1 00:00:00,160 --> 00:00:02,000 Speaker 1: Hey, Ken, did you know that gold is the only 2 00:00:02,000 --> 00:00:05,080 Speaker 1: currency that's held its value since the dawn of money? 3 00:00:05,240 --> 00:00:08,840 Speaker 1: Well I did, thanks to our friends at Legacy Precious Medals, 4 00:00:08,880 --> 00:00:12,719 Speaker 1: the most trusted name in gold investing. Investing in gold 5 00:00:12,840 --> 00:00:16,239 Speaker 1: protects you against inflation and gives you a hedge against 6 00:00:16,280 --> 00:00:19,960 Speaker 1: stock market volatility. Don't leave your retirement to chance. Call 7 00:00:20,120 --> 00:00:23,760 Speaker 1: Legacy Precious Medals today at eight six six six nine 8 00:00:23,880 --> 00:00:27,080 Speaker 1: one two one seven three or download your free Investor's 9 00:00:27,080 --> 00:00:30,640 Speaker 1: guide now at by Legacy goold dot com. That's by 10 00:00:30,960 --> 00:00:34,360 Speaker 1: Legacy goold dot com. Alright, at about fifteen minutes, here 11 00:00:34,400 --> 00:00:37,280 Speaker 1: comes another word your way. Stand by for the keyword, 12 00:00:37,320 --> 00:00:40,160 Speaker 1: which you can enter at the website KFI am sixty 13 00:00:40,240 --> 00:00:42,000 Speaker 1: dot com. Can't mess it. As soon as you go there, 14 00:00:42,400 --> 00:00:45,080 Speaker 1: a big banner drops down with Handel's head. That's where 15 00:00:45,080 --> 00:00:48,080 Speaker 1: you enter the word to possibly win a thousand dollars. 16 00:00:49,560 --> 00:00:52,680 Speaker 1: You know, in the last few days we've heard incessantly 17 00:00:52,720 --> 00:00:56,840 Speaker 1: from doctor Barbara Ferre that masks have to return by 18 00:00:56,880 --> 00:01:00,600 Speaker 1: next week. And then we found out and we played 19 00:01:00,640 --> 00:01:03,440 Speaker 1: the audio this. Steve Gregory had it that there are 20 00:01:03,440 --> 00:01:06,640 Speaker 1: two prominent doctors at the USC Medical Center which said 21 00:01:06,640 --> 00:01:10,680 Speaker 1: they're not seeing any evidence of this big hospital surge. 22 00:01:11,280 --> 00:01:16,040 Speaker 1: And one of them is an epidemiologist. He said repeatedly. Nobody, nobody, 23 00:01:16,200 --> 00:01:21,240 Speaker 1: nobody in the ICU, nobody intimated, nobody with any serious 24 00:01:21,680 --> 00:01:26,080 Speaker 1: lung issues. That the vast majority of people who have 25 00:01:26,240 --> 00:01:29,000 Speaker 1: COVID in the hospital came to the hospital with something else. 26 00:01:29,080 --> 00:01:30,800 Speaker 1: And they do a blood test, it's like, oh, you 27 00:01:30,840 --> 00:01:34,240 Speaker 1: got a little COVID too. But there's relatively few people 28 00:01:34,319 --> 00:01:37,920 Speaker 1: going to the hospital because of COVID. That has not 29 00:01:39,120 --> 00:01:43,280 Speaker 1: caused doctor Frere to budge. I love when people who 30 00:01:43,280 --> 00:01:47,320 Speaker 1: are wrong never budge. This is fascinating. But we'll see, 31 00:01:47,360 --> 00:01:50,160 Speaker 1: we'll see where We're calling in another expert here, Yes, 32 00:01:50,520 --> 00:01:53,440 Speaker 1: frequent guest here on the Johnny Ken Show, doctor Jeffrey Klausner. 33 00:01:54,200 --> 00:01:57,160 Speaker 1: That's right, Tex School of Medicine. He's an infectious disease 34 00:01:57,200 --> 00:02:00,840 Speaker 1: specialist at USC and he is weight in on this. 35 00:02:00,960 --> 00:02:04,200 Speaker 1: And the headline in this story is bad news. COVID 36 00:02:04,320 --> 00:02:07,160 Speaker 1: nineteen numbers are pretty meaningless. So let's talk about all that, 37 00:02:08,080 --> 00:02:12,320 Speaker 1: doctor Klausner, How are you all right? Good, good, good afternoon, gentlemen. 38 00:02:12,360 --> 00:02:16,200 Speaker 1: All right, so we got Barbara Ferreir who's claiming it's 39 00:02:16,240 --> 00:02:18,600 Speaker 1: so bad, we got to go back to indoor masking. 40 00:02:18,600 --> 00:02:20,839 Speaker 1: And then you had these two doctors and I'm sure 41 00:02:20,840 --> 00:02:23,639 Speaker 1: you know these two at the USC Medical Center saying 42 00:02:24,040 --> 00:02:27,000 Speaker 1: there's nothing, there's no big surge going on that's affecting 43 00:02:27,000 --> 00:02:30,080 Speaker 1: the hospital in the ICU unit. So what's what's the 44 00:02:30,120 --> 00:02:34,640 Speaker 1: story here. Well, the story is that the CDC has 45 00:02:34,680 --> 00:02:38,440 Speaker 1: created tears of risk and those tears are defined both 46 00:02:38,520 --> 00:02:42,200 Speaker 1: by a combination of a number of newly reported cases 47 00:02:42,400 --> 00:02:46,440 Speaker 1: which are positive test results, which I'm not sure you're 48 00:02:46,560 --> 00:02:49,400 Speaker 1: surprised that they're that that they've been high, right, We've 49 00:02:49,400 --> 00:02:51,200 Speaker 1: known a lot of people of tested pausities in the 50 00:02:51,240 --> 00:02:53,680 Speaker 1: past week or two. And then the second part of 51 00:02:53,680 --> 00:02:57,640 Speaker 1: that metric is if the hospitals of the hospital rates 52 00:02:57,639 --> 00:03:00,600 Speaker 1: are above ten per one hundred thousand, and right now, 53 00:03:00,600 --> 00:03:04,600 Speaker 1: according to doctor Frea and that LA County data, we're 54 00:03:04,639 --> 00:03:07,400 Speaker 1: at about eleven per hundred thousand, and if we continue 55 00:03:07,560 --> 00:03:10,880 Speaker 1: at that rate for more than fourteen days, then trigger 56 00:03:10,960 --> 00:03:14,280 Speaker 1: automatically the mass of man dates going now is that 57 00:03:14,360 --> 00:03:18,760 Speaker 1: eleven per hundred thousand all patients with COVID or patients 58 00:03:18,800 --> 00:03:23,639 Speaker 1: who were hospitalized specifically because of COVID. Yeah, well, that's 59 00:03:23,680 --> 00:03:25,440 Speaker 1: part of problem. And that's part of that problem in 60 00:03:25,520 --> 00:03:28,520 Speaker 1: my essay when I say that the you know, COVID 61 00:03:28,600 --> 00:03:33,480 Speaker 1: numbers are pretty meaningless because you know, a year ago 62 00:03:33,560 --> 00:03:36,160 Speaker 1: or two years ago, I mean, eighty ninety percent of 63 00:03:36,200 --> 00:03:40,120 Speaker 1: every COVID hospitalization was actually due to COVID. Now in 64 00:03:40,160 --> 00:03:43,360 Speaker 1: most places around the country, seventy eighty percent or not 65 00:03:43,440 --> 00:03:46,320 Speaker 1: due to COVID, And doctor Spelberg and his colleagues said 66 00:03:46,320 --> 00:03:50,760 Speaker 1: about ten percent maybe at the county hospital, truly due 67 00:03:50,800 --> 00:03:54,040 Speaker 1: to COVID, and only a few places Marine County, State 68 00:03:54,080 --> 00:03:58,720 Speaker 1: of Massachusetts actually teas apart the difference between people hospitalized 69 00:03:58,760 --> 00:04:02,600 Speaker 1: for COVID and hospitalized with COVID. Now, you and some 70 00:04:02,640 --> 00:04:06,200 Speaker 1: of your colleagues did a deep dive, you said, into 71 00:04:06,240 --> 00:04:11,320 Speaker 1: COVID hospitalizations at La County, at