1 00:00:03,560 --> 00:00:05,680 Speaker 1: Welcome back to a numbers game with Ryan Grodski. Thank 2 00:00:05,680 --> 00:00:08,039 Speaker 1: you guys for being here. I want to address the 3 00:00:08,320 --> 00:00:11,000 Speaker 1: tragic shooting that happened in Minneapolis between an ICE agent 4 00:00:11,039 --> 00:00:14,840 Speaker 1: and a protester that unfortunately to her her death. But 5 00:00:14,920 --> 00:00:17,479 Speaker 1: there's some economic news I'd like to get to you before that. 6 00:00:17,480 --> 00:00:19,000 Speaker 1: That's going to be the bulk of the show is 7 00:00:19,040 --> 00:00:21,919 Speaker 1: the Minneapolis shooting, But there's really important economic data that 8 00:00:21,920 --> 00:00:24,400 Speaker 1: I think that my audience really needs to know. First. 9 00:00:24,440 --> 00:00:28,440 Speaker 1: Our trade deficit has collapsed October twenty twenty five. The 10 00:00:28,440 --> 00:00:30,800 Speaker 1: new data that cay matchws it's the lowest trade deficit 11 00:00:31,000 --> 00:00:34,080 Speaker 1: is two thousand and nine, just a twenty nine point 12 00:00:34,080 --> 00:00:37,440 Speaker 1: four billion dollar trade deficit, about a fifty percent drop 13 00:00:37,520 --> 00:00:40,080 Speaker 1: from the month prior. According to The New York Times, 14 00:00:40,120 --> 00:00:43,279 Speaker 1: imports in October fell three point two percent to three 15 00:00:43,440 --> 00:00:46,479 Speaker 1: hundred and thirty one point four billion dollars in the 16 00:00:46,520 --> 00:00:49,519 Speaker 1: previous month, while exports rose two point six percent to 17 00:00:49,600 --> 00:00:53,040 Speaker 1: three hundred and two billion. Because exports grew more than imports, 18 00:00:53,159 --> 00:00:56,640 Speaker 1: the US trade deficit sank shrink rather in line with 19 00:00:56,680 --> 00:00:59,600 Speaker 1: President Trump's goal. This was a big promise for President 20 00:00:59,600 --> 00:01:02,320 Speaker 1: Trump and a lot of detractors said it's impossible at 21 00:01:02,280 --> 00:01:03,840 Speaker 1: the trade upisode down. In fact, it's a good thing. 22 00:01:03,880 --> 00:01:06,880 Speaker 1: We have a trade episode and it's not there yet. 23 00:01:06,920 --> 00:01:10,360 Speaker 1: We're not balanced yet. But man, it's closer than it's 24 00:01:10,440 --> 00:01:13,920 Speaker 1: been in a long long time. Two thousand and nine. 25 00:01:14,000 --> 00:01:16,920 Speaker 1: I mean that was I mean that was a long 26 00:01:17,040 --> 00:01:20,319 Speaker 1: time ago. I was still in college or is just 27 00:01:20,400 --> 00:01:22,080 Speaker 1: coming out of I was still in college at the 28 00:01:22,080 --> 00:01:24,119 Speaker 1: time before I dropped out. I mean, like it was, 29 00:01:24,440 --> 00:01:26,480 Speaker 1: it was a long time ago. This is a big, 30 00:01:26,520 --> 00:01:29,880 Speaker 1: big progress from President Trump. Productivity rose by four point 31 00:01:29,920 --> 00:01:32,039 Speaker 1: nine percent, which is very good. And I want to 32 00:01:32,080 --> 00:01:34,520 Speaker 1: note that the US jobs opened to drop though, by 33 00:01:34,520 --> 00:01:36,959 Speaker 1: three hundred and three thousand and seven point one four 34 00:01:36,959 --> 00:01:41,399 Speaker 1: to six million, the second lowest number since December twenty twenty. Okay, 35 00:01:41,760 --> 00:01:44,360 Speaker 1: among all this good news, there is some rough economic 36 00:01:44,440 --> 00:01:46,920 Speaker 1: data coming from Trump's for Trump's space, there was a 37 00:01:46,920 --> 00:01:50,360 Speaker 1: substec that I read by a g named Joseph Polatino. 38 00:01:51,120 --> 00:01:53,880 Speaker 1: You know, guys, my pronunciations are not always the best 39 00:01:53,880 --> 00:01:57,840 Speaker 1: with names. Joseph Polantino about America losing blue collar jobs 40 00:01:57,880 --> 00:02:00,680 Speaker 1: over the last year, he writes, quote, the country is 41 00:02:00,720 --> 00:02:04,120 Speaker 1: down sixty five thousand industrial jobs, in the last year, 42 00:02:04,120 --> 00:02:06,920 Speaker 1: a dramatic reverse from twenty twenty four, when it added 43 00:02:07,080 --> 00:02:10,320 Speaker 1: two hundred and fifty thousand jobs. The vast majority of 44 00:02:10,360 --> 00:02:16,240 Speaker 1: these job losses come from two industries, manufacturing and transportation. Transportation, remember, 45 00:02:16,400 --> 00:02:19,160 Speaker 1: is the biggest job creator for people without a high 46 00:02:19,160 --> 00:02:23,120 Speaker 1: school diploma. Total employment across trades is down one hundred 47 00:02:23,160 --> 00:02:26,079 Speaker 1: and twenty three thousand from their all time peak in 48 00:02:26,200 --> 00:02:28,920 Speaker 1: early twenty twenty five, and so it doesn't all have 49 00:02:29,000 --> 00:02:31,520 Speaker 1: to do with Trump, though. Manufacturing jobs have been on 50 00:02:31,520 --> 00:02:35,000 Speaker 1: a study declient for two straight years and the sector 51 00:02:35,040 --> 00:02:37,359 Speaker 1: has hit a record log. Just eight percent of all 52 00:02:37,520 --> 00:02:42,640 Speaker 1: jobs in the workforce are manufacturing. All major industries except 53 00:02:42,680 --> 00:02:46,119 Speaker 1: metals and non metallic minerals have lost a total two 54 00:02:46,560 --> 00:02:51,480 Speaker 1: hundred thousand manufacturing job since early twenty twenty three. Three 55 00:02:51,560 --> 00:02:54,680 Speaker 1: biggest types of manufacturing jobs seeing the clients are carmakers, 56 00:02:55,160 --> 00:02:59,880 Speaker 1: semiconductor fabricators, and product manufacturing. When it comes to carmakers, 57 00:03:00,240 --> 00:03:04,360 Speaker 1: the jobs dealing with steering and suspension have made up 58 00:03:04,560 --> 00:03:07,640 Speaker 1: half of those losses. I guess those are two particular 59 00:03:07,680 --> 00:03:10,959 Speaker 1: trades and manufacturing they make up the bulk states with 60 00:03:11,040 --> 00:03:13,440 Speaker 1: the biggest decline of manufacturing. A lot of them are 61 00:03:13,440 --> 00:03:17,919 Speaker 1: out West Montana, Oregon, California, Washington, but there's also Iowa, Virginia, 62 00:03:18,040 --> 00:03:22,480 Speaker 1: West Virginia, Missouri, and Nebraska. Of the three big blue 63 00:03:22,520 --> 00:03:25,320 Speaker 1: Wall states that Trump needed to win for the presidency, 64 00:03:25,680 --> 00:03:29,320 Speaker 1: only Pennsylvania has seen manufacturing grow from September twenty twenty 65 00:03:29,320 --> 00:03:33,600 Speaker 1: four to September twenty twenty five. Ohio Minnesota also saw 66 00:03:33,680 --> 00:03:37,200 Speaker 1: some job growth of manufacturing, but Illinois, Wisconsin, Michigan, Indiana 67 00:03:37,640 --> 00:03:40,000 Speaker 1: all of them are down. Other key swing states like 68 00:03:40,040 --> 00:03:44,280 Speaker 1: North Caro, Carolina and Arizona have also lost manufacturing jobs. 69 00:03:44,320 --> 00:03:47,240 Speaker 1: The big shift coming construction jobs, though it's only ated 70 00:03:47,320 --> 00:03:50,240 Speaker 1: fifty two thousand over the last twelve months compared to 71 00:03:50,280 --> 00:03:53,040 Speaker 1: one hundred and ninety one thousand in the year prior. 72 00:03:53,640 --> 00:03:58,440 Speaker 1: Residential contractors at like including plumbers, electrician, roofers, and other 73 00:03:58,480 --> 00:04:01,200 Speaker 1: specialty work like that they've lost fifth twenty five thousand 74 00:04:01,320 --> 00:04:05,000 Speaker 1: jobs in the last year. Polentino the substackt writer, so 75 00:04:05,080 --> 00:04:07,720 Speaker 1: the decline comes from structure of decline in manufacturing. The 76 00:04:07,800 --> 00:04:11,400 Speaker 1: demand for durable goods and consumer electronics is in decline, 77 00:04:11,520 --> 00:04:16,400 Speaker 1: and early COVID home building that boom has ended. Transportation 78 00:04:16,560 --> 00:04:21,400 Speaker 1: jobs are slowing as a trucking sector struggles, and semiconductors, 79 00:04:21,400 --> 00:04:24,159 Speaker 1: which remember President Biden, He's going to pass with Chips ACT, 80 00:04:24,160 --> 00:04:25,400 Speaker 1: and it was going to be amazing. It was going 81 00:04:25,480 --> 00:04:28,000 Speaker 1: to be our new industry with semiconductors, he says. Well, 82 00:04:28,040 --> 00:04:31,960 Speaker 1: semiconductor manufacturing is already below the twenty twenty one levels, 83 00:04:32,040 --> 00:04:35,760 Speaker 1: as most data center chips are imported from Taiwan and Mexico. 84 00:04:36,240 --> 00:04:39,640 Speaker 1: When it comes to construction, America gained seventy six thousand 85 00:04:39,760 --> 00:04:43,599 Speaker 1: non residential construction jobs in twenty five thousand civil and 86 00:04:43,720 --> 00:04:48,119 Speaker 1: heavy engineering jobs, but loss fifty thousand residential construction jobs. 87 00:04:48,160 --> 00:04:52,840 Speaker 1: Residential construction is in massive in decline. Real estate investment 88 00:04:52,880 --> 00:04:56,360 Speaker 1: in single homes is down twenty one percent, while investment 89 00:04:56,400 --> 00:04:59,440 Speaker 1: in multi valley homes is down twenty four percent since 90 00:04:59,480 --> 00:05:02,200 Speaker 1: twenty twenty one. The only area that's seeing a bit 91 00:05:02,200 --> 00:05:04,880 Speaker 1: of a boom is home renovations. People are like getting, 92 00:05:04,880 --> 00:05:08,000 Speaker 1: I guess inspired by HGTV, and they're remodeling old homes. 