1 00:00:00,080 --> 00:00:03,040 Speaker 1: The views and opinions expressed in this podcast are solely 2 00:00:03,080 --> 00:00:06,279 Speaker 1: those of the authors and participants and do not necessarily 3 00:00:06,320 --> 00:00:10,160 Speaker 1: represent those of iHeart Media, Tenderfoot TV, or their employees. 4 00:00:10,760 --> 00:00:15,040 Speaker 1: This series contains discussions of violence and sexual violence. Listener 5 00:00:15,080 --> 00:00:21,759 Speaker 1: discretion is advised. Hey, it's been here. Today's a bonus 6 00:00:21,800 --> 00:00:24,880 Speaker 1: episode before we return to the regular series. So we've 7 00:00:24,880 --> 00:00:28,240 Speaker 1: been getting some interesting voicemails and questions and there's some 8 00:00:28,240 --> 00:00:30,840 Speaker 1: stuff we want to investigate and follow up on, and 9 00:00:30,920 --> 00:00:33,159 Speaker 1: doing this episode gives us a little more time to 10 00:00:33,280 --> 00:00:38,880 Speaker 1: do that. So anyway, let's get to it. Hey, then, 11 00:00:39,159 --> 00:00:42,239 Speaker 1: my name is Clarissa. I have been listening to your 12 00:00:42,400 --> 00:00:45,199 Speaker 1: podcast for the last few days, and it's kind of 13 00:00:45,200 --> 00:00:47,080 Speaker 1: crazy to me because I listened to a lot of 14 00:00:47,080 --> 00:00:52,680 Speaker 1: true crime podcasts and this one especially hit home because 15 00:00:52,920 --> 00:00:58,640 Speaker 1: it's where I live. Um. I live in Burnham, Illinois, 16 00:00:59,080 --> 00:01:04,120 Speaker 1: which is right between Chicago and Northwest Indiana. I'm right 17 00:01:04,160 --> 00:01:10,080 Speaker 1: between Hammond and Chicago technically, um and randomly, I also 18 00:01:10,120 --> 00:01:15,440 Speaker 1: lived in Texas Houston, So I feel super related to 19 00:01:15,480 --> 00:01:19,000 Speaker 1: this story and I just wanted to know what information 20 00:01:19,040 --> 00:01:23,320 Speaker 1: you're looking for. So if you have any uh, leaves 21 00:01:23,440 --> 00:01:26,119 Speaker 1: or anything that I could possibly help look into. Since 22 00:01:26,200 --> 00:01:29,240 Speaker 1: I live in the area, I would love to be 23 00:01:29,280 --> 00:01:33,720 Speaker 1: able to help get any information that I can. Hey, Clarissa, 24 00:01:33,840 --> 00:01:36,400 Speaker 1: thanks for the offer, and we'll definitely keep that in mind. 25 00:01:36,880 --> 00:01:38,760 Speaker 1: But probably the thing that would help the most of 26 00:01:38,840 --> 00:01:42,520 Speaker 1: this point is just spreading word about the podcast and 27 00:01:42,640 --> 00:01:45,839 Speaker 1: just kind of awareness about how you know, Darren Vaughan 28 00:01:46,080 --> 00:01:49,800 Speaker 1: might be linked to these other cold cases. I'm still 29 00:01:49,880 --> 00:01:52,800 Speaker 1: very interested in talking to people who knew on at 30 00:01:52,840 --> 00:01:54,960 Speaker 1: various stages of his life to try to verify some 31 00:01:55,040 --> 00:01:58,400 Speaker 1: of his stories. And I'm also interested in talking to 32 00:01:58,520 --> 00:02:02,040 Speaker 1: China or people that knew her um about how it 33 00:02:02,240 --> 00:02:04,480 Speaker 1: was that she knew that Tira Beatty was dead in 34 00:02:04,520 --> 00:02:08,880 Speaker 1: an abandoned building, what was going on there? And Clarissa 35 00:02:09,160 --> 00:02:12,079 Speaker 1: also stay tuned because in an upcoming episode, we are 36 00:02:12,080 --> 00:02:15,320 Speaker 1: going to discuss a cluster of murders in Chicago and 37 00:02:15,400 --> 00:02:19,120 Speaker 1: explore whether or not Von might have been connected to it. Uh. 38 00:02:19,120 --> 00:02:25,519 Speaker 1: Speaking of Chicago, here's another voicemail. This is Pam Zeckman, 39 00:02:26,000 --> 00:02:31,920 Speaker 1: former CBS reporter who did the story with Tom Hargrove 40 00:02:32,120 --> 00:02:36,720 Speaker 1: on the possible serial killer