an La County Public hospital, 72 00:04:12,480 --> 00:04:15,960 Speaker 1: and you found that over two thirds were admitted for 73 00:04:16,000 --> 00:04:20,520 Speaker 1: reasons other than COVID. Yeah. So we reviewed about four 74 00:04:20,640 --> 00:04:25,520 Speaker 1: hundred and sixty consecutive hospitalizations in December and January last year. 75 00:04:25,600 --> 00:04:28,920 Speaker 1: We painstakingly looked at every chart, reviewed the medical records 76 00:04:29,120 --> 00:04:33,159 Speaker 1: soft people needed oxygen or treatment for COVID pneumonia, and 77 00:04:33,240 --> 00:04:36,520 Speaker 1: we found only thirty eight percent at that time, rashly 78 00:04:36,560 --> 00:04:40,159 Speaker 1: there for COVID. Most people, the majority were there for 79 00:04:40,480 --> 00:04:44,599 Speaker 1: other causes such as trauma such as you know, Buken 80 00:04:44,680 --> 00:04:47,400 Speaker 1: bones or something else. Part of the challenge is that 81 00:04:48,200 --> 00:04:51,799 Speaker 1: the state of California and the FEDS recommend that everyone 82 00:04:51,839 --> 00:04:55,360 Speaker 1: who's admitted to a hospital is tested for COVID. So 83 00:04:55,400 --> 00:04:59,599 Speaker 1: it's as if you know, everyone who had, you know, 84 00:04:59,640 --> 00:05:03,600 Speaker 1: blew hair all of a sudden is identified and they're 85 00:05:03,640 --> 00:05:07,719 Speaker 1: recognized as a potential as a potential COVID hostilization. So 86 00:05:07,760 --> 00:05:10,920 Speaker 1: there's a big overcount, and you know, the county really 87 00:05:10,920 --> 00:05:14,640 Speaker 1: should not be using these overcount numbers to implement policy. 88 00:05:14,680 --> 00:05:17,640 Speaker 1: They need to look at true COVID hospitalizations. Now they're 89 00:05:17,640 --> 00:05:20,440 Speaker 1: also overcounting deaths that are connected to COVID. Can you 90 00:05:20,440 --> 00:05:26,960 Speaker 1: explain that? So in April twenty twenty, the Department Health 91 00:05:26,960 --> 00:05:32,599 Speaker 1: Inting Services came out with guidelines that said that every 92 00:05:33,560 --> 00:05:36,440 Speaker 1: death certificate should put a COVID positive test in what 93 00:05:36,480 --> 00:05:39,279 Speaker 1: they call part one of the death certificate, which is 94 00:05:39,320 --> 00:05:42,440 Speaker 1: the upper part of the death certificate, what's our events 95 00:05:43,080 --> 00:05:47,400 Speaker 1: leading to death. So by definition, they were saying that 96 00:05:47,480 --> 00:05:51,880 Speaker 1: anyone at tests positive for COVID, that COVID was related 97 00:05:51,960 --> 00:05:55,040 Speaker 1: to their cause of death, whether it was truly related 98 00:05:55,160 --> 00:05:57,640 Speaker 1: or not. And that was a way to systematically make 99 00:05:57,680 --> 00:06:01,479 Speaker 1: sure that there was no undercounting of COVID mortality, which 100 00:06:01,520 --> 00:06:03,440 Speaker 1: was a big problem early on. Right when we didn't 101 00:06:03,440 --> 00:06:05,880 Speaker 1: have tests, people were dying at home without being tested. 102 00:06:06,160 --> 00:06:09,000 Speaker 1: We didn't really know the burden. But now you two 103 00:06:09,160 --> 00:06:11,920 Speaker 1: plus years later, we can do a much better job 104 00:06:11,960 --> 00:06:15,599 Speaker 1: of knowing who's dying from COVID versus something else. So 105 00:06:15,920 --> 00:06:18,599 Speaker 1: these daily reports we get from the county Health department 106 00:06:18,640 --> 00:06:21,720 Speaker 1: which lists the number of deaths, is that reliable? Have 107 00:06:21,800 --> 00:06:26,360 Speaker 1: they changed the methodology? Well, it's interesting. So on their 108 00:06:26,360 --> 00:06:29,960 Speaker 1: website every month they actually report in the prior month 109 00:06:30,080 --> 00:06:33,520 Speaker 1: how many COVID hospitalizations will probably due to COVID and 110 00:06:33,600 --> 00:06:36,600 Speaker 1: how many were probably not due to COVID but still 111 00:06:36,640 --> 00:06:40,120 Speaker 1: counted as COVID hospitalizations. And if you look at that, 112 00:06:40,240 --> 00:06:43,440 Speaker 1: in August twenty twenty one, not surprisingly, about eighty percent 113 00:06:43,520 --> 00:06:47,040 Speaker 1: of COVID hospitalizations were really due to COVID, and now 114 00:06:47,080 --> 00:06:50,080 Speaker 1: it's about twenty to thirty percent. So on the county's 115 00:06:50,080 --> 00:06:54,880 Speaker 1: own website it characterizes it. But for reasons Honor will understand, 116 00:06:55,080 --> 00:06:57,760 Speaker 1: those are not the data that are routinely shared with 117 00:06:57,839 --> 00:07:03,440 Speaker 1: the public. So what's what's behind this story? Because listening 118 00:07:03,480 --> 00:07:05,520 Speaker 1: to you is going to make people very cynical and 119 00:07:05,560 --> 00:07:08,600 Speaker 1: suspicious of all these official numbers, and people are going 120 00:07:08,600 --> 00:07:11,880 Speaker 1: to start to wonder, Okay, what what's the real reason 121 00:07:11,960 --> 00:07:15,840 Speaker 1: for a mask mandate here? Because you are saying pretty 122 00:07:15,920 --> 00:07:18,960 Speaker 1: much what the other two doctors that you USC are saying. 123 00:07:19,440 --> 00:07:22,760 Speaker 1: And then Barbara Ferrere is over here, and so who 124 00:07:22,800 --> 00:07:26,600 Speaker 1: are we supposed to trust and believe? Right? I mean, 125 00:07:26,600 --> 00:07:29,880 Speaker 1: obviously I'm not inside her head, so we can think 126 00:07:29,920 --> 00:07:33,200 Speaker 1: that you know, perhaps you know, the idea of mass 127 00:07:33,800 --> 00:07:35,880 Speaker 1: and as you know, there's a big debate about whether 128 00:07:35,960 --> 00:07:39,840 Speaker 1: mass are effective at a population level for preventing the spread. 129 00:07:40,120 --> 00:07:42,640 Speaker 1: We do know they do work to prevent you know, 130 00:07:42,840 --> 00:07:47,040 Speaker 1: acquisition of infection individuals, particularly those who are at risk 131 00:07:47,160 --> 00:07:51,520 Speaker 1: or elderly or unvaccinated. But as a public health strategy 132 00:07:51,600 --> 00:07:55,720 Speaker 1: to control the spread of infection, the you know, MASS 133 00:07:55,800 --> 00:07:59,240 Speaker 1: date is actually you know, quite uncertain. But maybe you know, 134 00:07:59,280 --> 00:08:01,840 Speaker 1: people are thinking, okay, well, if we can prevent new 135 00:08:01,880 --> 00:08:05,400 Speaker 1: positive tests, we can keep you know, people working, we 136 00:08:05,440 --> 00:08:08,200 Speaker 1: can keep people going to school, we won't have to 137 00:08:08,240 --> 00:08:13,600 Speaker 1: have these other disruptions that are positive test results in right, 138 00:08:13,640 --> 00:08:15,400 Speaker 1: But when you get into a mandate and you force 139 00:08:15,480 --> 00:08:17,600 Speaker 1: people against their will, you get you're going to run 140 00:08:17,640 --> 00:08:20,480 Speaker 1: into a lot of resistance, which I think is just 141 00:08:20,600 --> 00:08:23,960 Speaker 1: counterproductive if you want people to listen to in the 142 00:08:24,000 --> 00:08:27,800 Speaker 1: future when there's a real series problem. Well, I mean 143 00:08:27,840 --> 00:08:29,960 Speaker 1: I agree. I think you know, you know, public health 144 00:08:30,080 --> 00:08:37,120 Speaker 1: and is built on trustworthiness and transparency. Public health leaders 145 00:08:37,240 --> 00:08:42,080 Speaker 1: report to elected officials. The you know, elected officials need 146 00:08:42,160 --> 00:08:47,400 Speaker 1: to hold public health accountable if the populace is uncertain 147 00:08:47,640 --> 00:08:53,000 Speaker 1: or there's you know, questions about about policy. All right, 148 00:08:53,040 --> 00:08:55,120 Speaker 1: doctor Klauster, thank you for coming on with us again. 