93 00:05:08,400 --> 00:05:13,839 Speaker 1: States like New Jersey, New York, California, Nevada, Washington, Georgia, Florida, Louisiana, 94 00:05:13,880 --> 00:05:18,159 Speaker 1: and Arkansas have lost massive levels of construction jobs. One 95 00:05:18,200 --> 00:05:21,760 Speaker 1: bright spot is the Great Lake States, Michigan, Ohio, Wisconsin, 96 00:05:21,920 --> 00:05:25,200 Speaker 1: and places like Kentucky, Virginia, Alabama, and the Carolinas are 97 00:05:25,240 --> 00:05:30,200 Speaker 1: seeing construction jobs go up, but it's been very uneasy. 98 00:05:30,480 --> 00:05:35,160 Speaker 1: America has lost slightly roughly thirteen thousand jobs in resource extraction. 99 00:05:35,279 --> 00:05:37,680 Speaker 1: He also writes over the last year, primarily due to 100 00:05:37,680 --> 00:05:41,080 Speaker 1: extended losses in the fracking industry, America lost thirteen percent 101 00:05:41,120 --> 00:05:44,160 Speaker 1: of all its oil and gas extracting jobs since mid 102 00:05:44,440 --> 00:05:48,200 Speaker 1: twenty twenty three. This is how Joseph Palatino summarizes it 103 00:05:48,360 --> 00:05:51,039 Speaker 1: in the state of blue collar jobs in America. After all, 104 00:05:51,320 --> 00:05:55,239 Speaker 1: it's easier to invent more productive, less labor intense ways 105 00:05:55,279 --> 00:05:59,920 Speaker 1: to assemble televisions than cutting hair, engineering chemicals for teaching children, 106 00:06:00,040 --> 00:06:03,320 Speaker 1: and the key phrase that there is more productivity in 107 00:06:03,320 --> 00:06:08,400 Speaker 1: this world. Manufacturing employment would be declining while manufacturing output rises. 108 00:06:08,600 --> 00:06:11,039 Speaker 1: America would be building not only in new homes and 109 00:06:11,080 --> 00:06:14,080 Speaker 1: infrastructure to meet its current shortage, but also adding more 110 00:06:14,080 --> 00:06:17,080 Speaker 1: than enough to prepare for future generations. Instead, they are 111 00:06:17,120 --> 00:06:19,880 Speaker 1: struggling to meet rise in electricity demands. The country would 112 00:06:19,880 --> 00:06:23,719 Speaker 1: be rapidly building out new power sources. Highly automated factors 113 00:06:23,800 --> 00:06:26,560 Speaker 1: be chugging away None of this is happening right now. 114 00:06:26,600 --> 00:06:31,240 Speaker 1: Productivity is weak in both manufacturing and construction industries. Home 115 00:06:31,279 --> 00:06:33,599 Speaker 1: building activity is slower than it was in two thousand 116 00:06:33,640 --> 00:06:36,080 Speaker 1: and five. The country is making fewer cars than it 117 00:06:36,080 --> 00:06:38,560 Speaker 1: did in the late seventies. It's losing jobs in its 118 00:06:38,680 --> 00:06:43,159 Speaker 1: high tech electronic manufacturing industries. Perhaps the only major blue 119 00:06:43,200 --> 00:06:47,080 Speaker 1: collar sector that fits the ideal archetype is oil, where 120 00:06:47,080 --> 00:06:50,039 Speaker 1: America has turned the world to become the world's largest producer, 121 00:06:50,279 --> 00:06:53,279 Speaker 1: even as the industry employs forty percent fewer people from 122 00:06:53,320 --> 00:06:57,479 Speaker 1: peak twenty fourteen. Yet, despite America's rapid oil growth, it 123 00:06:57,520 --> 00:07:00,080 Speaker 1: remains but a tiny exception of the scale of the 124 00:07:00,120 --> 00:07:04,080 Speaker 1: overall economy. Short term factors are driving the loss of 125 00:07:04,120 --> 00:07:06,760 Speaker 1: blue collar jobs bad enough, but they are made even 126 00:07:06,760 --> 00:07:08,680 Speaker 1: worse for the fact that America would need more blue 127 00:07:08,680 --> 00:07:11,800 Speaker 1: collar workers and increase in their efficiency to make up 128 00:07:11,840 --> 00:07:15,920 Speaker 1: for decades of persistent underperformance. Instead, it is more likely 129 00:07:15,960 --> 00:07:18,640 Speaker 1: that the US will continue losing blue collar jobs even 130 00:07:18,720 --> 00:07:25,280 Speaker 1: as structural problems and shortages of necessity and necessary necessity sorry, guys, 131 00:07:25,480 --> 00:07:30,520 Speaker 1: necessary infrastructure gets even more acute. This is so profound 132 00:07:30,560 --> 00:07:32,960 Speaker 1: to me in this article, and it was the data 133 00:07:33,000 --> 00:07:36,320 Speaker 1: behind it was so rich. Listen, working class people, blue 134 00:07:36,360 --> 00:07:39,920 Speaker 1: collar people are the base of the Republican Party. Whites 135 00:07:39,960 --> 00:07:44,240 Speaker 1: without a college degree no Republican, or essentially no Republican. 136 00:07:44,280 --> 00:07:47,480 Speaker 1: Maybe there are a handful in South Texas or South Florida, 137 00:07:47,520 --> 00:07:52,240 Speaker 1: but essentially no Republican in this country can win without 138 00:07:52,280 --> 00:07:56,800 Speaker 1: the backs of blue collar, non college educated white Americans. 139 00:07:57,120 --> 00:07:59,760 Speaker 1: They are what put Donald Trump in the White House 140 00:08:00,120 --> 00:08:04,040 Speaker 1: more than anybody. They kept Florida red, they keep Texas red. 141 00:08:04,240 --> 00:08:07,440 Speaker 1: They made the Blue Wall states turn red. These are 142 00:08:07,520 --> 00:08:11,200 Speaker 1: the people that are the absolute base of the Republican Party. 143 00:08:11,240 --> 00:08:12,960 Speaker 1: If the republic Party were a cake, they would be 144 00:08:13,000 --> 00:08:16,280 Speaker 1: the batter. There is no exception to this rule. And 145 00:08:16,320 --> 00:08:19,440 Speaker 1: they're unlikely to become Democrats because the Democrats were policies 146 00:08:19,440 --> 00:08:21,800 Speaker 1: that are just too on align with what they believe in. 147 00:08:22,360 --> 00:08:24,520 Speaker 1: But if you want to be competitive in any election, 148 00:08:24,720 --> 00:08:27,560 Speaker 1: they need to show up. And their pokabout issues aren't 149 00:08:27,720 --> 00:08:31,080 Speaker 1: just a matter of bringing prices down or more available housing. 150 00:08:31,360 --> 00:08:33,840 Speaker 1: They have to have jobs. I mean, this is just 151 00:08:33,920 --> 00:08:36,679 Speaker 1: the truth. And this should be more important to a 152 00:08:36,760 --> 00:08:40,920 Speaker 1: Republican administration than the Venezuelans living in South Florida or 153 00:08:40,960 --> 00:08:43,360 Speaker 1: the tech bros in Silicon Valley or the people who 154 00:08:43,400 --> 00:08:47,040 Speaker 1: work on Wall Street. None of them got Donald Trump elected. 155 00:08:47,400 --> 00:08:51,440 Speaker 1: Working class white people got Donald Trump elected both in 156 00:08:51,480 --> 00:08:54,600 Speaker 1: twenty sixteen and in twenty twenty four. Anyone telling you 157 00:08:54,600 --> 00:08:56,920 Speaker 1: any different is talk with the margins. They're not talking 158 00:08:56,920 --> 00:08:59,320 Speaker 1: about the base of the Republican Party. Working class people 159 00:08:59,320 --> 00:09:02,280 Speaker 1: who voted for Jama twice, whose parents probably voted for Clinton. 160 00:09:02,520 --> 00:09:04,480 Speaker 1: If not, they also probably vote for Clinton if they're 161 00:09:04,480 --> 00:09:07,040 Speaker 1: of a certain age. These are the people who make 162 00:09:07,320 --> 00:09:09,920 Speaker 1: up the modern day Republican Party. And at the economy 163 00:09:09,960 --> 00:09:12,840 Speaker 1: is failing that. It is failing the very, very base 164 00:09:13,080 --> 00:09:16,640 Speaker 1: that they need to win in the future. It's just 165 00:09:16,760 --> 00:09:19,839 Speaker 1: that simple, and it should be all hands on deck 166 00:09:19,880 --> 00:09:21,199 Speaker 1: to sit there and figure out how do we get 167 00:09:21,240 --> 00:09:25,360 Speaker 1: manufacturing and construction and blue collar jobs not only increasing 168 00:09:25,400 --> 00:09:28,880 Speaker 1: and hiring, but protected into the future. These people should 169 00:09:28,920 --> 00:09:33,359 Speaker 1: not be replaced by robots and machines or immigrants or anybody. 