in the Chicago area. I 41 00:02:36,800 --> 00:02:39,839 Speaker 1: just wanted to tell you that I listened to your 42 00:02:40,480 --> 00:02:43,560 Speaker 1: podcasts and you did a terrific job with it, and 43 00:02:43,600 --> 00:02:46,280 Speaker 1: I know you have a lot more coming out, and 44 00:02:46,320 --> 00:02:49,400 Speaker 1: I wanted to just tell you that I thought I 45 00:02:49,440 --> 00:02:53,520 Speaker 1: was very impressed. Thanks bye. I was really excited to 46 00:02:53,560 --> 00:02:57,080 Speaker 1: get that voicemail. I wonder if if Hargrove shared the 47 00:02:57,080 --> 00:03:00,240 Speaker 1: podcast with her for context she was actually involved opped 48 00:03:00,240 --> 00:03:03,240 Speaker 1: in them rage Tavern staying that I mentioned in episode 49 00:03:03,280 --> 00:03:06,280 Speaker 1: one where the journalists made the fake bar to expose 50 00:03:06,320 --> 00:03:10,160 Speaker 1: city corruption. Um and Harger have actually mentioned her to me. 51 00:03:10,680 --> 00:03:12,760 Speaker 1: Um in one of his interviews that he was thrilled 52 00:03:12,760 --> 00:03:16,000 Speaker 1: to work with her about that cluster of serial murders 53 00:03:16,000 --> 00:03:20,040 Speaker 1: in Chicago. So the next message comes from Twitter, so 54 00:03:20,080 --> 00:03:23,239 Speaker 1: we'll have a text to speech algorithm read that one. 55 00:03:24,280 --> 00:03:28,000 Speaker 1: What's up, man, love listening to your pods. I'm from 56 00:03:28,000 --> 00:03:31,880 Speaker 1: Northwest Indiana. Back in the summer oft I met my 57 00:03:32,000 --> 00:03:36,160 Speaker 1: uncle at eighteen Street Brewery in Gary. My uncle works 58 00:03:36,160 --> 00:03:39,280 Speaker 1: in downtown Chicago and rides the train to work, and 59 00:03:39,320 --> 00:03:42,560 Speaker 1: there is a train stopped right by the brewery in Gary. 60 00:03:43,080 --> 00:03:45,480 Speaker 1: So it had to be around five o'clock or so. 61 00:03:46,040 --> 00:03:49,200 Speaker 1: There were probably ten to twenty people total at the brewery. 62 00:03:49,600 --> 00:03:53,320 Speaker 1: It was a Friday night, normally a pretty mixed race crowd. 63 00:03:53,760 --> 00:03:56,160 Speaker 1: This guy is sitting at the bar stuck out though. 64 00:03:56,480 --> 00:03:59,320 Speaker 1: A couple of months pass and van S mugshot pops 65 00:03:59,400 --> 00:04:02,920 Speaker 1: up on the low cool news. Uncle and I immediately 66 00:04:02,960 --> 00:04:05,480 Speaker 1: text each other and say, wasn't that the guy we've 67 00:04:05,480 --> 00:04:08,880 Speaker 1: seen at the brewery? He had one to two beers 68 00:04:08,960 --> 00:04:11,840 Speaker 1: while we were there. He was chatting with the bartender 69 00:04:11,880 --> 00:04:14,880 Speaker 1: and people at the bar. He left before us. Not 70 00:04:15,040 --> 00:04:18,039 Speaker 1: a groundbreaking story for you, but maybe you can reach 71 00:04:18,040 --> 00:04:20,000 Speaker 1: out to the brewery and see if he was a 72 00:04:20,040 --> 00:04:25,880 Speaker 1: regular or something. Good luck and keep crushing it. So 73 00:04:25,920 --> 00:04:27,919 Speaker 1: I contacted that brewery. They were saying it was a 74 00:04:27,960 --> 00:04:32,160 Speaker 1: small operation back in so probably the owner is the 75 00:04:32,200 --> 00:04:35,000 Speaker 1: one to speak to you. So I've tried to get 76 00:04:35,000 --> 00:04:37,400 Speaker 1: in contact with him and I'll let you guys know 77 00:04:37,480 --> 00:04:41,640 Speaker 1: if anything comes from it. Also, a computer pronouncing Vaughan's 78 00:04:41,760 --> 00:04:44,200 Speaker 1: last name is Van reminded me that that's a question 79 00:04:44,240 --> 00:04:46,880 Speaker 1: I've gotten a couple of times now. So his name 80 00:04:47,040 --> 00:04:49,680 Speaker 1: is Darren Vaughan, but a lot of the early news 81 00:04:49,680 --> 00:04:53,320 Speaker 1: reports pronounced it as Van, so I thought that was 82 00:04:53,440 --> 00:04:56,480 Speaker 1: his name until months into the project, and a lot 83 00:04:56,480 --> 00:04:58,960 Speaker 1: of people I interviewed. Also, I thought his name was 84 00:04:59,040 --> 00:05:02,800 Speaker 1: pronounced van and, but from the interrogation tapes, you know 85 00:05:02,839 --> 00:05:05,799 Speaker 1: it's clearly Vaughan and and a lot of people close 86 00:05:05,839 --> 00:05:08,520 Speaker 1: to the case, um, you know know that it's Vaughn. 