149 00:08:56,080 --> 00:08:59,160 Speaker 1: All right, take care, Doctor Jeffrey Kloster, And of course 150 00:08:59,160 --> 00:09:02,240 Speaker 1: he's a clinicalfessor. He works at the Tech School of 151 00:09:02,240 --> 00:09:06,200 Speaker 1: Medicine at USC And as we heard from the La 152 00:09:06,320 --> 00:09:10,319 Speaker 1: County Medical officer the other day that this hype about this, 153 00:09:10,440 --> 00:09:14,600 Speaker 1: I mean, as I said before, this you know metric 154 00:09:14,679 --> 00:09:16,760 Speaker 1: that they're using to consider us sort of in this 155 00:09:16,880 --> 00:09:20,480 Speaker 1: high community transmission rate is ridiculously low. It's like ten 156 00:09:20,520 --> 00:09:25,120 Speaker 1: hospitalizations on hundred thousand people. So we're dealing now with 157 00:09:26,160 --> 00:09:30,040 Speaker 1: a bureaucrat. Doctor Ferrere, and she again she's not a 158 00:09:30,040 --> 00:09:32,600 Speaker 1: medical doctor. She got a doctor. You're not calling her 159 00:09:32,679 --> 00:09:35,240 Speaker 1: doctor anywhere. Don't do that. That's right, all right. Barbara 160 00:09:35,240 --> 00:09:40,640 Speaker 1: Ferrere has got a studied public health and we got 161 00:09:40,640 --> 00:09:43,800 Speaker 1: a problem here because she's making claims. And now you 162 00:09:43,840 --> 00:09:45,920 Speaker 1: know we last week we brought you the two other 163 00:09:46,040 --> 00:09:49,360 Speaker 1: usc doctors and you heard doctor Klausner, and it's it's 164 00:09:49,360 --> 00:09:51,920 Speaker 1: a simple thing to understand here. You look at all 165 00:09:51,960 --> 00:09:56,199 Speaker 1: the people in the hospital who have COVID. Most of them, 166 00:09:56,360 --> 00:10:01,360 Speaker 1: vast majority were brought in with other reasons, like doctor 167 00:10:01,400 --> 00:10:05,320 Speaker 1: Klausner said, trauma, broken bones, to a blood test. Hey, 168 00:10:05,559 --> 00:10:08,400 Speaker 1: look you're tested positive. Now most of them may be 169 00:10:08,520 --> 00:10:12,160 Speaker 1: asymptomatic for all we know. And then you have the 170 00:10:12,240 --> 00:10:14,720 Speaker 1: same thing as doctor Klausner went through the statistics on 171 00:10:14,760 --> 00:10:19,000 Speaker 1: the deaths. People are dying. They may die and happen 172 00:10:19,080 --> 00:10:21,760 Speaker 1: to have the COVID virus in them, but the COVID 173 00:10:21,840 --> 00:10:24,439 Speaker 1: virus didn't kill them. The death was due to something else. 174 00:10:25,960 --> 00:10:28,920 Speaker 1: So we have we have Barbara Ferair using these numbers 175 00:10:29,800 --> 00:10:34,880 Speaker 1: to inflame people and to issue these restrictive mandates by force. 176 00:10:36,360 --> 00:10:39,560 Speaker 1: And clearly, if you objectively look at the numbers, that's 177 00:10:41,200 --> 00:10:46,319 Speaker 1: not necessary. This is like an abusive power. Right, So 178 00:10:46,480 --> 00:10:48,400 Speaker 1: what are we supposed to do? And this is what 179 00:10:48,440 --> 00:10:51,400 Speaker 1: I feared for all this time? What do you do 180 00:10:51,800 --> 00:10:55,679 Speaker 1: when public figures have the power, have emergency powers, but 181 00:10:55,720 --> 00:11:00,359 Speaker 1: there isn't actually an emergency? And who to go to 182 00:11:00,360 --> 00:11:02,760 Speaker 1: to point out the obvious We don't have an emergency. 183 00:11:03,160 --> 00:11:05,559 Speaker 1: We don't, But we have somebody who says there is 184 00:11:05,600 --> 00:11:09,440 Speaker 1: an emergency and they're wrong, and the numbers objectively prove 185 00:11:09,520 --> 00:11:11,960 Speaker 1: they're wrong, the science proves they're wrong. Now what do 186 00:11:12,000 --> 00:11:14,200 Speaker 1: we do? You know, they're supposed to be checks and 187 00:11:14,240 --> 00:11:16,840 Speaker 1: balances here. They're supposed to be a way for the 188 00:11:16,920 --> 00:11:26,440 Speaker 1: people to not be hit with onerous restrictions because somebody 189 00:11:26,720 --> 00:11:28,840 Speaker 1: is in error or is not a power trip as 190 00:11:28,880 --> 00:11:32,280 Speaker 1: you're obsessive. I mean, these are all human beings too, 191 00:11:32,360 --> 00:11:36,760 Speaker 1: They're they're not superheroes. She's got her issues and these 192 00:11:36,760 --> 00:11:39,760 Speaker 1: issues now she's acting out with them. She can't let 193 00:11:39,800 --> 00:11:43,120 Speaker 1: go of the power, and so she's looking at the 194 00:11:43,160 --> 00:11:45,560 Speaker 1: same numbers. She's looking at the same numbers that everybody 195 00:11:45,600 --> 00:11:48,080 Speaker 1: else is looking at. But she's got the interpretation that 196 00:11:48,120 --> 00:11:50,440 Speaker 1: we have an emergency. Everything has to be masked. Now, 197 00:11:51,760 --> 00:11:55,280 Speaker 1: all right, Well, there's more on Freer when we come back, 198 00:11:55,800 --> 00:11:59,319 Speaker 1: including also, the first thing we'll do is give you 199 00:11:59,360 --> 00:12:01,600 Speaker 1: the keyword for chance. It's some money John and Ken 200 00:12:01,640 --> 00:12:05,000 Speaker 1: show KFI well with Barbara Ferrerat's All in the Family, 201 00:12:05,040 --> 00:12:07,080 Speaker 1: will tell you a story coming up right after this. 202 00:12:08,120 --> 00:12:11,079 Speaker 1: Now your chance to win one thousand dollars. Just enter 203 00:12:11,120 --> 00:12:14,719 Speaker 1: this nationwide keyword on our website green. That's green g 204 00:12:15,040 --> 00:12:17,880 Speaker 1: R E E N. Entered now at KFI amix forty 205 00:12:17,960 --> 00:12:21,920 Speaker 1: dot com. Slash cash powered by Sweet James Accident Attorneys. 206 00:12:21,920 --> 00:12:24,160 Speaker 1: If you're hurting an accident, winning is everything. 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All right, well, we have a 216 00:12:53,440 --> 00:12:55,680 Speaker 1: story that came out this afternoon and it's at Red 217 00:12:55,760 --> 00:12:59,000 Speaker 1: State dot com, which is a conservative leaning websites written 218 00:12:59,040 --> 00:13:04,199 Speaker 1: by Jennifer Van Laar that apparently Barbara Ferrer's daughter was 219 00:13:04,280 --> 00:13:08,480 Speaker 1: the part of a study that the CDC relied upon 220 00:13:09,200 --> 00:13:14,760 Speaker 1: to push school mask mandates, and apparently that wasn't really 221 00:13:14,840 --> 00:13:18,839 Speaker 1: disclosed and appears to be a conflict of interest. The 222 00:13:19,000 --> 00:13:24,120 Speaker 1: report says that the study found the COVID mitigation efforts 223 00:13:24,160 --> 00:13:30,040 Speaker 1: in schools, including masking, were highly effective in stopping disease spread. 