170 00:09:33,600 --> 00:09:37,120 Speaker 1: They're your voters. You should protect them. That's how I 171 00:09:37,160 --> 00:09:39,560 Speaker 1: personally feel. Next up, I have a special guest to 172 00:09:39,559 --> 00:09:43,240 Speaker 1: discuss the Minneapolis shooting. It's a fascinating conversation by a 173 00:09:43,320 --> 00:09:49,640 Speaker 1: brilliant lawyer. That's me. I'm next stay tuned. My guest 174 00:09:49,679 --> 00:09:52,280 Speaker 1: for today's episode is my buddy Maria Medvin. She is 175 00:09:52,320 --> 00:09:55,600 Speaker 1: a very talented senior trial attorney. To go over the 176 00:09:55,640 --> 00:09:59,320 Speaker 1: incident in Minneapolis, the shooting. Marina, Now you're a lawyer, 177 00:09:59,360 --> 00:10:02,160 Speaker 1: so I want to just establish certain facts before we 178 00:10:02,200 --> 00:10:04,400 Speaker 1: go into it. So ICE was doing a law enforcement 179 00:10:04,679 --> 00:10:08,520 Speaker 1: measured an exercise. It wasn't sure whether it's a raid 180 00:10:08,640 --> 00:10:10,280 Speaker 1: or what it was, but it was an exercise though, 181 00:10:10,280 --> 00:10:12,960 Speaker 1: And the thirty seven year old mother, Renee Good, and 182 00:10:13,080 --> 00:10:15,880 Speaker 1: her wife, allegedly so she claimed to be her wife, 183 00:10:15,960 --> 00:10:23,640 Speaker 1: was there and her car was blocking the road where 184 00:10:23,760 --> 00:10:25,920 Speaker 1: ICE agents were trying to move. She let one go, 185 00:10:26,040 --> 00:10:29,360 Speaker 1: but her car was perpendicular to traffic, which she would 186 00:10:29,400 --> 00:10:32,000 Speaker 1: only do if you were backing up into a parking 187 00:10:32,000 --> 00:10:34,400 Speaker 1: spot or if you were making a k term which 188 00:10:34,480 --> 00:10:36,520 Speaker 1: was a one way street, which you wouldn't do. And 189 00:10:36,679 --> 00:10:40,080 Speaker 1: CNN witnesses said that Renee was her car was there 190 00:10:40,160 --> 00:10:43,440 Speaker 1: to be perpendicular for the purpose of stopping ICE ages 191 00:10:43,480 --> 00:10:48,120 Speaker 1: from Kentuck from conducting their movement. Is it legal to 192 00:10:48,360 --> 00:10:52,439 Speaker 1: block traffic for federal law enforcement trying to do an exercise? 193 00:10:53,679 --> 00:10:56,400 Speaker 2: So, in response to this exact question, now it's not 194 00:10:56,600 --> 00:10:59,959 Speaker 2: legal to block law enforcement officers. There's a federal call 195 00:11:00,120 --> 00:11:03,880 Speaker 2: section against this. It's eighteen USC. One to eleven. You're 196 00:11:03,920 --> 00:11:07,960 Speaker 2: not allowed to impede or interfere with law enforcement federal 197 00:11:08,000 --> 00:11:12,559 Speaker 2: law enforcement agents while they are on duty or carrying 198 00:11:12,600 --> 00:11:16,719 Speaker 2: out actions pursuing to their duties. In this case, in fact, 199 00:11:16,760 --> 00:11:18,960 Speaker 2: they were on duty. They were doing something with a 200 00:11:19,080 --> 00:11:22,520 Speaker 2: vehicle that appeared to have been stuck on ice or snow, 201 00:11:23,080 --> 00:11:26,959 Speaker 2: and so her actions based on what the witnesses are saying, 202 00:11:26,960 --> 00:11:29,520 Speaker 2: we don't have exact video evidence of what she was doing, 203 00:11:29,840 --> 00:11:32,480 Speaker 2: but it sounds like she was impeding or interfering with 204 00:11:32,520 --> 00:11:37,480 Speaker 2: their performance in at least through the particular positioning of 205 00:11:37,520 --> 00:11:40,160 Speaker 2: her vehicle. There may have been other things going on. 206 00:11:40,520 --> 00:11:43,240 Speaker 2: What's interesting, if we watched the same video, there's actually 207 00:11:43,280 --> 00:11:46,400 Speaker 2: a much longer video from the one that everyone's been seeing, 208 00:11:46,679 --> 00:11:48,800 Speaker 2: and if we watch the video, we're able to kind 209 00:11:48,800 --> 00:11:52,280 Speaker 2: of piece it together a little bit better. And actually 210 00:11:52,440 --> 00:11:54,800 Speaker 2: you'll see that in the beginning of the video. I 211 00:11:54,840 --> 00:12:01,880 Speaker 2: took a screenshot here, the officer who later uses force 212 00:12:02,040 --> 00:12:04,880 Speaker 2: is actually seeing with her wife right there. 213 00:12:05,240 --> 00:12:07,840 Speaker 1: Wow, I did not see this video. Okay, so explain 214 00:12:07,920 --> 00:12:10,240 Speaker 1: what was going on? So the officer comes to the car, 215 00:12:10,559 --> 00:12:13,400 Speaker 1: her wife is behind the vehicle. For those who go 216 00:12:13,440 --> 00:12:16,120 Speaker 1: on to you if you're watching this on camera, So 217 00:12:16,360 --> 00:12:18,319 Speaker 1: if you go on YouTube, you'll see this. If you're 218 00:12:18,360 --> 00:12:22,320 Speaker 1: listening to her wife posts behind the Yes, I'll. 219 00:12:22,120 --> 00:12:24,600 Speaker 2: Post it on my ex accounts so everyone can see. 220 00:12:24,640 --> 00:12:26,679 Speaker 2: But this is the same video that we were seeing 221 00:12:26,760 --> 00:12:29,240 Speaker 2: that everyone's watching, that angle that we keep seeing replaying 222 00:12:29,280 --> 00:12:31,360 Speaker 2: over and over. I simply dig a little bit of 223 00:12:31,400 --> 00:12:34,280 Speaker 2: digging and found the longest version of this video. And 224 00:12:34,320 --> 00:12:37,560 Speaker 2: this was simply in the beginning. So the officer is 225 00:12:37,720 --> 00:12:40,040 Speaker 2: out there and he has his cell phone in front 226 00:12:40,040 --> 00:12:42,800 Speaker 2: of him. He appears to be recording and this woman 227 00:12:42,920 --> 00:12:45,680 Speaker 2: is her wife, And how do we know because it 228 00:12:45,800 --> 00:12:49,120 Speaker 2: matches the outfit, and CNN and other news sources later 229 00:12:49,160 --> 00:12:52,520 Speaker 2: identified her as the wife. And in the video where 230 00:12:52,520 --> 00:12:55,720 Speaker 2: she's identified as the wife, you actually hear the witness 231 00:12:55,720 --> 00:12:59,920 Speaker 2: who's shooting the video state that she was previously inside 232 00:13:00,040 --> 00:13:03,640 Speaker 2: of that maroon suv with the woman who's now deceased, 233 00:13:03,720 --> 00:13:05,800 Speaker 2: So the two of them were in the vehicle. At 234 00:13:05,800 --> 00:13:09,199 Speaker 2: some point, the wife gets out. She also has her 235 00:13:09,200 --> 00:13:12,640 Speaker 2: cell phone at and she is following the officer who 236 00:13:12,760 --> 00:13:14,720 Speaker 2: has his cell phone out and the two of them 237 00:13:14,760 --> 00:13:17,440 Speaker 2: appear to be at this phone recording each other, but 238 00:13:17,480 --> 00:13:19,400 Speaker 2: the officer is recording, and in the beginning of the 239 00:13:19,440 --> 00:13:24,439 Speaker 2: video you see the officer actually start moving towards the 240 00:13:24,480 --> 00:13:29,240 Speaker 2: passenger side of that suv and the wife is following him. 241 00:13:29,400 --> 00:13:32,440 Speaker 2: Eventually they come out of you, so we can't see 242 00:13:32,440 --> 00:13:35,559 Speaker 2: what happens after. Now I started trying to figure out 243 00:13:35,600 --> 00:13:38,640 Speaker 2: I'm going back and forth on this video, pulling it 244 00:13:39,160 --> 00:13:41,760 Speaker 2: forward back in time, trying to figure out where he's 245 00:13:41,800 --> 00:13:45,960 Speaker 2: standing at this point, because the wife actually ends up 246 00:13:46,000 --> 00:13:47,760 Speaker 2: being somewhere farther back. 247 00:13:48,400 --> 00:13:50,839 Speaker 1: He's on the sidewalk, it appears at one point. 248 00:13:51,040 --> 00:13:54,800 Speaker 2: He's no longer So once she follows him through and 249 00:13:54,880 --> 00:13:56,760 Speaker 2: he goes to the side of the vehicle, she's no 250 00:13:56,840 --> 00:14:00,360 Speaker 2: longer anywhere near there. She goes back somewhere, so she's 251 00:14:00,400 --> 00:14:03,360 Speaker 2: no longer relevant to this particular story, but he is. 252 00:14:04,040 --> 00:14:07,240 Speaker 2: So where is his location? Well, I started circling an 253 00:14:07,280 --> 00:14:10,200 Speaker 2: orange here if you could see where his feet would be, 254 00:14:10,360 --> 00:14:13,679 Speaker 2: and he's not visible anywhere. Only position where he can 255 00:14:13,760 --> 00:14:17,600 Speaker 2: be standing is near the passenger side area. So we're 256 00:14:17,640 --> 00:14:19,640 Speaker 2: looking through at the front of the windows. We can't 257 00:14:19,680 --> 00:14:23,160 Speaker 2: see him around. We're looking for his feet, his shadow, nothing, 258 00:14:23,200 --> 00:14:26,320 Speaker 2: nothing is visible. The only area that's not visible is 259 00:14:26,440 --> 00:14:29,440 Speaker 2: this passenger side window or the corner maybe the front 260 00:14:29,440 --> 00:14:32,200 Speaker 2: area that's not visible. So he's somewhere there near the 261 00:14:32,240 --> 00:14:35,560 Speaker 2: passenger sibbed. Eventually, and this is now the video that 262 00:14:35,720 --> 00:14:37,320 Speaker 2: ever war at least a portion of the video that 263 00:14:37,360 --> 00:14:44,040 Speaker 2: everyone's now seeing. We see the car start moving because 264 00:14:45,000 --> 00:14:48,000 Speaker 2: off another officer. He actually curses, hiss, get the f 265 00:14:48,160 --> 00:14:50,080 Speaker 2: out of the vehicle. He tells me, get out of 266 00:14:50,080 --> 00:14:51,640 Speaker 2: the vehicle. I think he said that twice, and let's 267 00:14:51,720 --> 00:14:54,120 Speaker 2: us get the f out of the vehicle. Then with 268 00:14:54,480 --> 00:14:59,200 Speaker 2: his right hand he grabs the doorknob or the handle. 