87 00:05:08,680 --> 00:05:11,839 Speaker 1: But but even then, sometimes if I was interviewing them 88 00:05:11,839 --> 00:05:14,280 Speaker 1: early on, I might have said Van and they started 89 00:05:14,279 --> 00:05:19,640 Speaker 1: saying Van Hi. Then this is how they tuned him 90 00:05:19,680 --> 00:05:23,040 Speaker 1: from Birmingham, Alabama. I've been listening to Algorithm every week 91 00:05:23,080 --> 00:05:25,479 Speaker 1: on my directory work, So thanks for the great interial 92 00:05:26,279 --> 00:05:29,080 Speaker 1: question for you for the upcoming human A. Are you 93 00:05:29,120 --> 00:05:33,040 Speaker 1: finding it challenging to navigate the language around sex works 94 00:05:33,040 --> 00:05:37,320 Speaker 1: for the series, especially since Vawn himself uses such dehumanizing 95 00:05:37,400 --> 00:05:40,120 Speaker 1: language when it comes from the victims. Thanks in advance 96 00:05:40,200 --> 00:05:43,640 Speaker 1: for answering my question. I've gotten comments from a couple 97 00:05:43,640 --> 00:05:46,919 Speaker 1: of listeners about my use of the word prostitute on 98 00:05:46,960 --> 00:05:50,320 Speaker 1: the podcast, basically telling me that there's a lot of 99 00:05:50,360 --> 00:05:54,680 Speaker 1: stigma associated with that word, um, and that many prostitutes 100 00:05:54,720 --> 00:05:57,479 Speaker 1: prefer to be called sex workers. I'm going to try 101 00:05:57,480 --> 00:05:59,880 Speaker 1: to be better about using the term sex worker verse 102 00:06:00,040 --> 00:06:03,280 Speaker 1: as prostitute, but there are some places where I think 103 00:06:03,360 --> 00:06:06,520 Speaker 1: is still appropriate to use the term you know, for example, 104 00:06:06,640 --> 00:06:10,120 Speaker 1: if we're talking about statistics, like you know, what's the 105 00:06:10,200 --> 00:06:14,240 Speaker 1: percentage of the victims of serial killers who are prostitutes, 106 00:06:14,400 --> 00:06:16,640 Speaker 1: and you know what's the percentage of victims who are 107 00:06:16,640 --> 00:06:20,080 Speaker 1: sex workers? Those numbers will be different, and I think 108 00:06:20,120 --> 00:06:22,640 Speaker 1: we need to be you know, very specific sometimes, right, 109 00:06:22,760 --> 00:06:28,320 Speaker 1: because sex workers is this bigger, more all encompassing term 110 00:06:28,360 --> 00:06:33,600 Speaker 1: that includes people like strippers or people involved in pornography, 111 00:06:33,760 --> 00:06:39,839 Speaker 1: and you know, those people's risks, for example, being victimized 112 00:06:39,920 --> 00:06:43,280 Speaker 1: in crime, are going to be different than people who 113 00:06:43,320 --> 00:06:46,559 Speaker 1: are engaging in prostitution. And in fact, you know, even 114 00:06:46,600 --> 00:06:51,240 Speaker 1: within prostitution there's different levels. Street prostitution is a lot 115 00:06:51,360 --> 00:06:54,240 Speaker 1: higher risk than being in your own room the way 116 00:06:54,279 --> 00:06:58,159 Speaker 1: Africa was is actually one of the least risky ways 117 00:06:58,160 --> 00:07:02,279 Speaker 1: of doing prostitution. But you know, nothing is ever completely 118 00:07:02,360 --> 00:07:06,159 Speaker 1: risk free. There are also sex workers who do self 119 00:07:06,200 --> 00:07:14,080 Speaker 1: identify as prostitutes. For example, here's a voicemail from Maxine Dugan. Hey, Hi, 120 00:07:14,320 --> 00:07:19,320 Speaker 1: it's Maxcine Dugan