 00:04:03,617 --> 00:04:05,417 Speaker 1: lot of them are not from Chicago. The people are 73 00:04:05,417 --> 00:04:08,137 Speaker 1: all across the US, but a lot of them are 74 00:04:08,257 --> 00:04:13,097 Speaker 1: Chicago natives. I've asked them and they're making they're making 75 00:04:13,137 --> 00:04:17,017 Speaker 1: comments about the shootings and about the violence that's going 76 00:04:17,017 --> 00:04:20,457 Speaker 1: on in that city, and you say, well, that's not 77 00:04:20,497 --> 00:04:22,537 Speaker 1: the way it's supposed to be nine one hundred and 78 00:04:22,537 --> 00:04:25,057 Speaker 1: one people through the eighth of May, so it's over 79 00:04:25,217 --> 00:04:28,297 Speaker 1: over nine hundred and one people right now have been shot, 80 00:04:28,737 --> 00:04:31,657 Speaker 1: one hundred and seventy three of them fatally. So we're 81 00:04:31,737 --> 00:04:34,057 Speaker 1: not even halfway through the year and there have been 82 00:04:34,137 --> 00:04:39,937 Speaker 1: nine hundred people shot in the city of Chicago. That's appalling. 83 00:04:40,417 --> 00:04:43,377 Speaker 1: How do we get to this place? Why are there 84 00:04:43,417 --> 00:04:46,897 Speaker 1: so many shootings in the city of Chicago, you can see, 85 00:04:46,897 --> 00:04:49,777 Speaker 1: I mean how many you know, by the way, this 86 00:04:49,817 --> 00:04:51,297 Speaker 1: is true all across the country. It's not just a 87 00:04:51,657 --> 00:04:54,777 Speaker 1: Chicago thing. As we know, there's crime rising, particularly violent 88 00:04:54,817 --> 00:04:56,977 Speaker 1: crime and shootings has been rising in major US cities. 89 00:04:57,497 --> 00:05:00,777 Speaker 1: Why is this going on? What is the circumstance, what's 90 00:05:00,777 --> 00:05:07,217 Speaker 1: the situation that brings me to the Black Lives Matter movement? 91 00:05:07,737 --> 00:05:10,577 Speaker 1: And what happened in twenty twenty. And I'll tell you 92 00:05:10,617 --> 00:05:16,177 Speaker 1: this story through the prism of a fellow named Zach Kriegman, 93 00:05:16,217 --> 00:05:19,257 Speaker 1: who I'd never heard of before. And Barry Weiss on 94 00:05:19,337 --> 00:05:23,337 Speaker 1: her substack has a piece buy Zach Kriegman. She allows 95 00:05:23,417 --> 00:05:27,377 Speaker 1: people she remember she was the New York Times editorial 96 00:05:27,617 --> 00:05:33,577 Speaker 1: page person who was supposed to bring in alternate voices 97 00:05:33,697 --> 00:05:38,497 Speaker 1: from the lib orthodoxy, and she was harassed so much 98 00:05:38,497 --> 00:05:42,617 Speaker 1: by her colleagues that she resigned. Because it's such a 99 00:05:42,697 --> 00:05:48,257 Speaker 1: it's an atrocious joke. I mean, it's an intellectually fetid, 100 00:05:48,577 --> 00:05:51,697 Speaker 1: rotten place at the New York Times now, because you 101 00:05:51,777 --> 00:05:57,177 Speaker 1: either believe these left wing orthodoxes or they harass you, 102 00:05:57,177 --> 00:05:58,937 Speaker 1: they try to destroy you, they want to ruin you. 103 00:05:58,977 --> 00:06:01,177 Speaker 1: All that stuff. Well, you know that's true at the 104 00:06:01,177 --> 00:06:04,737 Speaker 1: New York Times. It's also true at Thompson Reuters. Right, 105 00:06:04,817 --> 00:06:09,057 Speaker 1: it's also true at Reuters. And you say to yourself, well, 106 00:06:09,097 --> 00:06:11,257 Speaker 1: this is this is supposed to be one of the 107 00:06:11,337 --> 00:06:16,417 Speaker 1: more a mainstream isn't the word that I should use here? 108 00:06:16,457 --> 00:06:22,257 Speaker 1: But less political I mean Reuters associated press. That's supposed 