224 00:13:30,800 --> 00:13:33,960 Speaker 1: The CDC did cite it and several of its statements 225 00:13:34,040 --> 00:13:38,720 Speaker 1: on the importance of masking in schools. It was authored 226 00:13:38,800 --> 00:13:43,160 Speaker 1: by the La County Office of Education, and one of 227 00:13:43,200 --> 00:13:47,040 Speaker 1: the people in the Office of Education apparently is Barbara 228 00:13:47,080 --> 00:13:52,599 Speaker 1: Farere's daughter. Her name is Caitlin Barnes. Her title is 229 00:13:52,840 --> 00:13:58,880 Speaker 1: Project manager at the La County Office of Education. It 230 00:13:59,040 --> 00:14:02,440 Speaker 1: said although any authors are required to disclose any conflicts 231 00:14:02,440 --> 00:14:07,280 Speaker 1: of interest, and Barbara Ferrer was in the acknowledgement section, 232 00:14:07,840 --> 00:14:10,800 Speaker 1: there was no mention that one of the people involved 233 00:14:10,840 --> 00:14:14,360 Speaker 1: in the study is the daughter of the La County 234 00:14:15,160 --> 00:14:21,440 Speaker 1: Medical Office head who and pushes masking in schools or 235 00:14:21,600 --> 00:14:26,200 Speaker 1: personal agenda. Also, she's not a medical doctor. In fact, 236 00:14:26,320 --> 00:14:31,160 Speaker 1: she has no scientific background or a PhD in any field. No, 237 00:14:31,400 --> 00:14:34,680 Speaker 1: it says here that her professional background is a combination 238 00:14:35,520 --> 00:14:38,960 Speaker 1: of her father's career in urban planning and community organizing. 239 00:14:40,520 --> 00:14:42,840 Speaker 1: You know, it never occurred to me that Barbara Ferrera 240 00:14:42,880 --> 00:14:46,680 Speaker 1: would had a husband. I don't know why. Maybe not 241 00:14:46,800 --> 00:14:49,840 Speaker 1: anymore because she's got her father's last name and Barbara 242 00:14:49,840 --> 00:14:52,120 Speaker 1: fare does not. Maybe yeah, I don't know if that 243 00:14:52,240 --> 00:14:55,160 Speaker 1: just didn't strike me. But she not only had a 244 00:14:55,240 --> 00:14:59,760 Speaker 1: husband or something and a daughter. So she got a 245 00:15:01,280 --> 00:15:04,120 Speaker 1: with a major in metropolitan studies and a minor in 246 00:15:04,200 --> 00:15:08,680 Speaker 1: politics from NYU. I'll look at this. Her research focused 247 00:15:08,720 --> 00:15:12,800 Speaker 1: on urban planning and policy, immigration, race and racism, and 248 00:15:13,440 --> 00:15:18,880 Speaker 1: health equity. Yeah, that's all the bullcrap studies, all the 249 00:15:18,960 --> 00:15:23,080 Speaker 1: nonsense studies. So this study concluded that students who went 250 00:15:23,120 --> 00:15:26,080 Speaker 1: to school during the winter of twenty twenty one tested 251 00:15:26,120 --> 00:15:28,840 Speaker 1: positive for COVID at a much lower rate than their 252 00:15:28,960 --> 00:15:32,560 Speaker 1: peers who did not attend La County schools. That the 253 00:15:32,680 --> 00:15:35,320 Speaker 1: protocols in place in La County were responsible for this, 254 00:15:36,040 --> 00:15:40,280 Speaker 1: according Jones, kind of bear. According to the daughter of 255 00:15:40,400 --> 00:15:44,000 Speaker 1: the person who put in the protocols. A rash Stories 256 00:15:44,000 --> 00:15:46,800 Speaker 1: published around this time. The study was released follow the 257 00:15:46,840 --> 00:15:49,080 Speaker 1: same pattern. They lead with the story of a teacher 258 00:15:49,240 --> 00:15:52,040 Speaker 1: who allegedly infected twenty six people by reading to her 259 00:15:52,120 --> 00:15:55,840 Speaker 1: students without wearing a mask, taking care to emphasize that 260 00:15:55,920 --> 00:15:58,640 Speaker 1: the teacher was one of only two unvaccinated teachers in 261 00:15:58,720 --> 00:16:03,360 Speaker 1: the school. Then the story shift. Hey, look, this new 262 00:16:03,400 --> 00:16:06,240 Speaker 1: study out of La County shows that mandatory masking works. 263 00:16:09,160 --> 00:16:12,880 Speaker 1: And apparently the CDC, the CDC director this for Shell Willinski, 264 00:16:13,520 --> 00:16:17,600 Speaker 1: had mentioned the study in a press briefing. Now, as 265 00:16:17,680 --> 00:16:21,200 Speaker 1: we have found out that the spread in schools around 266 00:16:21,240 --> 00:16:26,000 Speaker 1: the nation was extremely tiny, and here in La County, 267 00:16:26,280 --> 00:16:30,280 Speaker 1: in La City specifically, you not only had well you 268 00:16:30,360 --> 00:16:33,400 Speaker 1: didn't have kids wearing masks in schools. There were no schools. 269 00:16:33,640 --> 00:16:37,360 Speaker 1: Everything was shut down. Kids were told to sit at 270 00:16:37,400 --> 00:16:39,240 Speaker 1: home and stare at the screen and that was a 271 00:16:39,320 --> 00:16:44,240 Speaker 1: complete failure. Wow, you know what we told you. The 272 00:16:44,320 --> 00:16:48,120 Speaker 1: salary of Barbara Ferrare the other day, including benefits six 273 00:16:48,240 --> 00:16:51,800 Speaker 1: hundred and thirty seven thousand. Caitlyn Barnes or daughter is 274 00:16:51,800 --> 00:16:55,440 Speaker 1: making one hundred and forty seven thousand in twenty twenty one. 275 00:16:56,040 --> 00:17:00,160 Speaker 1: So between the two of them, we are paying barbar 276 00:17:00,200 --> 00:17:07,080 Speaker 1: Frere and her daughter seven hundred eighty thousand dollars seven 277 00:17:07,240 --> 00:17:13,520 Speaker 1: hundred and eighty four thousand dollars good Bard. The Department 278 00:17:13,560 --> 00:17:16,240 Speaker 1: of Education is headed by a woman who's very fond 279 00:17:16,400 --> 00:17:19,959 Speaker 1: of Farrere. She actually tweeted out, I'm in all these 280 00:17:20,040 --> 00:17:22,760 Speaker 1: two amazing women who I could only hope to emulate 281 00:17:22,800 --> 00:17:26,000 Speaker 1: through my own leadership. And then the picture there is 282 00:17:26,920 --> 00:17:30,320 Speaker 1: you know something, and there's a conclusion in the story 283 00:17:32,280 --> 00:17:34,680 Speaker 1: that says, this woman is not a scientist or a 284 00:17:34,720 --> 00:17:39,439 Speaker 1: public health professional. She's an activist pursuing her agenda by 285 00:17:39,480 --> 00:17:43,280 Speaker 1: any means necessary. And that's that's Barbara Ferre and that's 286 00:17:43,320 --> 00:17:46,640 Speaker 1: the daughter. That's what we're being held hostage to. Two 287 00:17:47,119 --> 00:17:51,000 Speaker 1: social justice war are activists with a woke agenda, and 288 00:17:51,119 --> 00:17:55,200 Speaker 1: they are now twisting the numbers and insisting on mask 289 00:17:55,280 --> 00:18:00,320 Speaker 