269 00:15:00,240 --> 00:15:04,000 Speaker 2: Left hand he grabs the window area that's actually open, 270 00:15:04,080 --> 00:15:06,920 Speaker 2: so his fingers are inside of the vehicle at this point, 271 00:15:07,120 --> 00:15:10,400 Speaker 2: so his left hand is inside of that window. His 272 00:15:10,560 --> 00:15:15,480 Speaker 2: right hand is on the vehicle door. And the vehicle 273 00:15:15,520 --> 00:15:18,800 Speaker 2: starts moving with him attached to it. And as the 274 00:15:18,880 --> 00:15:23,240 Speaker 2: vehicle moves, all of a sudden, see the other officer 275 00:15:23,360 --> 00:15:25,080 Speaker 2: in view and look at his hand. Then I'll talk 276 00:15:25,120 --> 00:15:26,720 Speaker 2: to about the movement a second. But look at what's 277 00:15:26,720 --> 00:15:30,040 Speaker 2: in his hand. It's his cell phone. He's still but 278 00:15:30,120 --> 00:15:32,000 Speaker 2: how do we get to this frame even before that, 279 00:15:32,040 --> 00:15:33,400 Speaker 2: and no one seemed to have pointed out that he 280 00:15:33,480 --> 00:15:36,040 Speaker 2: was actually just standing there recording. How did we get 281 00:15:36,080 --> 00:15:40,600 Speaker 2: to that point while he was not visible previously? Right 282 00:15:40,880 --> 00:15:45,160 Speaker 2: then he becomes visible. Now, how does that happen? She 283 00:15:45,400 --> 00:15:49,840 Speaker 2: moves her car back and straight. By doing that, she 284 00:15:50,080 --> 00:15:54,000 Speaker 2: reveals the area that was previously that driver's side door 285 00:15:54,120 --> 00:15:57,120 Speaker 2: or the driver I'm sorry, the passing side door, yeah, 286 00:15:57,360 --> 00:16:00,200 Speaker 2: passenger corner. So all of a sudden we see that 287 00:16:00,240 --> 00:16:02,040 Speaker 2: he was in fact that area that we couldn't figure 288 00:16:02,080 --> 00:16:04,120 Speaker 2: out where he was standing out in that passenger area. 289 00:16:04,320 --> 00:16:06,800 Speaker 2: It's revealed that he's standing still. He wasn't moving or 290 00:16:06,800 --> 00:16:07,880 Speaker 2: walking in that area. 291 00:16:07,920 --> 00:16:09,080 Speaker 1: He's standing still in. 292 00:16:09,000 --> 00:16:12,360 Speaker 2: That passenger area. So he didn't stand in front of 293 00:16:12,400 --> 00:16:14,680 Speaker 2: her car, He didn't get in front of her vehicle, 294 00:16:15,040 --> 00:16:18,400 Speaker 2: he didn't mission himself there. She positioned him there. 295 00:16:18,880 --> 00:16:22,360 Speaker 1: That's fascinating because the reporting is saying ice officers usually 296 00:16:22,400 --> 00:16:24,480 Speaker 1: don't approach from the front of the car, and this 297 00:16:24,600 --> 00:16:27,440 Speaker 1: is therefore bad behavior by him. But what you're saying 298 00:16:27,480 --> 00:16:29,800 Speaker 1: is that the car moved to make it the front 299 00:16:29,800 --> 00:16:31,440 Speaker 1: of the car. That's fascinating. 300 00:16:31,720 --> 00:16:36,240 Speaker 2: Correct, Okay, And in fact, he was walking around the car. 301 00:16:36,320 --> 00:16:39,160 Speaker 2: Again when we see that full video, he's actually walking 302 00:16:39,200 --> 00:16:42,600 Speaker 2: from behind to that side area and then eventually becomes 303 00:16:42,600 --> 00:16:46,280 Speaker 2: in the front. When her vehicle moves and reveals he's 304 00:16:46,320 --> 00:16:49,120 Speaker 2: standing there, she positions him to a point where he's 305 00:16:49,120 --> 00:16:51,840 Speaker 2: standing in front. He didn't, he didn't make that his 306 00:16:51,920 --> 00:16:55,480 Speaker 2: strategic position. And again he is there filming. His cell 307 00:16:55,520 --> 00:16:57,760 Speaker 2: phone is in his hand. In his hand, is his 308 00:16:57,800 --> 00:16:59,440 Speaker 2: cell phone right there? That's not a farm, that's a 309 00:16:59,440 --> 00:17:03,840 Speaker 2: cell phone. That's point in that direction. Now, only after 310 00:17:04,680 --> 00:17:08,720 Speaker 2: she switches gears, she's no longer going in reverse. 311 00:17:09,280 --> 00:17:12,040 Speaker 1: She is now wait can wait, wait, wait one saying, 312 00:17:12,119 --> 00:17:15,359 Speaker 1: so when she goes into reverse, she's clearly moving I 313 00:17:15,359 --> 00:17:18,239 Speaker 1: guess to her right. Her car is angling to her 314 00:17:18,320 --> 00:17:20,919 Speaker 1: right and then gearing out to the left because the wheels, 315 00:17:20,920 --> 00:17:22,240 Speaker 1: I think move towards the right. 316 00:17:22,320 --> 00:17:24,760 Speaker 2: Okay, the wheels at this point aren't even moving yet. 317 00:17:24,760 --> 00:17:28,639 Speaker 2: So initially she puts the car in reverse. After she 318 00:17:28,680 --> 00:17:32,080 Speaker 2: puts the car in reverse and she drives it in reverse, 319 00:17:32,280 --> 00:17:36,960 Speaker 2: she then changes gears, and she then shifts her tires 320 00:17:36,960 --> 00:17:41,480 Speaker 2: and then now pointed forwards towards the officer. The car. 321 00:17:41,640 --> 00:17:44,879 Speaker 2: The wheels of the car has three directions in that video. 322 00:17:45,440 --> 00:17:48,560 Speaker 2: Initially it's pointed towards the left of the frame of 323 00:17:48,600 --> 00:17:53,000 Speaker 2: the video, then they're pointed towards the officer and forward, 324 00:17:53,000 --> 00:17:55,359 Speaker 2: and eventually they point to the right of the frame 325 00:17:55,400 --> 00:17:58,280 Speaker 2: of the video. And so there are three separate directions 326 00:17:58,800 --> 00:18:03,000 Speaker 2: when those times are pointed towards the officer, and when 327 00:18:03,119 --> 00:18:06,400 Speaker 2: the car is in drive, and when it is approaching him, 328 00:18:06,400 --> 00:18:09,040 Speaker 2: and in fact it's accelerating towards him. As you could hear, 329 00:18:09,560 --> 00:18:13,720 Speaker 2: he draws that firearm. This is the moment that he's 330 00:18:13,840 --> 00:18:18,000 Speaker 2: drawing by firearm. Look at the tires at that moment 331 00:18:18,280 --> 00:18:21,680 Speaker 2: he's drawing right there, we see and look at her tires, Yeah, 332 00:18:21,880 --> 00:18:25,440 Speaker 2: pointed straight towards him. They are not pointed towards the 333 00:18:25,520 --> 00:18:29,600 Speaker 2: right of the frame just yet. The moment that he fires. 334 00:18:29,640 --> 00:18:31,560 Speaker 2: This is the moment that he fires. You can't see 335 00:18:31,600 --> 00:18:34,960 Speaker 2: anything specific, but this is the moment where you hear 336 00:18:35,000 --> 00:18:38,760 Speaker 2: on audio his farm coming up. The tires are pointed 337 00:18:38,800 --> 00:18:45,320 Speaker 2: straight or ward towards him. Not until after that first 338 00:18:45,440 --> 00:18:49,800 Speaker 2: bullet leaves his gun that the tires then shift to 339 00:18:49,840 --> 00:18:52,240 Speaker 2: what we see as the right of the frame, and 340 00:18:52,280 --> 00:18:55,840 Speaker 2: then she kind of wanders off. At this point, I 341 00:18:55,840 --> 00:18:59,200 Speaker 2: don't know how much control she has because a bullet. 342 00:18:59,640 --> 00:19:03,760 Speaker 1: Some are okay, so wait before before I want to 343 00:19:03,760 --> 00:19:05,480 Speaker 1: go into there. I have a few other questions that 344 00:19:05,520 --> 00:19:08,439 Speaker 1: were asked from people there. People were saying online, I 345 00:19:08,480 --> 00:19:11,160 Speaker 1: wanted a lawyer to clear up. So now it looks 346 00:19:11,200 --> 00:19:13,720 Speaker 1: like she did leave. She did waive at least one 347 00:19:13,880 --> 00:19:17,880 Speaker 1: car to go before she stopped other cars, maybe trying 348 00:19:17,880 --> 00:19:20,040 Speaker 1: to slow just ICE agents down. It wasn't exactly sure. 349 00:19:20,400 --> 00:19:23,120 Speaker 1: People say online that ICE agents have no legal right 350 00:19:23,200 --> 00:19:26,639 Speaker 1: to interrogate or arrest American citizens. Is that true? 351 00:19:28,000 --> 00:19:32,280 Speaker 2: Was that the position that people held on January sixth, 352 00:19:32,520 --> 00:19:36,359 Speaker 2: when a lot of liberals advocated for American citizens to 353 00:19:36,520 --> 00:19:40,680 Speaker 2: be investigated for federal offenses. No, that's obviously not the case. 354 00:19:40,720 --> 00:19:46,960 Speaker 2: Federal okay, can enforced federal laws, and depending on the situation, 355 00:19:47,160 --> 00:19:50,080 Speaker 2: there are different laws that are applicable here. Like I said, 356 00:19:50,119 --> 00:19:54,520 Speaker 2: eighteen USC one eleven, that's the obstruction of federal law 357 00:19:54,640 --> 00:19:56,840 Speaker 2: enforcement and the performance of their duties. It's that code 358 00:19:56,840 --> 00:20:00,399 Speaker 2: section that appears to have been the reason why they stopped. 359 00:20:00,440 --> 00:20:03,879 Speaker 2: Her vehicle was obstructing them as they were attempting to 360 00:20:03,920 --> 00:20:07,360 Speaker 2: perform their job. She was impeding them, so they at 361 00:20:07,359 --> 00:20:09,800 Speaker 2: that point were investigating her for a violation of that 362 00:20:09,880 --> 00:20:13,760 Speaker 2: code section. You had a simple suspicion to stop a vehicle. 363 00:20:14,119 --> 00:20:16,280 Speaker 2: They had that reasonable suspicion at that time. 364 00:20:16,520 --> 00:20:17,000 Speaker 1: Did they have. 365 00:20:16,960 --> 00:20:19,399 Speaker 2: Probably bosts to arrest her for that, I don't know. 366 00:20:19,440 --> 00:20:20,720 Speaker 2: Maybe they had, Maybe. 