109 00:06:22,337 --> 00:06:26,577 Speaker 1: to be the most straight down the middle journalism you 110 00:06:26,617 --> 00:06:28,897 Speaker 1: can get. Right, that's at least what the brand is 111 00:06:28,897 --> 00:06:31,337 Speaker 1: supposed to be. That's not true at all. We're about 112 00:06:31,337 --> 00:06:35,897 Speaker 1: to discuss how not true that is. There's certainly something 113 00:06:36,937 --> 00:06:41,617 Speaker 1: something about these media entities that represents themselves as oh, 114 00:06:41,697 --> 00:06:44,577 Speaker 1: we're just doing the reporting. We're just the reporters doing 115 00:06:44,577 --> 00:06:49,057 Speaker 1: the reporting. Well, this individual who worked at Thompson Reuters, 116 00:06:49,097 --> 00:06:56,457 Speaker 1: he was their chief data scientist. He was fed to 117 00:06:56,537 --> 00:07:01,257 Speaker 1: the diversity and inclusion machinery. Right, they lowered him in 118 00:07:01,417 --> 00:07:05,177 Speaker 1: feet first into the grinding gears of the diversity and 119 00:07:05,217 --> 00:07:09,177 Speaker 1: inclusion and human resources office. Which I'm going to tell 120 00:07:09,217 --> 00:07:12,377 Speaker 1: you this in these companies is they're very similar to 121 00:07:12,737 --> 00:07:20,137 Speaker 1: the Soviet political commissars who were deployed in factories and 122 00:07:20,537 --> 00:07:23,577 Speaker 1: businesses stayed owned, but businesses all over the Soviet Union, 123 00:07:24,177 --> 00:07:27,897 Speaker 1: there were people who were assigned by the state to 124 00:07:28,177 --> 00:07:30,817 Speaker 1: monitor the people that were doing labor in the state 125 00:07:30,857 --> 00:07:34,937 Speaker 1: owned enterprises, to make sure they never criticized anything, to 126 00:07:35,057 --> 00:07:40,217 Speaker 1: make sure that they praised the plan, they praised the 127 00:07:40,257 --> 00:07:45,297 Speaker 1: Communist Party and the revolution. How different is that really 128 00:07:46,097 --> 00:07:48,217 Speaker 1: from what you have going on with all of these 129 00:07:48,257 --> 00:07:54,377 Speaker 1: diversity and inclusion offices that just traffic in this identity politics, 130 00:07:54,897 --> 00:08:01,137 Speaker 1: race obsession, CRT narrative which has been become a dominant ideology, 131 00:08:01,217 --> 00:08:04,057 Speaker 1: the dominant ideology of the left and the Democrat Party, 132 00:08:04,937 --> 00:08:08,097 Speaker 1: And all they do is police people for thought crimes. 133 00:08:08,817 --> 00:08:12,257 Speaker 1: That is their existence, that is what goes on. So 134 00:08:12,337 --> 00:08:16,257 Speaker 1: here you have this data scientist writing for the very 135 00:08:16,297 --> 00:08:20,497 Speaker 1: white substag and he just looked at the numbers. And 136 00:08:21,217 --> 00:08:24,057 Speaker 1: this is very interesting for a conservative like me, because 137 00:08:24,497 --> 00:08:27,617 Speaker 1: this is a guy's clearly not a conservative, He's probably 138 00:08:27,657 --> 00:08:32,497 Speaker 1: a Democrat voting lib. But he understands numbers and this 139 00:08:32,657 --> 00:08:35,017 Speaker 1: whole story that the BLM movement is built on, that 140 00:08:35,457 --> 00:08:38,097 Speaker 1: unarmed black men are constantly being shot. It's a national 141 00:08:38,137 --> 00:08:41,417 Speaker 1: crisis by police by police. There is a national crisis 142 00:08:41,417 --> 00:08:45,417 Speaker 1: of black men being shot. It is not by police, 143 00:08:45,417 --> 00:08:48,577 Speaker 1: though it is overwhelmingly by other young black men. That's 144 00:08:48,577 --> 00:08:51,937 Speaker 1: actually what the data shows. That is what is statistically 145 00:08:51,977 --> 00:08:55,537 Speaker 1: obvious and clear. That is a national crisis. That is 146 00:08:55,577 --> 00:08:58,537 Speaker 1: a national emergency. But police shooting on armed black men 147 00:08:58,737 --> 00:09:02,377 Speaker 1: is not a national crisis. It is not something that 148 00:09:02,417 --> 00:09:06,857 Speaker 1: happens frequently at all. And he knows this because he 149 00:09:06,897 --> 00:09:08,617 Speaker 1: looked at all the numbers and he became kind of 150 00:09:08,617 --> 00:09:12,457 Speaker 1: obsessed when he was at router with looking at the numbers. 151 00:09:13,617 --> 00:09:16,257 Speaker 1: This is from the piece quote. Statistics from the most 152 00:09:16,297 --> 00:09:21,417 Speaker 1: complete database indicate that over the last five years, police 153 00:09:21,457 --> 00:09:25,617 Speaker 1: have fatally shot thirty nine percent more unarmed whites than blacks. 154 00:09:25,657 --> 00:09:28,017 Speaker 1: Because there are roughly six times as many white Americans 155 00:09:28,017 --> 00:09:30,057 Speaker 1: as Black Americans, that figure should be closer to six 156 00:09:30,177 --> 00:09:33,497 Speaker 1: hundred percent. BLM activists insist the fact that it's not 157 00:09:33,577 --> 00:09:37,657 Speaker 1: that there's a gap, is, they say, evidence of the 158 00:09:37,697 --> 00:09:41,137 Speaker 1: bias of police departments when you actually look further the data. Though, 159 00:09:41,137 --> 00:09:45,017 Speaker 1: according to calculations which is published by the Deputy District 160 00:09:45,057 --> 00:09:49,177 Speaker 1: Attorney for Los Angeles based on FBI data, black Americans 161 00:09:49,177 --> 00:09:51,777 Speaker 1: account for thirty seven percent of those who murder police 162 00:09:51,817 --> 00:09:55,137 Speaker 1: officers and thirty four percent of the unarmed suspects killed 163 00:09:55,137 --> 00:09:58,137 Speaker 1: by police. Whites make up forty two percent of cop 164 00:09:58,177 --> 00:10:01,497 Speaker 1: killers and forty two percent of the unarmed suspects shot 165 00:10:01,537 --> 00:10:04,457 Speaker 1: by police, meaning whites are killed by police at a 166 00:10:04,537 --> 00:10:08,497 Speaker 1: seven percent higher rate than blacks. Because what he's doing 167 00:10:08,617 --> 00:10:11,697 Speaker 1: is trying to buy the numbers control for use of 168 00:10:11,777 --> 00:10:16,017 Speaker 1: force situations. That's what really matters. If you're looking at 169 00:10:16,057 --> 00:10:20,097 Speaker 1: our police disproportionately using lethal force, you have to look 170 00:10:20,097 --> 00:10:24,417 Speaker 1: at lethal force situations, not just the general population. Because 171 00:10:24,417 --> 00:10:29,977 Speaker 1: what this gets to is that Black Americans are disproportionately 172 00:10:30,097 --> 00:10:34,777 Speaker 1: involved in use of four situations against police that result 173 00:10:34,897 --> 00:10:39,697 Speaker 1: in use of force by police. And so this guy 174 00:10:39,777 --> 00:10:42,057 Speaker 1: points this out, this is just all by the numbers, 175 00:10:43,217 --> 00:10:45,537 Speaker 1: and he says, quote, if you broaden the analysis to 176 00:10:45,577 --> 00:10:50,017 Speaker 1: include armed suspects, whites are shot at a seventy percent 177 00:10:50,097 --> 00:10:53,937 Speaker 1: higher rate than blacks. Other experts in the field concur 178 00:10:54,017 --> 00:10:56,057 Speaker 1: that in relation to the number of police officers murdered, 179 00:10:56,057 --> 00:11:00,257 Speaker 1: whites are shot disproportionately. So