1: protocols simply because she finds inequity in the numbers, for example, 290 00:18:00,400 --> 00:18:04,520 Speaker 1: more a disproportionate number of Hispanics getting the yeah, more 291 00:18:04,600 --> 00:18:08,520 Speaker 1: cases in the what they call the lower income underserved communities, right, 292 00:18:09,840 --> 00:18:13,639 Speaker 1: and as she agenda to fix that by throwing restrictions 293 00:18:13,680 --> 00:18:17,040 Speaker 1: on everyone, but really necessarily works. So you you you 294 00:18:17,240 --> 00:18:19,680 Speaker 1: put you use your your woke language and your woke 295 00:18:19,720 --> 00:18:25,480 Speaker 1: agenda to intimidate people into wearing a face diaper every 296 00:18:25,520 --> 00:18:29,119 Speaker 1: time they walk indoors. And there's no scientific need for this. 297 00:18:29,880 --> 00:18:33,240 Speaker 1: And some of the science came from Ferrer's daughter, who's 298 00:18:33,240 --> 00:18:37,920 Speaker 1: got no scientific background. The one thing we've learned about 299 00:18:38,040 --> 00:18:41,680 Speaker 1: COVID is that it comes and goes and bursts, it 300 00:18:41,920 --> 00:18:44,560 Speaker 1: moves in. There's a number of cases, then it fades 301 00:18:44,600 --> 00:18:47,280 Speaker 1: away after a couple of months. It's happened time and 302 00:18:47,440 --> 00:18:49,600 Speaker 1: time again. But what we're looking at now is not 303 00:18:49,760 --> 00:18:52,560 Speaker 1: any real sicknesses from it. So this is why we 304 00:18:52,640 --> 00:18:57,200 Speaker 1: need to move. It's now watered down. It's more of 305 00:18:57,240 --> 00:19:00,640 Speaker 1: a nuisance than anything else. I mean, I know plenty 306 00:19:00,640 --> 00:19:02,640 Speaker 1: of people have gotten COVID just in the last couple 307 00:19:02,640 --> 00:19:05,639 Speaker 1: of weeks. Answer that's pain in the neck, nuisance for 308 00:19:05,680 --> 00:19:09,040 Speaker 1: a few days. But nobody's being taken out in a stretcher. 309 00:19:09,240 --> 00:19:12,719 Speaker 1: Not even close. All Right, we got more coming up. 310 00:19:14,280 --> 00:19:17,560 Speaker 1: How does this end? And who's going to make it end? 311 00:19:18,320 --> 00:19:21,240 Speaker 1: John and Ken show KFI. All right, day after tomorrow, 312 00:19:21,400 --> 00:19:23,760 Speaker 1: we're back with the moistline. People. A quick reminder to 313 00:19:23,840 --> 00:19:27,400 Speaker 1: leave your messages either through the iHeart radio app. What's 314 00:19:27,440 --> 00:19:30,000 Speaker 1: the little microphone icon you used to talk back? To 315 00:19:30,080 --> 00:19:32,560 Speaker 1: the show or by calling the tell free number one 316 00:19:32,600 --> 00:19:35,960 Speaker 1: eight seven seven moist eighty six one eight seven seven 317 00:19:36,480 --> 00:19:39,960 Speaker 1: six six four seven eighty eight six. Well, we don't 318 00:19:40,040 --> 00:19:43,280 Speaker 1: normally talk about things that deal with sports on the 319 00:19:43,359 --> 00:19:45,560 Speaker 1: John and Ken Show, but this one has quite an 320 00:19:45,600 --> 00:19:49,359 Speaker 1: angle to it, and we cannot resist. This is the 321 00:19:49,440 --> 00:19:52,639 Speaker 1: fact that recently UCLA and USC announced they're leaving the 322 00:19:52,680 --> 00:19:57,800 Speaker 1: PAC twelve and joining the Big Ten Conference with their sports. Today, 323 00:19:57,960 --> 00:20:02,040 Speaker 1: Gavin Newsom showed up at a UC Board of Regents 324 00:20:02,119 --> 00:20:06,040 Speaker 1: meeting and he was worked up. The first duty of 325 00:20:06,160 --> 00:20:09,240 Speaker 1: every public university is to its people, especially at students. 326 00:20:09,720 --> 00:20:13,640 Speaker 1: UCLA must clearly explain to the public how this deal 327 00:20:13,760 --> 00:20:16,880 Speaker 1: will improve the experience for all of its student athletes 328 00:20:17,400 --> 00:20:20,439 Speaker 1: and will honor its century old partnership with UC Berkeley. 329 00:20:20,800 --> 00:20:23,200 Speaker 1: Because this move leaves UC Berkeley is the only state 330 00:20:23,280 --> 00:20:25,840 Speaker 1: school left in the PAC twelve, and they will be 331 00:20:26,000 --> 00:20:30,600 Speaker 1: economically devastated. The TV rights will shrink almost nothing. That well, 332 00:20:30,800 --> 00:20:33,640 Speaker 1: UCLA plus other schools are likely to leave too well. 333 00:20:33,760 --> 00:20:37,159 Speaker 1: The TV rights of the reason this is happening because oh, 334 00:20:37,280 --> 00:20:39,960 Speaker 1: this is a big deal. Apparently UCLA had a big deficit, 335 00:20:40,119 --> 00:20:42,800 Speaker 1: so yeah, they're looking for this TV deal money. No, 336 00:20:42,960 --> 00:20:46,240 Speaker 1: the PAC twelve network does not produce much revenue. No, 337 00:20:46,720 --> 00:20:49,160 Speaker 1: so you know the rights deal for the Big Ten. 338 00:20:49,240 --> 00:20:53,560 Speaker 1: For UCLA, you're probably the onion dollars. I just can't 339 00:20:53,680 --> 00:20:56,960 Speaker 1: understand why Gavin Newsom is worried about this when there's 340 00:20:57,040 --> 00:20:58,960 Speaker 1: so much other stuff going on in this state that 341 00:20:59,000 --> 00:21:01,760 Speaker 1: he should be worried about. Who cares if UCLA and 342 00:21:01,880 --> 00:21:04,600 Speaker 1: USC are leaving the PAC twelve to go make more 343 00:21:04,680 --> 00:21:08,120 Speaker 1: money for their schools and their student athletes, which will 344 00:21:08,160 --> 00:21:10,520 Speaker 1: all go back to the school all this revenue. So 345 00:21:11,359 --> 00:21:13,440 Speaker 1: Newsom has so many other problems to worry about, he 346 00:21:13,520 --> 00:21:16,280 Speaker 1: needs to figure that out first. The big networks run 347 00:21:16,359 --> 00:21:21,600 Speaker 1: the big conferences down football, ESPN carries all the SEC 348 00:21:21,840 --> 00:21:25,359 Speaker 1: games in the South right, and it's it's a huge 349 00:21:25,400 --> 00:21:29,600 Speaker 1: windfall money and they get big audiences. And Fox is 350 00:21:29,760 --> 00:21:31,680 Speaker 1: I think going to be running is running the Big 351 00:21:31,720 --> 00:21:35,440 Speaker 1: ten network. They do right, So now you USC and 352 00:21:35,560 --> 00:21:40,240 Speaker 1: UCLA can have a share of that huge windfall. But 353 00:21:40,440 --> 00:21:42,680 Speaker 1: PAC twelve is its own independent network, and from what 354 00:21:42,760 --> 00:21:46,200 Speaker 1: I've read over the years, it was conceived badly, managed badly, 355 00:21:46,520 --> 00:21:48,879 Speaker 1: and nobody's making many any money at it, and the 356 00:21:48,960 --> 00:21:52,840 Speaker 1: audiences are small. Yeah, I have direct TV. They refuse 357 00:21:52,960 --> 00:21:54,679 Speaker 1: to pick it up, so that cost him a lot 358 00:21:54,720 --> 00:21:57,359 Speaker 1: of viewers. Yeah, I get it on a specialty tier, 359 00:21:58,840 --> 00:22:00,639 Speaker 1: and you know it's way up in the three hundred 360 00:22:00,680 --> 00:22:03,480 Speaker 1: and seventies. You don't get