367 00:20:20,560 --> 00:20:22,439 Speaker 1: They did not. They did ask her to get out 368 00:20:22,480 --> 00:20:24,760 Speaker 1: of the car. Yes, and they attempted to remove from 369 00:20:24,760 --> 00:20:27,640 Speaker 1: the car. That's the thing that is a clear thing 370 00:20:27,680 --> 00:20:29,320 Speaker 1: that they didn't just go to shoot. They asked to 371 00:20:29,320 --> 00:20:31,400 Speaker 1: get her out of the car. Now you I want 372 00:20:31,440 --> 00:20:34,280 Speaker 1: to my listeners to hear this. Using cars to stop 373 00:20:34,320 --> 00:20:37,720 Speaker 1: ICE agents from performing their practices is becoming very normal 374 00:20:37,760 --> 00:20:40,720 Speaker 1: by anti ICE protesters. In Minneapolis just six months ago, 375 00:20:40,800 --> 00:20:43,600 Speaker 1: this ICE agent who did the shooting received thirty three 376 00:20:43,600 --> 00:20:45,920 Speaker 1: stitches after he was dragged by three for three hundred 377 00:20:45,920 --> 00:20:48,680 Speaker 1: feet by a protesters car. There was another attack last 378 00:20:48,800 --> 00:20:51,480 Speaker 1: month in Minneapolis where someone rammed their car into agents 379 00:20:51,920 --> 00:20:53,919 Speaker 1: we're trying to arrest in legal alien. There have been 380 00:20:54,000 --> 00:20:57,400 Speaker 1: twenty eight known ramming incidences in the last several months 381 00:20:57,400 --> 00:20:59,800 Speaker 1: against ICE agents, eight thirteen hundred percent increase in the 382 00:20:59,880 --> 00:21:01,960 Speaker 1: US year prior. Now let's go back to the case. 383 00:21:02,160 --> 00:21:04,240 Speaker 1: Agents approach the car, they try to open the door. 384 00:21:04,440 --> 00:21:06,679 Speaker 1: That's when Renee backs up and they start yelling at her, 385 00:21:06,920 --> 00:21:10,760 Speaker 1: you know, stop, and then she goes. Is there a 386 00:21:10,920 --> 00:21:15,119 Speaker 1: real case of self defense for the ice agent you 387 00:21:15,200 --> 00:21:18,320 Speaker 1: mentioned the tires, Is the fact that he's shot multiple 388 00:21:18,359 --> 00:21:22,040 Speaker 1: times a case against him like that was excessive force. 389 00:21:22,680 --> 00:21:25,600 Speaker 2: That's a good question because there's been This is not 390 00:21:25,640 --> 00:21:28,200 Speaker 2: obviously the first officer who's ever used force. There's been 391 00:21:28,440 --> 00:21:31,359 Speaker 2: quite a few expert witnesses who have testified in a 392 00:21:31,440 --> 00:21:34,959 Speaker 2: variety of these law enforcement use to force cases that 393 00:21:35,040 --> 00:21:37,359 Speaker 2: have all said that once an officer is committed to 394 00:21:37,480 --> 00:21:41,440 Speaker 2: firing that first shot, can't control how many come out. 395 00:21:41,560 --> 00:21:44,400 Speaker 2: So the reality is that an officer can fire one, two, 396 00:21:44,480 --> 00:21:46,800 Speaker 2: three bullets and all of that is part of that 397 00:21:46,920 --> 00:21:50,359 Speaker 2: initial mindset where I'm going to use force right now 398 00:21:50,400 --> 00:21:54,600 Speaker 2: to deter the danger, and the idea that well, after 399 00:21:54,880 --> 00:22:00,760 Speaker 2: he fires that first bullet, though, wheels then shipped away 400 00:22:00,880 --> 00:22:03,560 Speaker 2: from him and the car starts hearing off, that he 401 00:22:03,600 --> 00:22:06,560 Speaker 2: at that point should have stopped firing. That's not how 402 00:22:06,640 --> 00:22:09,479 Speaker 2: human brains work. Once they perceived that threat, they commit 403 00:22:09,800 --> 00:22:12,480 Speaker 2: to deterring the threat, and that's exactly what he did. Here, 404 00:22:12,800 --> 00:22:15,399 Speaker 2: if he would have fired one shot, I would have 405 00:22:15,440 --> 00:22:19,159 Speaker 2: been surprised, to be perfectly honest, because that adrenaline is 406 00:22:19,200 --> 00:22:22,320 Speaker 2: pumping through a man's body he doesn't have. That's a 407 00:22:22,359 --> 00:22:25,080 Speaker 2: type of control where he's going to necessarily fire just 408 00:22:25,240 --> 00:22:27,880 Speaker 2: want or to. I think three tends to be average 409 00:22:27,920 --> 00:22:31,520 Speaker 2: in terms of how many shots are fired when the 410 00:22:31,800 --> 00:22:36,840 Speaker 2: individual who's holding a firearm commits to using force as 411 00:22:36,840 --> 00:22:38,560 Speaker 2: self defense or lethal force. 412 00:22:39,240 --> 00:22:41,680 Speaker 1: And now, and a federal agent has or any law 413 00:22:41,800 --> 00:22:45,360 Speaker 1: enforcement has there this claim, and I think that it's 414 00:22:45,400 --> 00:22:47,520 Speaker 1: a joke. But I mean I have to address because 415 00:22:47,560 --> 00:22:49,840 Speaker 1: some people have said it that police officers and law 416 00:22:49,880 --> 00:22:52,920 Speaker 1: person have to retreat before shooting. Is that true? 417 00:22:53,440 --> 00:22:57,159 Speaker 2: It is absolutely not true. Under no circumstances do federal 418 00:22:57,240 --> 00:23:01,679 Speaker 2: law enforcement officers have to retreat before using force. I mean, 419 00:23:01,760 --> 00:23:04,639 Speaker 2: that is insane and absurd that that's even a conversation. 420 00:23:05,080 --> 00:23:08,760 Speaker 2: I've heard on CNN experts saying that the officer should 421 00:23:08,800 --> 00:23:12,320 Speaker 2: have just moved out of the way. That in that second, 422 00:23:12,480 --> 00:23:16,800 Speaker 2: and it's literally a second between her car accelerating towards 423 00:23:16,880 --> 00:23:20,560 Speaker 2: him and him firing. It's about one second. He has 424 00:23:20,640 --> 00:23:22,440 Speaker 2: to at that point drawing his farm and make a 425 00:23:22,520 --> 00:23:26,399 Speaker 2: decision to use it because she continues moving towards him. 426 00:23:26,400 --> 00:23:29,280 Speaker 2: And by the way, after he pulls out his firearm, 427 00:23:29,320 --> 00:23:31,800 Speaker 2: if you watch it really slow, frame by frame, he 428 00:23:32,040 --> 00:23:35,000 Speaker 2: points it. She continues moving towards him as he points, 429 00:23:35,400 --> 00:23:37,840 Speaker 2: So he's pointing, she's moving towards him, and then he 430 00:23:37,920 --> 00:23:41,560 Speaker 2: takes the shot, so there is a forward progress. Right 431 00:23:41,680 --> 00:23:44,959 Speaker 2: before he takes the shot, it's continuing straight towards him, 432 00:23:45,240 --> 00:23:47,280 Speaker 2: and he makes the decision. He commits to using the 433 00:23:47,359 --> 00:23:50,760 Speaker 2: force there. He has absolutely no duty to retreat, which 434 00:23:50,800 --> 00:23:54,080 Speaker 2: means to evaluate his surroundings and figure out where else 435 00:23:54,200 --> 00:23:56,639 Speaker 2: he can go to try and avoid getting hit by 436 00:23:56,760 --> 00:24:01,439 Speaker 2: a car that is pointed or became pointed directly at it. Remember, 437 00:24:01,520 --> 00:24:04,560 Speaker 2: he did not position himself there. She positioned her car 438 00:24:04,680 --> 00:24:07,600 Speaker 2: to point at him. She shows this. 439 00:24:08,320 --> 00:24:10,920 Speaker 1: A lot of progressive Democrats are also saying that she's 440 00:24:10,960 --> 00:24:15,160 Speaker 1: a quote legal observer. I have never heard this term before, 441 00:24:15,200 --> 00:24:17,760 Speaker 1: but as some law group keep saying, she's just a 442 00:24:17,920 --> 00:24:20,440 Speaker 1: legal observer, and that's what they're saying. These aenti ice 443 00:24:20,520 --> 00:24:23,560 Speaker 1: protesters are their legal observers, like kind of like a journalist, 444 00:24:23,720 --> 00:24:24,720 Speaker 1: do you know what this is? 445 00:24:25,640 --> 00:24:30,119 Speaker 2: So there are various groups of people who call themselves 446 00:24:30,320 --> 00:24:33,960 Speaker 2: observers or First Amendment auditors was the term I've heard 447 00:24:34,000 --> 00:24:37,320 Speaker 2: before as well, And they follow law enforcement officers around 448 00:24:37,440 --> 00:24:39,960 Speaker 2: and they try to record them, hoping that they catch 449 00:24:40,080 --> 00:24:43,760 Speaker 2: them doing something unlawful. So that is a thing that exists. 450 00:24:43,840 --> 00:24:46,480 Speaker 2: Whether she was doing that or not, I don't know. 451 00:24:46,600 --> 00:24:49,320 Speaker 2: What I do know from the evidence that we saw 452 00:24:49,720 --> 00:24:54,280 Speaker 2: is that she was interfering with their work that day. Right, 453 00:24:54,359 --> 00:24:57,639 Speaker 2: she's talking police vehicles. She's not the one who should 454 00:24:57,640 --> 00:25:01,000 Speaker 2: be directing where these federal vehicles are. She should write 455 00:25:01,359 --> 00:25:04,600 Speaker 2: at traffic positioning her car unlawfully in the street. She 456 00:25:04,720 --> 00:25:08,719 Speaker 2: is a very active participant, not an observer. So an 457 00:25:08,760 --> 00:25:11,560 Speaker 2: observer just watches and records. And there are plenty of 458 00:25:11,600 --> 00:25:15,560 Speaker 2: people who were observing and recording, even the talking, screaming things. 459 00:25:15,880 --> 00:25:19,840 Speaker 2: None of those people were directly interfering with law enforcement actions. 