what this is saying is 180 00:11:00,297 --> 00:11:04,017 Speaker 1: in a dangerous use of force situation where police officer's 181 00:11:04,097 --> 00:11:05,737 Speaker 1: life is in jeopardy, which is when you can use 182 00:11:05,817 --> 00:11:09,577 Speaker 1: lethal force, cops are more likely to use lethal force 183 00:11:09,577 --> 00:11:12,377 Speaker 1: against a white assailant than a black assailant. This is 184 00:11:12,617 --> 00:11:17,217 Speaker 1: by the numbers comepied by the FBI, the Washington Post 185 00:11:17,217 --> 00:11:20,057 Speaker 1: and others. This is what the actual data says. This 186 00:11:20,137 --> 00:11:23,497 Speaker 1: is the chief data scientist at Reuter's telling you this. 187 00:11:25,017 --> 00:11:27,937 Speaker 1: Oh boy, not allowed to talk about this, No sir, 188 00:11:29,177 --> 00:11:32,577 Speaker 1: Oh my gosh, not allowed to say it. Not allowed 189 00:11:32,577 --> 00:11:38,097 Speaker 1: to say it. And here we are. Now we see 190 00:11:38,137 --> 00:11:40,737 Speaker 1: what happens. He gets fed into the diversity and inclusion 191 00:11:41,497 --> 00:11:46,297 Speaker 1: machinery right all of a sudden, the HR and Diversity 192 00:11:46,337 --> 00:11:50,497 Speaker 1: office wants to talk to him. And it's because Oh also, 193 00:11:50,577 --> 00:11:53,017 Speaker 1: before I get to that, he says that there are 194 00:11:53,017 --> 00:11:56,817 Speaker 1: just countless stories at Reuter's where they were just lying. 195 00:11:57,577 --> 00:12:00,057 Speaker 1: I mean, they were just leaving out critical data. They 196 00:12:00,057 --> 00:12:03,177 Speaker 1: were just misleading the public, he writes. In one story, 197 00:12:03,217 --> 00:12:05,897 Speaker 1: Reuters reported on police and Kenosha shooting a black man, 198 00:12:06,017 --> 00:12:09,377 Speaker 1: Jacob Blake, in the back, but failed to mention that 199 00:12:09,417 --> 00:12:12,177 Speaker 1: they did so only after he grabbed a knife and 200 00:12:12,297 --> 00:12:16,217 Speaker 1: looked likely to lunge at them on video. In another story, 201 00:12:16,897 --> 00:12:19,817 Speaker 1: Reuters referred to a wave of killings of African Americans 202 00:12:19,817 --> 00:12:24,377 Speaker 1: by police using unjustified lethal force, despite a lack of 203 00:12:24,377 --> 00:12:27,777 Speaker 1: statistical evidence that such a wave of police killings had 204 00:12:27,817 --> 00:12:32,137 Speaker 1: taken place in twenty twenty eighteen. We are a country 205 00:12:32,137 --> 00:12:36,337 Speaker 1: of three hundred and thirty million people. Eighteen unarmed black 206 00:12:36,377 --> 00:12:38,777 Speaker 1: Americans were killed by police. According to the Washington Post, 207 00:12:39,857 --> 00:12:42,617 Speaker 1: one hundred and seven thousand people died of drug overdoses 208 00:12:42,697 --> 00:12:46,537 Speaker 1: last year. Just to put the numbers into context, how 209 00:12:46,617 --> 00:12:49,737 Speaker 1: much time does the Democrat corporate media spend on that 210 00:12:50,217 --> 00:12:53,457 Speaker 1: versus eighteen unarmed black men killed by police? By the way, 211 00:12:53,537 --> 00:12:55,337 Speaker 1: unarmed does not mean not a threat, does not mean 212 00:12:55,337 --> 00:12:57,577 Speaker 1: that they were not. That does not mean in and 213 00:12:57,577 --> 00:13:01,657 Speaker 1: of itself that those shooting incidents were not justified legally 214 00:13:01,977 --> 00:13:05,417 Speaker 1: and morally justified. I'm sure if you look at them, 215 00:13:06,177 --> 00:13:08,257 Speaker 1: a lot of them, maybe a majority of them were 216 00:13:09,017 --> 00:13:12,417 