it automatically the way you 361 00:22:03,560 --> 00:22:08,800 Speaker 1: get Fox and ESPN channels. But here's the fun part 362 00:22:08,880 --> 00:22:11,080 Speaker 1: of this story, which is why we're talking about it. 363 00:22:11,840 --> 00:22:15,119 Speaker 1: Highlighted in a Wall Street Journal column by a writer 364 00:22:15,720 --> 00:22:20,720 Speaker 1: named James Freeman, UCLA's Big ten story. The school claims 365 00:22:20,760 --> 00:22:24,200 Speaker 1: that athletic contests and states on California's band list will 366 00:22:24,240 --> 00:22:28,920 Speaker 1: be voluntary for coaches, staff and players. We talked about 367 00:22:29,000 --> 00:22:33,000 Speaker 1: this recently. The state of California actually passed a law 368 00:22:33,840 --> 00:22:38,159 Speaker 1: that bans any official state travel to twenty two states 369 00:22:38,320 --> 00:22:42,399 Speaker 1: because of their policies dealing with the LGBTQ community or 370 00:22:43,160 --> 00:22:45,920 Speaker 1: it is an abortion. I forget the full list, but 371 00:22:46,720 --> 00:22:50,720 Speaker 1: a lot of a number of transgender issues. Ohio, Indiana, 372 00:22:50,840 --> 00:22:57,000 Speaker 1: and Iowa are on that list. So UCLA travels there. 373 00:22:57,560 --> 00:22:59,400 Speaker 1: How are they going to do that when they would 374 00:22:59,720 --> 00:23:03,000 Speaker 1: use It's going to be money. The alumni are going 375 00:23:03,040 --> 00:23:06,119 Speaker 1: to have to finance the trip. But you can't force 376 00:23:06,480 --> 00:23:09,240 Speaker 1: the athletes, and you can't force the coaches to go 377 00:23:09,359 --> 00:23:13,359 Speaker 1: on a trip to a banned state. Officially, everyone is 378 00:23:13,400 --> 00:23:19,120 Speaker 1: going to volunteer to go. Yes, the response came from 379 00:23:21,160 --> 00:23:26,160 Speaker 1: here's from Scott Markley, the UCLA Bruins Senior Associate Athletic 380 00:23:26,200 --> 00:23:30,280 Speaker 1: director for Communications. That's a long title. Should use CELA 381 00:23:30,400 --> 00:23:34,000 Speaker 1: computer recruit in a banned state in compliance with the law. 382 00:23:34,119 --> 00:23:35,960 Speaker 1: None of the cost for travel to that state will 383 00:23:35,960 --> 00:23:39,880 Speaker 1: come from state funds. In addition, if a team competes 384 00:23:39,880 --> 00:23:42,480 Speaker 1: in a banned state, student athletes and staff will receive 385 00:23:43,160 --> 00:23:48,200 Speaker 1: education about the relevant California law, like the reasons why 386 00:23:48,240 --> 00:23:51,320 Speaker 1: we're not supposed to go there, and as John mentioned, 387 00:23:51,359 --> 00:23:53,760 Speaker 1: they'll be given the option to opt out of the 388 00:23:53,880 --> 00:23:58,160 Speaker 1: travel with no risk of consequence. Nobody's going to opt out. 389 00:23:58,480 --> 00:24:02,960 Speaker 1: They're gonna get pamphlets about all the sexual logs. This 390 00:24:03,119 --> 00:24:06,440 Speaker 1: is this is what California. This is amazing. Newsom has 391 00:24:06,560 --> 00:24:11,000 Speaker 1: stuck his beacon to other states politics. You imagine if 392 00:24:11,040 --> 00:24:13,960 Speaker 1: he became president, life would be like he's telling these 393 00:24:14,040 --> 00:24:18,320 Speaker 1: states that if you don't go with my value system, 394 00:24:18,840 --> 00:24:22,200 Speaker 1: then we're not going to pay to send our football 395 00:24:22,280 --> 00:24:28,240 Speaker 1: teams to play your teams. Well, please, if you don't 396 00:24:28,320 --> 00:24:31,840 Speaker 1: listen to Newsom and you're living in and you're living 397 00:24:31,840 --> 00:24:35,040 Speaker 1: in Indiana, and Gavin Newsom is telling people what to 398 00:24:35,160 --> 00:24:40,120 Speaker 1: do there or their football team can't play our football team. 399 00:24:40,160 --> 00:24:43,080 Speaker 1: It's like F and U. And of course the teams 400 00:24:43,080 --> 00:24:45,160 Speaker 1: are gonna play, and of course you know, the alumni 401 00:24:45,200 --> 00:24:50,080 Speaker 1: are gonna fund whatever travel fund needs to be put together. 402 00:24:50,520 --> 00:24:52,440 Speaker 1: And who was even gonna go check to see whether 403 00:24:52,520 --> 00:24:56,040 Speaker 1: or not they didn't spend any Yeah, you can stay 404 00:24:56,200 --> 00:24:58,280 Speaker 1: tax money on the trip. You could bury it in 405 00:24:58,320 --> 00:25:01,040 Speaker 1: the miscellaneous cadegy llus. This bills been around a few years. 406 00:25:01,160 --> 00:25:03,040 Speaker 1: I'd have to look it up and I imagine you 407 00:25:03,119 --> 00:25:05,000 Speaker 1: still it's probably traveled to one of these states in 408 00:25:05,080 --> 00:25:08,080 Speaker 1: the last several years. It's on this list. This is so. 409 00:25:08,600 --> 00:25:11,160 Speaker 1: But now that it's the Big Ten conference and three 410 00:25:11,400 --> 00:25:13,280 Speaker 1: states are on the list, what are you gonna do? 411 00:25:13,560 --> 00:25:18,720 Speaker 1: This is nanni woke government. They start infecting everything. A 412 00:25:18,840 --> 00:25:22,000 Speaker 1: college football team or a basketball team can't just have 413 00:25:22,160 --> 00:25:25,560 Speaker 1: its normal schedule. Now they're going to have to do 414 00:25:26,000 --> 00:25:30,080 Speaker 1: through the complexities of raising money and separating the money 415 00:25:30,160 --> 00:25:33,560 Speaker 1: and spending certain funds just on these road games when 416 00:25:33,560 --> 00:25:36,760 Speaker 1: they're traveling, and the players are going to get all 417 00:25:36,840 --> 00:25:41,120 Speaker 1: kinds of pamphlets on all the sexuality laws that these 418 00:25:41,160 --> 00:25:44,000 Speaker 1: twenty states have or don't have, and how this offends 419 00:25:44,040 --> 00:25:48,639 Speaker 1: governed a newsom. It's none of your business because what 420 00:25:48,760 --> 00:25:51,000 Speaker 1: happened was I mean, that was basically a virtue signal. 421 00:25:51,040 --> 00:25:53,680 Speaker 1: They just wanted to show everybody we're taking a stand. God, 422 00:25:54,040 --> 00:25:57,680 Speaker 1: did anybody ask about the sports teams? And Eric's right, 423 00:25:58,440 --> 00:26:00,920 Speaker 1: We can all go through a list of ten real 424 00:26:01,000 --> 00:26:04,600 Speaker 1: emergencies going on in the state that he's utterly clueless 425 00:26:04,640 --> 00:26:09,000 Speaker 1: and incompetent on. Instead, he's flying around the country's selling 426 00:26:09,119 --> 00:26:13,880 Speaker 1: himself as the next big Democratic hope, and he's actually 427 00:26:14,000 --> 00:26:20,760 Speaker 1: prattling about travel restrictions for a football team. Every hour 428 00:26:20,960 --> 00:26:22,960 Speaker 1: is more absurd than the last hour. You think the 429 00:26:23,000 --> 00:26:26,760 Speaker 1: student athletes care about the bands in those states either, No, exactly, 430 00:26:26,960 --> 00:26:29,800 Speaker 1: nobody cares. Nobody cares about this stuff. There's a tiny 431 00:26:29,880 --> 