460 00:25:20,160 --> 00:25:22,600 Speaker 2: She was doing something different, which is why they, by 461 00:25:22,640 --> 00:25:24,960 Speaker 2: the way, did not get shot. It's because they weren't 462 00:25:25,000 --> 00:25:28,240 Speaker 2: doing anything and they weren't interfering. She ends up getting 463 00:25:28,320 --> 00:25:32,560 Speaker 2: stopped by police because of her actions, drags one officer 464 00:25:32,720 --> 00:25:35,200 Speaker 2: on her vehicle as she's moving. Don't forget that second 465 00:25:35,240 --> 00:25:38,080 Speaker 2: officer is attached we'll talk about protection of third party 466 00:25:38,119 --> 00:25:40,879 Speaker 2: in a second, because there's two officers who were assaulted here, 467 00:25:41,400 --> 00:25:46,920 Speaker 2: and then positions her vehicle and drives with forward progress, 468 00:25:47,040 --> 00:25:50,040 Speaker 2: accelerating towards the officer who later used force. That's what 469 00:25:50,160 --> 00:25:54,320 Speaker 2: she was doing. So that's not observation at all, participation 470 00:25:54,560 --> 00:25:57,359 Speaker 2: to a degree that caused for her to lose her life, 471 00:25:57,840 --> 00:26:01,480 Speaker 2: and it is unfortunate that life is lost. However, she 472 00:26:02,080 --> 00:26:07,600 Speaker 2: made choices that escalated to that point. Right, we're not 473 00:26:07,720 --> 00:26:10,600 Speaker 2: to learn from that right for observers or wherever else. 474 00:26:10,840 --> 00:26:14,359 Speaker 2: If you won't observe, fine, stand the side of the road, observe, 475 00:26:14,720 --> 00:26:16,879 Speaker 2: don't participate that free right. 476 00:26:16,960 --> 00:26:20,600 Speaker 1: And the eyewitness said quote she was very successfully blocking traffic. 477 00:26:20,720 --> 00:26:22,960 Speaker 1: She was successful in trying to do and what she 478 00:26:23,119 --> 00:26:25,560 Speaker 1: was trying to do, that's what That's what her participant 479 00:26:25,640 --> 00:26:28,040 Speaker 1: in the protest said. And it was that, and it 480 00:26:28,160 --> 00:26:31,000 Speaker 1: was corroborated by someone else who was a witness there. 481 00:26:31,000 --> 00:26:33,320 Speaker 1: I think someone who owned their home there. And your 482 00:26:33,440 --> 00:26:36,119 Speaker 1: car would not be perpendicular unless you were making a 483 00:26:36,240 --> 00:26:39,440 Speaker 1: K turn or backing up into a parking spot. She 484 00:26:39,640 --> 00:26:41,560 Speaker 1: was doing neither a witch or or I guess if 485 00:26:41,560 --> 00:26:43,879 Speaker 1: your car skid out, but her car wasn't skinning out, 486 00:26:43,880 --> 00:26:45,920 Speaker 1: it was purposely there. And as she said with the video, 487 00:26:46,280 --> 00:26:49,720 Speaker 1: there's a conversation between the officer and the wife. So 488 00:26:49,880 --> 00:26:52,520 Speaker 1: they're there for some time. It's not like they just 489 00:26:53,119 --> 00:26:55,440 Speaker 1: it just you know, she reared in, made it like, 490 00:26:55,520 --> 00:26:57,920 Speaker 1: you know, made a crazy turn, and she's stuck there. 491 00:26:58,400 --> 00:27:01,520 Speaker 1: They're there in parked for quite a she had actually 492 00:27:01,640 --> 00:27:04,800 Speaker 1: quite some time to lead the situation. Talk about the 493 00:27:04,880 --> 00:27:09,280 Speaker 1: third party that you've mentioned before, so you mentioned. 494 00:27:09,080 --> 00:27:12,280 Speaker 2: This new revelation that we learned today that the officer 495 00:27:12,480 --> 00:27:16,480 Speaker 2: who fired the defensive shots that he was about six 496 00:27:16,600 --> 00:27:20,919 Speaker 2: or seven months ago involved in another assault by vehicle 497 00:27:21,119 --> 00:27:22,960 Speaker 2: where our car dragged him I think it was one 498 00:27:23,040 --> 00:27:25,320 Speaker 2: hundred feet was dragged and he had serious injuries to 499 00:27:25,400 --> 00:27:27,879 Speaker 2: his hand and arm. And in this case, one of 500 00:27:28,000 --> 00:27:31,399 Speaker 2: the officers involved, the one who tried to open the 501 00:27:31,440 --> 00:27:33,399 Speaker 2: door and put his left hand on the inside of 502 00:27:33,440 --> 00:27:36,920 Speaker 2: the door. He was attached and holding on to that 503 00:27:37,320 --> 00:27:41,760 Speaker 2: Burgundy suv as it was reversing and then pulling forward, 504 00:27:42,119 --> 00:27:45,600 Speaker 2: So he was touching that vehicle, attached with the entire time. 505 00:27:46,240 --> 00:27:49,359 Speaker 2: And so when the officer is firing that bullet, and 506 00:27:49,760 --> 00:27:52,119 Speaker 2: this is obviously going to have to be articulated by him. 507 00:27:52,160 --> 00:27:54,000 Speaker 2: Whether or not he was thinking this, I don't know, 508 00:27:54,520 --> 00:27:58,479 Speaker 2: but presumably, considering what we know about him, he may 509 00:27:58,600 --> 00:28:01,159 Speaker 2: also have been thinking he's acting in protection of a 510 00:28:01,280 --> 00:28:04,119 Speaker 2: third party, which is the second officer. So this is 511 00:28:04,160 --> 00:28:08,399 Speaker 2: a s andred that allows a police officer or anyone 512 00:28:08,480 --> 00:28:13,920 Speaker 2: really to use self defense or le or to use 513 00:28:14,240 --> 00:28:18,200 Speaker 2: a firearm in order to protect the life or safety 514 00:28:18,320 --> 00:28:20,800 Speaker 2: of another being in here, it would be the officer 515 00:28:20,880 --> 00:28:22,960 Speaker 2: attached to the vehicle. So he may have been thinking, oh, 516 00:28:23,040 --> 00:28:24,560 Speaker 2: my god, this officer is going to get dragged just 517 00:28:24,640 --> 00:28:26,199 Speaker 2: like I did to protect him. 518 00:28:26,240 --> 00:28:29,240 Speaker 1: I have to stop this vehicle, right. That's fascinating, and 519 00:28:29,240 --> 00:28:32,640 Speaker 1: that's absolutely fascinating. What I said was, you know, it's 520 00:28:32,680 --> 00:28:35,200 Speaker 1: a tragy that anyone lost her life, especially someone who 521 00:28:35,200 --> 00:28:37,639 Speaker 1: has got kids. And I you know, it's it's terrible. 522 00:28:37,800 --> 00:28:39,960 Speaker 1: But what it looks like to me, based upon what 523 00:28:40,040 --> 00:28:42,080 Speaker 1: the wife said right afterwards, the wife said, this is 524 00:28:42,160 --> 00:28:43,960 Speaker 1: my fault. I told her to come down here. The 525 00:28:44,040 --> 00:28:46,560 Speaker 1: wife is clearly yelling at the officers and participating in 526 00:28:46,600 --> 00:28:49,760 Speaker 1: some kind of demonstration. This is a woman who looks 527 00:28:49,840 --> 00:28:52,960 Speaker 1: like she was told that orange man bad. This is 528 00:28:53,080 --> 00:28:55,600 Speaker 1: you know every kind of suit, every kind of thing 529 00:28:55,640 --> 00:28:58,360 Speaker 1: you've ever heard about ice being talked about before. And 530 00:28:58,720 --> 00:29:00,720 Speaker 1: she decided to get involved in a pro her wife 531 00:29:00,760 --> 00:29:03,200 Speaker 1: told us sit there and do something allegedly according to 532 00:29:03,280 --> 00:29:06,680 Speaker 1: what the wife said, and she made a very bad 533 00:29:06,760 --> 00:29:09,200 Speaker 1: decision in literally a snap second. If she would have 534 00:29:09,240 --> 00:29:11,640 Speaker 1: gotten out of her vehicle like the agent said, she 535 00:29:11,760 --> 00:29:14,800 Speaker 1: would be alive today. But by I have no reason 536 00:29:14,840 --> 00:29:17,720 Speaker 1: to presume otherwise. You wrote something really profound. I want 537 00:29:17,720 --> 00:29:20,040 Speaker 1: to conclude on this, but you weren't very to me 538 00:29:20,240 --> 00:29:22,680 Speaker 1: profound on Twitter, and that I wanted you to talk 539 00:29:22,720 --> 00:29:25,120 Speaker 1: about you said quote. This is the reality that no 540 00:29:25,200 --> 00:29:27,960 Speaker 1: one ever discusses that there is a cost enforcing the laws. 541 00:29:28,000 --> 00:29:30,600 Speaker 1: Society pays this cost for both loss of officers and 542 00:29:30,640 --> 00:29:33,920 Speaker 1: loss of civilians for various reasons associated with society granting 543 00:29:33,960 --> 00:29:37,040 Speaker 1: police officers the right to use force to effectuate the 544 00:29:37,200 --> 00:29:40,160 Speaker 1: will of the legislature. Details aside, something like this was 545 00:29:40,200 --> 00:29:42,720 Speaker 1: bound to happen, and other instances like this will continue 546 00:29:42,760 --> 00:29:46,600 Speaker 1: to happen. Lastly, if Americans want America wants to continue deportations, 547 00:29:46,800 --> 00:29:49,320 Speaker 1: instances like this cannon will continue to happen. It is 548 00:29:49,440 --> 00:29:51,840 Speaker 1: a cost in America is willing to pay question RK 549 00:29:51,920 --> 00:29:55,440 Speaker 1: considering that infrequency of such incidents, as I would guess, yes, 550 00:29:56,560 --> 00:29:59,880 Speaker 1: you're saying that, I mean, explain that as a whole, 551 00:30:00,360 --> 00:30:04,200 Speaker 1: that that this is. Yes, it's tragic, but it also 552 00:30:04,520 --> 00:30:08,239 Speaker 1: it didn't It didn't it was I wasn't unfazed by 553 00:30:08,440 --> 00:30:11,000 Speaker 1: how we've deported I think six or seven hundred thousand 554 00:30:11,080 --> 00:30:15,720 Speaker 1: people and how this hasn't happened yet. So do you 555 00:30:15,760 --> 00:30:17,400 Speaker 1: want to is this is Do you think that this 556 00:30:17,600 --> 00:30:19,640 Speaker 1: is for everyone who says the ICE agents are these 557 00:30:19,760 --> 00:30:23,920 Speaker 1: rogue mass people kidnapping people and randomly deport them, it seems, 558 00:30:24,280 --> 00:30:28,080 Speaker 1: and that they're quote unquote untrained. They seem fairly well trained. 