Speaker 1: legally and more justify, and maybe there were a couple 217 00:13:12,457 --> 00:13:15,937 Speaker 1: that weren't, and those officers should be prosecuted to the 218 00:13:15,937 --> 00:13:19,897 Speaker 1: fullest extent of the law. That's it. We have the system, 219 00:13:20,097 --> 00:13:22,657 Speaker 1: we have it in place, and it actually works pretty 220 00:13:23,097 --> 00:13:28,417 Speaker 1: darn well. But the only the only really serious story 221 00:13:28,457 --> 00:13:31,177 Speaker 1: to look a serious study to look at police shootings 222 00:13:31,897 --> 00:13:35,057 Speaker 1: on black versus white suspects was done by a professor 223 00:13:35,097 --> 00:13:38,497 Speaker 1: at Harvard a named Professor Friar and his study found 224 00:13:38,617 --> 00:13:41,457 Speaker 1: you wait for it, cops are more likely under similar 225 00:13:41,457 --> 00:13:44,457 Speaker 1: circumstances to shoot a white suspect than a black suspect. 226 00:13:44,697 --> 00:13:46,337 Speaker 1: We all know why this is. It's not it's not 227 00:13:46,377 --> 00:13:50,137 Speaker 1: hard to understand. If you shoot a white guy, the 228 00:13:50,217 --> 00:13:52,537 Speaker 1: media is not going to say, oh my gosh, it's racist. 229 00:13:53,617 --> 00:13:57,177 Speaker 1: But if there are questionable in any in any capacity, 230 00:13:57,697 --> 00:14:00,097 Speaker 1: if there's any sort of a judgment call situation with 231 00:14:00,217 --> 00:14:03,097 Speaker 1: a police officer, particularly a white police officer, shooting a 232 00:14:03,177 --> 00:14:08,337 Speaker 1: black suspect, the rush to its racist. The cops are murderer, 233 00:14:08,577 --> 00:14:11,897 Speaker 1: the protest, the answer, prosecution happens right away, and the 234 00:14:11,897 --> 00:14:16,497 Speaker 1: cops life is ruined. We all know this, okay, it's 235 00:14:16,537 --> 00:14:21,137 Speaker 1: reflected in the data, in the numbers. Libs just don't 236 00:14:21,137 --> 00:14:23,777 Speaker 1: want to hear it. And that then brings me into 237 00:14:23,777 --> 00:14:29,657 Speaker 1: what happened with this individual, This individual Kriegman who got 238 00:14:29,697 --> 00:14:34,817 Speaker 1: fired fired from Reuter's. You know that's coming because he 239 00:14:34,937 --> 00:14:40,017 Speaker 1: posted on the internal chat board for Reuters, meant for 240 00:14:40,217 --> 00:14:45,457 Speaker 1: discussion of controversial issues. He posted the data and his 241 00:14:45,577 --> 00:14:49,657 Speaker 1: findings to essentially say, hey, guys, this Reuter's narrative that 242 00:14:49,737 --> 00:14:53,297 Speaker 1: we're going for here, that unarmed black men are being 243 00:14:53,377 --> 00:15:00,457 Speaker 1: murdered by racist police as a continuous problem, as a 244 00:15:00,497 --> 00:15:04,297 Speaker 1: systematic problem, is a lie, and it results in the 245 00:15:04,297 --> 00:15:08,057 Speaker 1: Ferguson effect, which is cops undermined, defunded, less able to 246 00:15:08,097 --> 00:15:10,657 Speaker 1: do their jobs. And that then was alts and more 247 00:15:10,697 --> 00:15:16,297 Speaker 1: unarmed black men being killed predominantly disproportionately by other black 248 00:15:16,377 --> 00:15:19,177 Speaker 1: men in America. That is what actually happens. This is 249 00:15:19,177 --> 00:15:22,657 Speaker 1: what he said on the internal chat. What do you 250 00:15:22,697 --> 00:15:25,937 Speaker 1: think happened? Then they took it down right away, took 251 00:15:25,937 --> 00:15:29,697 Speaker 1: it down. Not allowed to say that, why it's data. 252 00:15:29,977 --> 00:15:33,897 Speaker 1: Not allowed to say