00:26:33,800 Speaker 1: fringe that cares, but they're constantly shouting about it, and 432 00:26:33,880 --> 00:26:36,399 Speaker 1: they've got all the media megaphones, and you can't escape 433 00:26:36,440 --> 00:26:39,240 Speaker 1: it anymore. You can't just say, look, I don't want 434 00:26:39,240 --> 00:26:41,000 Speaker 1: to hear it anymore. I don't care. I just want 435 00:26:41,000 --> 00:26:42,800 Speaker 1: to live life. I don't want to live in a 436 00:26:42,840 --> 00:26:45,400 Speaker 1: world where I constantly are being hit over the head 437 00:26:45,440 --> 00:26:49,960 Speaker 1: with this stuff. By coming up after five o'clock, what 438 00:26:50,119 --> 00:26:54,399 Speaker 1: a story. Apparently the La County District Attorney, George Gasconne 439 00:26:55,080 --> 00:26:59,320 Speaker 1: is pulling in quite a big named lawyer to represent 440 00:26:59,560 --> 00:27:03,480 Speaker 1: himself in front of the California State Supreme Court. This 441 00:27:03,640 --> 00:27:07,320 Speaker 1: is an appeal because obviously his policies and his directives 442 00:27:07,359 --> 00:27:09,919 Speaker 1: have been challenged even by his own LA County Deputy 443 00:27:09,960 --> 00:27:12,760 Speaker 1: District attorneys, and he is going to be spending a 444 00:27:12,840 --> 00:27:15,800 Speaker 1: lot of your taxpayer money on a top attorney. You 445 00:27:15,840 --> 00:27:19,560 Speaker 1: actually represented al Gore in the two thousand election dispute 446 00:27:20,480 --> 00:27:22,880 Speaker 1: and has appeared before the Supreme Court dozens of times. 447 00:27:22,920 --> 00:27:26,840 Speaker 1: So it says here his firm charges as much as 448 00:27:26,920 --> 00:27:29,440 Speaker 1: two thousand, four hundred and sixty five dollars an hour. 449 00:27:30,280 --> 00:27:34,800 Speaker 1: And apparently he has two distinctly opposite viewpoints on the 450 00:27:34,880 --> 00:27:39,240 Speaker 1: same issue, depending on whose side he's on. All right, 451 00:27:39,280 --> 00:27:42,640 Speaker 1: we'll talk about this after five, John and Ken k fine, oh, 452 00:27:42,880 --> 00:27:44,760 Speaker 1: I understand that next hour there's going to be a 453 00:27:44,800 --> 00:27:47,040 Speaker 1: really good story you got to hear about a West 454 00:27:47,119 --> 00:27:51,720 Speaker 1: Hollywood City Council member. West Hollywood recently voted to reduce 455 00:27:51,840 --> 00:27:55,119 Speaker 1: the number of La County Sheriff's deputies who are patrolling 456 00:27:55,320 --> 00:27:59,760 Speaker 1: the city in favor of security ambassadors. You need to 457 00:27:59,840 --> 00:28:02,480 Speaker 1: hear the story. Apparently Steve Greggory's putting it together was 458 00:28:02,480 --> 00:28:05,919 Speaker 1: supposed to present it at five thirty. Is that right, Johnny? 459 00:28:06,520 --> 00:28:08,560 Speaker 1: I have something to live for for another forty minutes. 460 00:28:08,600 --> 00:28:12,200 Speaker 1: Then I have a question for Deborah Mark. Yes, Ken, 461 00:28:12,880 --> 00:28:15,400 Speaker 1: did you or did you not kill p eighty nine? 462 00:28:16,200 --> 00:28:19,000 Speaker 1: I did not kill that poor mountain lion. Yes, it 463 00:28:19,160 --> 00:28:21,199 Speaker 1: wasn't my neck of the woods, but it was one 464 00:28:21,280 --> 00:28:24,240 Speaker 1: on one freeway in the Santa Monica Mountains, the area 465 00:28:24,280 --> 00:28:27,520 Speaker 1: of Woodland Hills to Soda And when Netta exits, isn't 466 00:28:27,560 --> 00:28:30,520 Speaker 1: this your territory? He was hit by as a woman 467 00:28:30,600 --> 00:28:35,080 Speaker 1: in a convertible. It was smiled and waved. She wearing 468 00:28:35,160 --> 00:28:38,880 Speaker 1: Hollywood glasses. You you hit an animal this size? Wouldn't 469 00:28:38,880 --> 00:28:42,080 Speaker 1: that do damage? Oh? God? Yes, oh yeah, that'd be 470 00:28:42,120 --> 00:28:44,840 Speaker 1: a big explosion of the picture of him. He's lying 471 00:28:44,880 --> 00:28:47,680 Speaker 1: there dead. Did you see it? No? I caller on 472 00:28:47,800 --> 00:28:54,040 Speaker 1: the tracking callers on him. So well, them's the brakes. 473 00:28:54,280 --> 00:28:56,600 Speaker 1: What um, what do you think mountain lion tastes like? 474 00:28:57,880 --> 00:29:01,160 Speaker 1: I wouldn't have a clue. Isn't there an we have 475 00:29:01,200 --> 00:29:02,920 Speaker 1: a road kill law now that you can know. Is 476 00:29:03,000 --> 00:29:05,440 Speaker 1: there a restaurant um? I think it's up in the 477 00:29:05,520 --> 00:29:07,680 Speaker 1: Northern Valley area. Oh, I know what you're talking about, 478 00:29:07,720 --> 00:29:12,760 Speaker 1: where they serve all kinds of wild game saddlebacks. Yea, yeah, 479 00:29:12,920 --> 00:29:14,760 Speaker 1: I've always wanted to go there. How about you know 480 00:29:14,920 --> 00:29:18,600 Speaker 1: that Deborah Market that sounds porn to you? First of all, 481 00:29:18,720 --> 00:29:21,080 Speaker 1: it is, and I've driven by it, and I know 482 00:29:21,280 --> 00:29:24,640 Speaker 1: people who are meat eaters that go there. They like it, Yeah, 483 00:29:24,680 --> 00:29:27,080 Speaker 1: they do. And then I'd love to see what the 484 00:29:27,120 --> 00:29:29,760 Speaker 1: menu looks like. Oh, I'll send it to you. And 485 00:29:29,840 --> 00:29:32,000 Speaker 1: they're allowed to do this. I thought there's some game 486 00:29:32,120 --> 00:29:34,520 Speaker 1: you can't really, I don't know exactly what they serve 487 00:29:34,600 --> 00:29:39,400 Speaker 1: because I have not gone there. Up, I've always wanted 488 00:29:39,440 --> 00:29:43,000 Speaker 1: to go. I've never had much in the way of 489 00:29:43,400 --> 00:29:46,520 Speaker 1: unusual game. I had alligator once. Yeah, I was in 490 00:29:46,600 --> 00:29:48,680 Speaker 1: a foreign country. They had tiger on the menu, but 491 00:29:49,080 --> 00:29:53,080 Speaker 1: pass the hotel where we had our wedding served alligator 492 00:29:53,120 --> 00:29:55,160 Speaker 1: on their lunch men. Ye, that's that's when I had it. 493 00:29:55,280 --> 00:29:56,560 Speaker 1: I had it like a couple of days before the 494 00:29:56,600 --> 00:29:58,960 Speaker 1: Why do needs alligators? How does it matter, and they're 495 00:29:59,000 --> 00:30:01,800 Speaker 1: gonna die like this poor thing got hit by a car. 496 00:30:01,880 --> 00:30:05,520 Speaker 1: It's dead, So serve it up, are you ken? I 497 00:30:05,560 --> 00:30:06,960 Speaker 1: don't know. That's why I asked, what does the mount 498 00:30:07,040 --> 00:30:09,200 Speaker 1: Lyon taste like? You want to hear some of the 499 00:30:09,600 --> 00:30:14,760 Speaker 1: dinners here, Tartar of New Zealand Elk, I'd have that. 500 00:30:18,360 --> 00:30:23,520 Speaker 1: Uh see, I don't see anything all like crazy buffalo. Yes, 501 00:30:23,720 --> 00:30:26,640 Speaker 1: we had buffalo jerky years ago. We had Ostrich jerky. 