559 00:30:28,480 --> 00:30:32,560 Speaker 1: Given that it's been a year basically since since Trump 560 00:30:32,640 --> 00:30:35,200 Speaker 1: took over and we have this is the first time 561 00:30:35,200 --> 00:30:35,640 Speaker 1: we're seeing this. 562 00:30:36,960 --> 00:30:41,760 Speaker 2: American law enforcement officers are incredibly well trained. One of 563 00:30:42,040 --> 00:30:44,640 Speaker 2: the things that America really needs to pad itself in 564 00:30:44,720 --> 00:30:49,720 Speaker 2: the back for is training for police officers up and down, local, state, federal. 565 00:30:49,960 --> 00:30:53,880 Speaker 2: We have incredible law enforcement officers, and a lot of 566 00:30:53,960 --> 00:30:56,760 Speaker 2: that has to do with their training. American character is 567 00:30:56,760 --> 00:30:59,920 Speaker 2: also part of that, right, the personality of the people too. 568 00:31:00,040 --> 00:31:04,080 Speaker 2: Using that employment. But it's also that training and Americans 569 00:31:04,160 --> 00:31:07,920 Speaker 2: overall are kept safe because of good training, and so, yes, 570 00:31:07,960 --> 00:31:10,400 Speaker 2: you're absolutely correct, it is extremely rare for us to 571 00:31:10,480 --> 00:31:14,120 Speaker 2: see these incidents, even though they should be expected because 572 00:31:14,160 --> 00:31:16,880 Speaker 2: of that training. The training we provide our officers is 573 00:31:16,920 --> 00:31:19,560 Speaker 2: incredible and we only improve it. Right, We've got so 574 00:31:19,720 --> 00:31:24,160 Speaker 2: many officers who are captains lieutenants. These are people who 575 00:31:24,320 --> 00:31:26,600 Speaker 2: work on training these officers, and they're constantly trying to 576 00:31:26,680 --> 00:31:29,800 Speaker 2: improve training, learning from each incident. What could we have 577 00:31:29,880 --> 00:31:32,680 Speaker 2: done differently? What could we have done better? Including this one. 578 00:31:32,720 --> 00:31:34,160 Speaker 2: I'm sure officers are going to be looking at this 579 00:31:34,280 --> 00:31:36,440 Speaker 2: up and down and thinking, Okay, how could we have 580 00:31:36,600 --> 00:31:40,280 Speaker 2: avoided this? What could we have done differently? Because officers 581 00:31:40,360 --> 00:31:43,520 Speaker 2: want to save lives of the civilians who they're arresting. 582 00:31:44,320 --> 00:31:48,600 Speaker 2: In fact, there are images of officers rendering aid to 583 00:31:48,680 --> 00:31:50,920 Speaker 2: this one because that's what our officers do. As soon 584 00:31:51,200 --> 00:31:54,160 Speaker 2: as an offender is shot by an officer, they render 585 00:31:54,240 --> 00:31:56,719 Speaker 2: aid medical aid right away. That's what was happening here. 586 00:31:56,960 --> 00:31:59,320 Speaker 2: But yes, there is a cost to enforcing the lawns 587 00:31:59,360 --> 00:32:02,800 Speaker 2: aside cost is through use of force, right, because the 588 00:32:03,120 --> 00:32:06,760 Speaker 2: concept of law enforcement is that in order to effectuate 589 00:32:07,160 --> 00:32:12,640 Speaker 2: the laws written by our legislators. The police officers have 590 00:32:12,800 --> 00:32:16,600 Speaker 2: to be able to use force. That's their tool in 591 00:32:16,680 --> 00:32:21,400 Speaker 2: effectuating those laws. And the initial use of force is 592 00:32:21,640 --> 00:32:25,520 Speaker 2: the voice, a vocal command, and then it goes. It 593 00:32:25,760 --> 00:32:29,239 Speaker 2: escalates from that to hands, to tools to firearms. Right, 594 00:32:30,040 --> 00:32:35,760 Speaker 2: there is escalating level and our officers will eventually in 595 00:32:35,920 --> 00:32:38,760 Speaker 2: various incidents for various reasons. In this case, it was 596 00:32:38,920 --> 00:32:41,560 Speaker 2: caused by the desire of a woman to not want 597 00:32:41,600 --> 00:32:45,040 Speaker 2: to comply. But in various incidents there'll be reasons to 598 00:32:45,920 --> 00:32:49,360 Speaker 2: use force that could result in debt. And this is 599 00:32:49,560 --> 00:32:53,240 Speaker 2: a cost that society pays when enforcing the law written 600 00:32:53,280 --> 00:32:55,840 Speaker 2: by legislators. And here that's exactly what they were doing 601 00:32:55,840 --> 00:32:58,720 Speaker 2: in Congress, and it is unlawful to come to this 602 00:32:59,040 --> 00:33:02,480 Speaker 2: country and lawfully they created parameters and create a criminal 603 00:33:02,520 --> 00:33:06,520 Speaker 2: offenses for these ice officers to enforce. And that's what 604 00:33:06,600 --> 00:33:09,000 Speaker 2: they were there doing. And they were at work doing that, 605 00:33:09,120 --> 00:33:11,440 Speaker 2: and this woman interfered with their performance and one thing 606 00:33:11,560 --> 00:33:13,880 Speaker 2: led to another and now she's the sea stone where 607 00:33:13,920 --> 00:33:14,600 Speaker 2: he're discussing it. 608 00:33:15,360 --> 00:33:18,200 Speaker 1: No, okay, I have one more question. Actually, since there 609 00:33:18,320 --> 00:33:20,360 Speaker 1: was a neighbor who said I'm a medic, can I 610 00:33:20,480 --> 00:33:22,240 Speaker 1: go check on her, and they were not allowed to. 611 00:33:22,480 --> 00:33:23,840 Speaker 1: Why was that appropriate? 612 00:33:24,240 --> 00:33:27,360 Speaker 2: It's a crime scene, you do not under any circumstances. 613 00:33:27,400 --> 00:33:30,320 Speaker 2: There is no law enforcement officer who's going to allow 614 00:33:30,880 --> 00:33:34,880 Speaker 2: civilians to come in and to do anything unless there's 615 00:33:34,880 --> 00:33:38,240 Speaker 2: an emergency. They had bounds of officers who were capable 616 00:33:38,320 --> 00:33:41,560 Speaker 2: and trained of rendering aid, some civilians saying oh, hey, 617 00:33:41,920 --> 00:33:45,960 Speaker 2: I'm a nurse, I'm a doctor's unless it's an emergency, 618 00:33:46,000 --> 00:33:48,480 Speaker 2: they're not going to let that person enter crime scene. 619 00:33:48,520 --> 00:33:51,200 Speaker 2: But at that point, it's a crime scene and they 620 00:33:51,400 --> 00:33:54,360 Speaker 2: need to very carefully preserve all the evidence. They also 621 00:33:54,480 --> 00:33:57,200 Speaker 2: need to make sure that this is not a person 622 00:33:57,240 --> 00:34:00,760 Speaker 2: who's a threat, because those officers were surrounded by people 623 00:34:00,760 --> 00:34:03,160 Speaker 2: who post a threat to them, and so I think 624 00:34:03,560 --> 00:34:05,400 Speaker 2: mainly for the reason that it's a crime scene, but 625 00:34:05,520 --> 00:34:08,320 Speaker 2: also because they don't know who this person is and 626 00:34:08,480 --> 00:34:10,920 Speaker 2: they've been threatened by so many individuals in that group. 627 00:34:11,000 --> 00:34:13,200 Speaker 2: I mean, there's other videos from that day of people 628 00:34:13,360 --> 00:34:16,279 Speaker 2: just surrounding, abducting. Under the law, it meets a legal 629 00:34:16,360 --> 00:34:19,760 Speaker 2: definition of abduction. These officers surrounding and preventing their movement 630 00:34:19,840 --> 00:34:22,520 Speaker 2: for hours, and so this idea of well, we should 631 00:34:22,600 --> 00:34:25,600 Speaker 2: allow these same people to come in and interfere with 632 00:34:25,680 --> 00:34:28,600 Speaker 2: this crime scene or as they're claiming to render medical aid. No, 633 00:34:28,960 --> 00:34:32,160 Speaker 2: I don't think that any supervisor on sceenior officer would 634 00:34:32,200 --> 00:34:32,680 Speaker 2: permit that. 635 00:34:33,080 --> 00:34:35,040 Speaker 1: Well, you are so so smart. Where can we'll go 636 00:34:35,120 --> 00:34:37,480 Speaker 1: to read more of your stuff? What's your Twitter handle? 637 00:34:37,840 --> 00:34:39,440 Speaker 1: Give everyone your social media? 638 00:34:39,880 --> 00:34:43,239 Speaker 2: Well, my social media is at Marina Medvin. I only 639 00:34:43,360 --> 00:34:46,279 Speaker 2: use Twitter, and I use that in between work at 640 00:34:46,280 --> 00:34:48,919 Speaker 2: the Wise Sage. I work, but Twitter is my point 641 00:34:48,960 --> 00:34:52,440 Speaker 2: of distraction and entertainment, and so I will ranmomly discuss 642 00:34:52,960 --> 00:34:57,400 Speaker 2: my viewpoints on legal issues or political issues in between 643 00:34:57,480 --> 00:34:58,839 Speaker 2: work to keep myself us. 644 00:35:00,000 --> 00:35:01,960 Speaker 1: Oh you're so smart. Thank you for doing this podcast. 645 00:35:02,000 --> 00:35:02,759 Speaker 1: I really appreciate it. 646 00:35:03,120 --> 00:35:03,440 Speaker 2: Thank you. 647 00:35:06,960 --> 00:35:08,840 Speaker 1: Now it's time to Ask Me Anything segment. If you 648 00:35:08,920 --> 00:35:11,280 Speaker 1: want be part of my Asking Me Anything segment, email 649 00:35:11,320 --> 00:35:14,040 Speaker 1: me Ryan at Numbers Gamepodcast dot com. That's Ryan at 650 00:35:14,160 --> 00:35:17,000 Speaker 1: Numbers Plural Numbers Game Podcast dot com. I love these 651 00:35:17,080 --> 00:35:20,480 Speaker 1: questions are so fascinating. To my last listener last week, 652 00:35:20,520 --> 00:35:22,520 Speaker 1: we mentioned a book about in New York City in 653 00:35:22,719 --> 00:35:25,239 Speaker 1: the late eighties and early nineties. I actually bought the book. 654 00:35:25,239 --> 00:35:26,600 Speaker 1: I can't wait to read it. So thank you for 655 00:35:26,680 --> 00:35:28,919 Speaker 1: that recommendation. I will tell you guys on the show 656 00:35:28,920 --> 00:35:31,680 Speaker 1: how I like it. This question comes from Lee. He says, 657 00:35:31,840 --> 00:35:34,399 Speaker 1: I have been a listener since day one. Lee, thank 658 00:35:34,480 --> 00:35:36,640 Speaker 1: you from the bottom of my heart. I can't tell 659 00:35:36,680 --> 00:35:39,000 Speaker 1: you that that means so much to me, and you're 660 00:35:39,000 --> 00:35:41,720 Speaker 1: a big fan since my first appearance on and Culture Substack. 