the data. It's provocative and harmful, 253 00:15:33,937 --> 00:15:38,857 Speaker 1: they said. And then it got worse. Of course, he writes, 254 00:15:38,897 --> 00:15:40,537 Speaker 1: over the next two weeks, I kept checking back to 255 00:15:40,577 --> 00:15:43,057 Speaker 1: see whether they would reinstate it. After a good bit 256 00:15:43,097 --> 00:15:45,497 Speaker 1: of waiting and wondering, I was told by HR a 257 00:15:45,497 --> 00:15:48,857 Speaker 1: team of human resources and communications professionals was reviewing it. 258 00:15:49,697 --> 00:15:52,377 Speaker 1: I asked if I'd be allowed to discuss the moderator's concerns. 259 00:15:52,417 --> 00:15:54,857 Speaker 1: I was told no. Finally, they told me the post 260 00:15:54,937 --> 00:15:57,937 Speaker 1: would not be reinstated because it was antagonistic and provocative. 261 00:15:59,057 --> 00:16:01,897 Speaker 1: When I asked what exactly was provocative or antagonistic? She 262 00:16:01,977 --> 00:16:05,297 Speaker 1: suggests that I speak with the head of Diversity and inclusion, Oh, 263 00:16:05,377 --> 00:16:12,297 Speaker 1: Chief Commissar. So I scheduled the meeting and I was 264 00:16:12,377 --> 00:16:15,577 Speaker 1: told that I had what I had, it could hurt 265 00:16:15,577 --> 00:16:17,417 Speaker 1: me at the company. It could put the kaibosh on 266 00:16:17,457 --> 00:16:22,337 Speaker 1: any future promotions. Then the comments started rolling in from colleagues. 267 00:16:22,817 --> 00:16:25,697 Speaker 1: They called my review of the academic literature white splaining, 268 00:16:26,417 --> 00:16:28,337 Speaker 1: failing to note that many of the academics I cited 269 00:16:28,337 --> 00:16:31,257 Speaker 1: were black, That it was laughable what I wrote, not 270 00:16:31,377 --> 00:16:35,377 Speaker 1: worth engaging in. One even said, my unwillingness to engage 271 00:16:35,417 --> 00:16:38,177 Speaker 1: doesn't signal the strength of your argument. If someone says 272 00:16:38,217 --> 00:16:40,697 Speaker 1: that KKK did lots of good things for the community, 273 00:16:40,737 --> 00:16:44,217 Speaker 1: prove me wrong. I'm not obligated to do so. I 274 00:16:44,257 --> 00:16:45,897 Speaker 1: mean the person who wrote that as a moron and 275 00:16:45,977 --> 00:16:47,857 Speaker 1: a psychopath. I don't know who it is with in Reuters, 276 00:16:47,937 --> 00:16:52,337 Speaker 1: but that person is a complete, blanking idiot, total moron 277 00:16:53,097 --> 00:16:55,257 Speaker 1: paid by Reuters though. But you see, this is what happens. 278 00:16:55,697 --> 00:16:58,177 Speaker 1: People create these comfortable little narratives from themselves were they 279 00:16:58,177 --> 00:17:04,457 Speaker 1: don't have to engage or debate. And then Kriegman sent 280 00:17:04,617 --> 00:17:09,297 Speaker 1: a letter to company leadership saying, hey, what's going on here? 281 00:17:10,377 --> 00:17:12,697 Speaker 1: You know they did. After that, I brought him into 282 00:17:12,737 --> 00:17:15,937 Speaker 1: an office HR they fired him, terminated him after six years. 283 00:17:15,977 --> 00:17:20,217 Speaker 1: Chief data scientist Reuters, one of the biggest news gathering 284 00:17:20,337 --> 00:17:25,377 Speaker 1: organizations in the world, fired its chief data scientist for 285 00:17:25,537 --> 00:17:29,817 Speaker 1: sharing data that showed that the BLM narrative is bullcrap 286 00:17:31,177 --> 00:17:34,937 Speaker 1: and they didn't want to hear it, so they fired him. 287 00:17:35,057 --> 00:17:38,977 Speaker 1: This is at Reuters. This is also, though in essence, 288 00:17:39,297 --> 00:17:41,337 Speaker 1: at The New York Times, at the Washington Post, at 289 00:17:41,457 --> 00:17:44,817 Speaker 1: name a major corporation these days, at Uber, at Apple, 290 00:17:45,377 --> 00:17:47,617 Speaker 1: at all these places, you can't share this, and now 291 00:17:47,937 --> 00:17:50,137 Speaker 1: this is about news and news gathering, so he should 292 00:17:50,137 --> 00:17:52,457 Speaker 1: have even more right. I mean, you can make the argument, well, 293 00:17:52,457 --> 00:17:54,097 Speaker 1: we shouldn't be arguing we shouldn't be talking to this 294 00:17:54,097 --> 00:17:56,097 Speaker 1: at all of those companies. Okay, well, of course they do. 295 00:17:56,257 --> 00:17:59,977 Speaker 1: They indoctrinate with the BLM rhetoric at these places. But 296 00:18:00,297 --> 00:18:03,497 Speaker 1: at a news organization, you can't share data that contradicts 297 00:18:03,497 --> 00:18:07,937 Speaker 1: the newsroom narrative, clearly contradicts it. No, you cannot, Now 298 00:18:07,977 --> 00:18:10,977 Speaker 1: you cannot. It's a good time to tell you about 299 00:18:10,977 --> 00:18:14,057 Speaker 1: the Law Enforcement Legal Defense Fund, which is defending many 300 00:18:14,137 --> 00:18:17,617 Speaker 1: officers who have been wrongfully accused charged or fired just 301 00:18:17,657 --> 00:18:20,217 Speaker 1: for doing their jobs. These officers and their families are 302 00:18:20,217 --> 00:18:22,177 Speaker 1: counting on you to support them in their time of need. 303 00:18:22,217 --> 00:18:24,537 Speaker 1: Your donation will help them stay out of prison and 304 00:18:24,577 --> 00:18:28,457 Speaker 1: avoid being financially bankrupted by bogus accusations and pressure from 305 00:18:28,497 --> 00:18:30,737 Speaker 1: the far left to charge the police for just doing 306 00:18:30,737 --> 00:18:33,537 Speaker 1: their job of protecting all of us. Take a moment now, 307 00:18:33,697 --> 00:18:36,057 Speaker 1: don't just let this be something we talk about. Take action. 308 00:18:36,497 --> 00:18:39,097 Speaker 1: Go to police defense dot org. Not only are they 309 00:18:39,177 --> 00:18:41,697 Speaker 1: raising funds necessary to get officers charged the best legal 310 00:18:41,737 --> 00:18:44,977 Speaker 1: defense possible, they're helping those officers and their families keep 311 00:18:44,977 --> 00:18:46,737 Speaker 1: the lights on, keep food on the table, keep a 312 00:18:46,817 --> 00:18:51,657 Speaker 1: roof over their heads. Fundraising is crucial for officers wrongfully 313 00:18:51,657 --> 00:18:55,137 Speaker 1: accused in these woke times. Please go to police defense 314 00:18:55,217 --> 00:18:58,697 Speaker 1: dot org. Consider contributing just fifteen dollars or more to 315 00:18:58,737 --> 00:19:00,617 Speaker 1: the Police Defense Fund to help these officers in their 316 00:19:00,617 --> 00:19:04,497 Speaker 1: time of need. That's police Defense dot org. Police Defense 317 00:19:05,017 --> 00:19:08,057 Speaker 1: dot org. I will let you all know how my 318 00:19:08,177 --> 00:19:10,937 Speaker 1: first trip to Chicago. I think it'll be a lot 319 00:19:10,937 --> 00:19:13,857 Speaker 1: of fun and I don't anticipate that there will be 320 00:19:13,897 --> 00:19:17,377 Speaker 1: any issues. Even though everyone saying there will be issues, 321 00:19:17,577 --> 00:19:18,977 Speaker 1: I don't think there will be I think I'll have 322 00:19:18,977 --> 00:19:21,617 Speaker 1: a lovely time. I'll give you my full report on 323 00:19:21,697 --> 00:19:23,817 Speaker 1: all this when I see you on Monday. Talk to 324 00:19:23,897 --> 00:19:26,057 Speaker 1: him Monday rather shields high