502 00:30:26,800 --> 00:30:30,640 Speaker 1: Trader Joe sells buffalo burgers. There's a lot of places 503 00:30:30,680 --> 00:30:34,160 Speaker 1: actually had buffalo burgers. Yeah, that's about his. Guys are 504 00:30:34,280 --> 00:30:37,600 Speaker 1: killing me here. Then you know what? I thought they 505 00:30:37,600 --> 00:30:40,680 Speaker 1: would have more strange exotic stuff. It's not that. Well, 506 00:30:40,720 --> 00:30:43,200 Speaker 1: you can check it off your list. That crazy. Yeah, 507 00:30:43,240 --> 00:30:46,480 Speaker 1: other than the elk and Elk burgers. I remember that 508 00:30:46,600 --> 00:30:49,719 Speaker 1: was the thing. There was some company, some entrepreneur who 509 00:30:49,800 --> 00:30:52,760 Speaker 1: was trying to sell elk burgers some years ago. Hm 510 00:30:53,000 --> 00:30:57,120 Speaker 1: as I remember eating an ant eater or like an army. 511 00:30:57,600 --> 00:31:01,640 Speaker 1: How about peacock? Peacock? Yeah, put a peacock on a platter. 512 00:31:01,840 --> 00:31:04,840 Speaker 1: That's a pretty flimsy bird. Though there's not much notat 513 00:31:04,880 --> 00:31:08,240 Speaker 1: there now like a flamingo. There's not much to it. Flamingo, 514 00:31:09,400 --> 00:31:13,440 Speaker 1: the side of flamingo. That'd be cruel. Cook a flamingo. 515 00:31:13,800 --> 00:31:16,360 Speaker 1: It'd be tough to chop up. An armadillo. It's here right, 516 00:31:16,760 --> 00:31:20,520 Speaker 1: Oh yeah, I saw a video of an arm until 517 00:31:20,680 --> 00:31:23,720 Speaker 1: the other day, and if it feels threatened, it rolls 518 00:31:23,760 --> 00:31:28,200 Speaker 1: itself up into a ball with its armor. Yeah. Yeah, 519 00:31:28,240 --> 00:31:30,480 Speaker 1: it's so. It's supposedly you can't penetrate. It looks like 520 00:31:30,560 --> 00:31:34,040 Speaker 1: a metal soccer ball, so if you try to eat that, 521 00:31:34,200 --> 00:31:36,880 Speaker 1: it'll probably get lodged in your throat. So I really 522 00:31:36,920 --> 00:31:38,920 Speaker 1: thought this place was It was crazy with all kinds 523 00:31:38,960 --> 00:31:40,760 Speaker 1: of weird animals, and it's just not. None of this 524 00:31:40,920 --> 00:31:43,400 Speaker 1: is on the menu. You don't eat weird stuff, though, 525 00:31:43,480 --> 00:31:46,760 Speaker 1: So I don't understand why you were so excited about it. Now, Debrah, 526 00:31:46,800 --> 00:31:49,600 Speaker 1: I thought they built wildlife crossings to try to these 527 00:31:49,680 --> 00:31:53,240 Speaker 1: critters get across the freeways. No, yes, there's one in Agora, 528 00:31:53,320 --> 00:31:56,160 Speaker 1: but it's not it's not done yet. Didn't Nobody told 529 00:31:56,200 --> 00:31:58,959 Speaker 1: the Mountain Lion I was not done yet. No, it's not. Besides, 530 00:31:59,080 --> 00:32:01,160 Speaker 1: they have to be educated and told where to cross. Well, 531 00:32:01,200 --> 00:32:04,959 Speaker 1: I'm confused. I don't know exactly how these animals are 532 00:32:05,000 --> 00:32:06,960 Speaker 1: going to figure it out. But I'm sure that there's 533 00:32:07,080 --> 00:32:13,800 Speaker 1: there's a plan they're gonna signs. Yeah, sure, see animals ready, 534 00:32:14,240 --> 00:32:17,160 Speaker 1: there's different routes for different animals until the street races. 535 00:32:17,240 --> 00:32:19,320 Speaker 1: Do you go this way? Mountain lions go? This way 536 00:32:19,480 --> 00:32:21,640 Speaker 1: is like a like a little it's supposed to be done. 537 00:32:22,920 --> 00:32:28,160 Speaker 1: The wildlife crossing is over the one on one? Oh 538 00:32:28,280 --> 00:32:31,320 Speaker 1: is that right? Two more years? Geez? How long does 539 00:32:31,360 --> 00:32:35,200 Speaker 1: it take to build a bridge? Well, obviously a long time. 540 00:32:36,480 --> 00:32:39,080 Speaker 1: You got one of these mountain lion field biologists. His 541 00:32:39,200 --> 00:32:43,440 Speaker 1: name is Jeff Psyekitch, and he's saying that what we've 542 00:32:43,520 --> 00:32:45,959 Speaker 1: learned the freeways or made your barriers to move it, well, 543 00:32:46,080 --> 00:32:49,480 Speaker 1: too bad. The one on one, the four or five 544 00:32:49,520 --> 00:32:52,200 Speaker 1: are like ten lanes. It's very dangerous. Most animals don't cross, 545 00:32:52,240 --> 00:32:54,160 Speaker 1: but every now and then, So he tried. The freeways 546 00:32:54,200 --> 00:32:56,040 Speaker 1: have been around for sixty years and they just figured 547 00:32:56,120 --> 00:33:01,680 Speaker 1: out that wildlife can't cross it. Wow, sixty years they 548 00:33:01,760 --> 00:33:05,600 Speaker 1: had to build to build a bridge. I don't know 549 00:33:05,640 --> 00:33:08,520 Speaker 1: what they do all day, I really don't. All right, 550 00:33:08,560 --> 00:33:12,320 Speaker 1: apparently we're gonna start next hour with Steve Gregory on 551 00:33:12,480 --> 00:33:16,040 Speaker 1: this West Hollywood City Council member story, it's a good one. 552 00:33:16,760 --> 00:33:20,160 Speaker 1: As I mentioned, they recently voted to downsize from the 553 00:33:20,320 --> 00:33:24,280 Speaker 1: La County Sheriff's deputy patrols to more guys wearing vests 554 00:33:24,360 --> 00:33:30,200 Speaker 1: with little security ambassador badges. It darn's out that. Stop. 555 00:33:30,280 --> 00:33:36,920 Speaker 1: I have a security badge. Stop, I'm an ambassador. So 556 00:33:37,080 --> 00:33:39,760 Speaker 1: Steve is going to come on next with this story 557 00:33:39,920 --> 00:33:44,880 Speaker 1: because the city council member is requesting protection from the 558 00:33:45,000 --> 00:33:48,120 Speaker 1: sheriff's deputies, not from the ambassadors. You'll find out. So 559 00:33:48,240 --> 00:33:50,400 Speaker 1: if that guy gets let out again, the one who's 560 00:33:50,400 --> 00:33:54,400 Speaker 1: whacking women with metal pipes, Oh, that's in downtown. Yeah. Yeah. 561 00:33:54,440 --> 00:33:56,160 Speaker 1: What are you supposed to do if he's whacking you 562 00:33:56,200 --> 00:33:57,920 Speaker 1: over the head with a metal pipe and you're supposed 563 00:33:57,960 --> 00:34:02,960 Speaker 1: to call mister ambassador, save me, Save me. Oh? I 564 00:34:03,520 --> 00:34:07,000 Speaker 1: couldn't resist this story about the Mountain lion. It has 565 00:34:07,040 --> 00:34:09,000 Speaker 1: a name that I have to you know how I 566 00:34:09,080 --> 00:34:13,600 Speaker 1: love names. A senior scientist for the Center for Biological 567 00:34:13,680 --> 00:34:18,279 Speaker 1: Diversity is Tiffany Yap. That brings new meaning when she's 568 00:34:18,280 --> 00:34:24,239 Speaker 1: speaking too much. It shut your yap, Yeap, Tiffany Yap, Yeap. 569 00:34:24,800 --> 00:34:27,879 Speaker 1: Steve Gregory's coming on deck John again, show Debra Mark 570 00:34:27,960 --> 00:34:30,960 Speaker 1: has news kf I AM six forty It's never been 571 00:34:31,040 --> 00:34:34,600 Speaker 1: more important to diversify your financial portfolio. Well, that's right. 572 00:34:34,920 --> 00:34:37,799 Speaker 1: The S ANDP is down twenty percent from the last year, 573 00:34:37,840 --> 00:34:40,480 Speaker 1: and this year looks even worse. 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