661 00:35:42,120 --> 00:35:44,920 Speaker 1: My question is this, is there any data on how 662 00:35:45,000 --> 00:35:47,320 Speaker 1: Venezuelan's voted in New York City in the mayoral election? 663 00:35:47,480 --> 00:35:50,200 Speaker 1: And broadly, is there any data on voting patterns and 664 00:35:50,320 --> 00:35:54,439 Speaker 1: immigrants from communists and former communist countries versus other immigrant groups? 665 00:35:54,640 --> 00:35:57,680 Speaker 1: Pledger thanks to the question. Fascinating question. So New York 666 00:35:57,719 --> 00:36:00,800 Speaker 1: City does not have a tremendously large event as population. 667 00:36:01,360 --> 00:36:04,520 Speaker 1: The area with the largest concentration of Venezuelans is a 668 00:36:04,600 --> 00:36:08,680 Speaker 1: neighborhood called Jackson Heights, Queens. It also happens to also 669 00:36:08,760 --> 00:36:13,040 Speaker 1: the largest concentrations of Southeast Asians, so Mandani won there 670 00:36:13,120 --> 00:36:16,759 Speaker 1: by a pretty large margin. But because they live all integrated, 671 00:36:16,800 --> 00:36:19,600 Speaker 1: it's very difficult to sit there and tel New York's 672 00:36:19,680 --> 00:36:25,759 Speaker 1: Latino communities are basically primarily Puerto Rican Dominican, and there's 673 00:36:25,760 --> 00:36:28,080 Speaker 1: small pockets of Mexicans and other ones, but it's really 674 00:36:28,320 --> 00:36:31,160 Speaker 1: Puerto Ricans and Dominicans make up the large chunk. How 675 00:36:31,400 --> 00:36:35,040 Speaker 1: other former communist countries that answer I can actually give you. 676 00:36:35,440 --> 00:36:37,759 Speaker 1: So there's a couple of different groups that you could 677 00:36:37,760 --> 00:36:40,160 Speaker 1: sit there and look at. First, Russians in South Brooklyn 678 00:36:40,280 --> 00:36:46,360 Speaker 1: voted overwhelmingly in opposition to Mandani. It was not even close. 679 00:36:46,840 --> 00:36:51,080 Speaker 1: Then you have communities like the Chinese communities over in Flushing, Queens, 680 00:36:51,160 --> 00:36:54,560 Speaker 1: over in Southern Brooklyn and the Chinatown areas, they also 681 00:36:55,000 --> 00:36:59,080 Speaker 1: voted against Mandani. Other Asians which are often countered when 682 00:36:59,080 --> 00:37:02,360 Speaker 1: they cluster in Asians together are Southeast Asians like Indians, 683 00:37:02,440 --> 00:37:05,120 Speaker 1: Pakistanis they voted Foreman Dinning by big numbers. But the 684 00:37:05,160 --> 00:37:09,400 Speaker 1: East Asian communities in South Brooklyn and Queens they've pretty 685 00:37:09,480 --> 00:37:13,600 Speaker 1: much voted pretty heavily for Cuomo in that election. And 686 00:37:13,719 --> 00:37:16,239 Speaker 1: then lastly, another group that I could possibly think of 687 00:37:16,360 --> 00:37:19,440 Speaker 1: is the Polish community. So Polish community is very interesting 688 00:37:19,480 --> 00:37:22,520 Speaker 1: because there's two major pockets I can think of. There's 689 00:37:22,760 --> 00:37:26,879 Speaker 1: part in Brooklyn in Greenpoint, Brooklyn, but Greenpoint has become 690 00:37:27,000 --> 00:37:30,040 Speaker 1: hipsterdom like it was when I was growing up. Everyone 691 00:37:30,120 --> 00:37:32,960 Speaker 1: in green Point was Polish. Everyone was one hundred percent Polish. 692 00:37:33,000 --> 00:37:35,719 Speaker 1: It was little poland the churches were in Polish. That 693 00:37:35,880 --> 00:37:39,239 Speaker 1: has changed substantially because Greenpoint is right next to Williamsburg. 694 00:37:39,480 --> 00:37:42,279 Speaker 1: As Williamsburg became more expensive, they moved to green Point. 695 00:37:42,840 --> 00:37:44,279 Speaker 1: A lot of the hipsters and the hips and there, 696 00:37:44,400 --> 00:37:46,279 Speaker 1: and the Polish who lived in green Point, a lot 697 00:37:46,320 --> 00:37:49,520 Speaker 1: of them have migrated to parts of Queens like Maspith Queens. 698 00:37:49,520 --> 00:37:52,000 Speaker 1: They have something called Polock Alley in Massmith Queens. A 699 00:37:52,040 --> 00:37:54,800 Speaker 1: lot of older Polish lived there who have been through communism, 700 00:37:55,080 --> 00:37:59,080 Speaker 1: and Cuomo also won on that part of Maspith as well. 701 00:37:59,360 --> 00:38:02,000 Speaker 1: A lot of in the Venezuelan community and the Polis community, 702 00:38:02,280 --> 00:38:03,759 Speaker 1: and in the Russi community. There are a lot of 703 00:38:04,000 --> 00:38:07,239 Speaker 1: non registered non citizens who live in the city, so 704 00:38:07,640 --> 00:38:09,960 Speaker 1: who knows how many and also in the Chinese community 705 00:38:10,000 --> 00:38:12,480 Speaker 1: as well, so who knows how many of them are 706 00:38:12,560 --> 00:38:14,840 Speaker 1: even able to capable of vote and if they represent 707 00:38:15,120 --> 00:38:18,239 Speaker 1: blocks within those areas. Venezuelans definitely don't. But if you 708 00:38:18,360 --> 00:38:22,839 Speaker 1: look at certainly how Russians voted in South Brooklyn overwhelmingly 709 00:38:22,920 --> 00:38:26,680 Speaker 1: against Mundani East Asians in South Brooklyn and in parts 710 00:38:26,719 --> 00:38:30,399 Speaker 1: of Flushing, Queens overwhelmingly for Cuomo and if you look 711 00:38:30,440 --> 00:38:33,279 Speaker 1: at the Polish populations in place like Masbith where they 712 00:38:33,320 --> 00:38:36,160 Speaker 1: still have a very large block in certain pockets of 713 00:38:36,200 --> 00:38:39,839 Speaker 1: the neighborhood, they voted for Cuomo as well. So that's 714 00:38:39,880 --> 00:38:41,080 Speaker 1: the best I could sit they're and gift to you. 715 00:38:41,160 --> 00:38:43,359 Speaker 1: It does seem like they voted. They tend to vote 716 00:38:43,520 --> 00:38:47,120 Speaker 1: less in favor of socialism than other people did, people 717 00:38:47,160 --> 00:38:50,000 Speaker 1: from former communist nations, especially the Russians. I mean the Russians. 718 00:38:50,040 --> 00:38:51,799 Speaker 1: It wasn't even I mean it was like a ninety 719 00:38:51,880 --> 00:38:53,560 Speaker 1: to ten type of voting block. 720 00:38:53,640 --> 00:38:54,040 Speaker 2: For sure. 721 00:38:54,680 --> 00:38:56,200 Speaker 1: That's the best I could do for you. It's very 722 00:38:56,480 --> 00:38:58,799 Speaker 1: I would love if they actually broke the data down 723 00:38:58,840 --> 00:39:01,000 Speaker 1: precinct by preescinc But the New York that I grew 724 00:39:01,080 --> 00:39:04,719 Speaker 1: up in was very segregated, for lack of better terms, 725 00:39:04,760 --> 00:39:06,759 Speaker 1: like the Irish did live predominant one area, and the 726 00:39:07,080 --> 00:39:10,040 Speaker 1: Chinese and another, and the Polish and another. And as 727 00:39:10,480 --> 00:39:12,839 Speaker 1: you know, in the last twenty years, as New York 728 00:39:12,920 --> 00:39:15,520 Speaker 1: City has changed, there's a lot more integration, which is 729 00:39:15,560 --> 00:39:17,399 Speaker 1: both a good thing and bad thing in the terms 730 00:39:17,440 --> 00:39:20,840 Speaker 1: of trying to identify how certain groups vote. State Island's 731 00:39:20,840 --> 00:39:24,520 Speaker 1: probably the last holdouts. Southern Russians in southern Brooklyn. Probably 732 00:39:24,520 --> 00:39:27,320 Speaker 1: another hold out Hasidic Jews. There's a few. There's Irish 733 00:39:27,400 --> 00:39:30,640 Speaker 1: in the Bronx and in Rockaway, Queens. There's a handful, 734 00:39:30,680 --> 00:39:32,960 Speaker 1: and there's a few podcasts of places, but there's not 735 00:39:33,160 --> 00:39:35,239 Speaker 1: as many as there once was when I was growing 736 00:39:35,320 --> 00:39:37,759 Speaker 1: up it was overwhelmingly So that's the best I could 737 00:39:37,760 --> 00:39:39,200 Speaker 1: do for you. Thank you for this question. Thank you 738 00:39:39,280 --> 00:39:41,080 Speaker 1: for listening to this podcast. I really appreciate it, and 739 00:39:41,080 --> 00:39:42,920 Speaker 1: I appreciate all of you for listening to this podcast. 740 00:39:43,200 --> 00:39:45,840 Speaker 1: If you like this podcast, please press like and subscribe 741 00:39:45,880 --> 00:39:49,360 Speaker 1: on the iHeartRadio app, Apple Podcasts, Spotify, YouTube, wherever you 742 00:39:49,400 --> 00:39:51,480 Speaker 1: get this podcast. And I will see you guys that 743 00:39:51,560 --> 00:39:53,520 Speaker 1: next week. I just got some big guests. It's going 744 00:39:53,560 --> 00:39:56,279 Speaker 1: to be a great show, so very very excited about that. 745 00:39:56,400 --> 00:39:56,959 Speaker 1: Thank you, guys.