UM callings from San Francisco, California. I'm 121 00:07:19,320 --> 00:07:23,520 Speaker 1: with the Erotic Service Providers Union UM. The Erotic Service 122 00:07:23,560 --> 00:07:27,880 Speaker 1: Providers Union is by and for those who labor erotically 123 00:07:28,000 --> 00:07:33,160 Speaker 1: to gain you know, their agency through solidarity organizing for occupational, 124 00:07:33,560 --> 00:07:38,040 Speaker 1: social and economic rights UM and I myself work as 125 00:07:38,040 --> 00:07:43,000 Speaker 1: a prostitute of thirty plus years, so I find that 126 00:07:43,080 --> 00:07:46,920 Speaker 1: your show is phenomenal in that the woman who was 127 00:07:46,960 --> 00:07:49,600 Speaker 1: looking for Africa, her friend is able to tell the 128 00:07:49,640 --> 00:07:52,480 Speaker 1: police that she knows the phone number of the guys 129 00:07:52,480 --> 00:07:55,160 Speaker 1: who saw Africa laugh, and she gives it to the 130 00:07:55,160 --> 00:07:58,600 Speaker 1: police and they're able to get him into custody. That 131 00:07:58,720 --> 00:08:02,920 Speaker 1: woman's faced inn array of felony charges for facilitating prostitution, 132 00:08:03,600 --> 00:08:06,600 Speaker 1: you know, which has been recriminalized in recent years as 133 00:08:06,640 --> 00:08:10,240 Speaker 1: sex trafficking, when really she's just a part of Africa's 134 00:08:10,320 --> 00:08:13,920 Speaker 1: you know security. You know. I'm glad she was able 135 00:08:13,960 --> 00:08:15,960 Speaker 1: to do the right thing and tell the police the 136 00:08:16,000 --> 00:08:20,400 Speaker 1: crucial information to end this particular serial killer's reign of terror. 137 00:08:20,920 --> 00:08:26,000 Speaker 1: She deserves a metal. Marvin and Tara's story reminds me 138 00:08:26,320 --> 00:08:29,880 Speaker 1: of Sarah Derrid's story, who goes missing in the Lower 139 00:08:29,920 --> 00:08:34,760 Speaker 1: East Side of Vancouver, BC in the late ninete Sarah's 140 00:08:34,800 --> 00:08:37,600 Speaker 1: customer tries to report her missing to the police, but 141 00:08:37,640 --> 00:08:40,240 Speaker 1: the police had some arbitrary rule that had to be 142 00:08:40,280 --> 00:08:43,960 Speaker 1: a relative to report the missing person, so the customer 143 00:08:44,000 --> 00:08:49,080 Speaker 1: context Sarah's sister, Maggie, and Maggie was able to report um, 144 00:08:49,200 --> 00:08:52,360 Speaker 1: but given Sarah's status as a street based drug using 145 00:08:52,400 --> 00:08:56,240 Speaker 1: prostitute you know, which are all criminalized activities, the police 146 00:08:56,280 --> 00:08:59,560 Speaker 1: don't find her until they find her DNA on the 147 00:08:59,600 --> 00:09:04,280 Speaker 1: property of a now convicted serial killer, Robert Pickton. The 148 00:09:04,320 --> 00:09:08,280 Speaker 1: podcast also reminds me of the Green River Killer victims, 149 00:09:08,400 --> 00:09:12,319 Speaker 1: whose boyfriends were often labeled as TIMPs when they tried 150 00:09:12,360 --> 00:09:16,360 Speaker 1: to report the missing to the police, so the Seattle 151 00:09:16,400 --> 00:09:21,160 Speaker 1: Police Department dismissed them because the missing people's status as 152 00:09:21,280 --> 00:09:24,920 Speaker 1: street based prostitutes, and the police also responded with conducting 153 00:09:25,000 --> 00:09:28,520 Speaker 1: stam operations for certain known prostitution areas you know, which 154 00:09:28,520 --> 00:09:31,240 Speaker 1: only had the effect of forcing those workers until less 155 00:09:31,240 --> 00:09:34,640 Speaker 1: populated and not will at areas where they became easier 156 00:09:34,679 --> 00:09:39,000 Speaker 1: targets for the Green River Killer. I really appreciate that 157 00:09:39,000 --> 00:09:42,560 Speaker 1: that feedback, Maccine, and thanks for listening. I hope that 158 00:09:42,720 --> 00:09:47,280 Speaker 1: regardless of how anyone feels about sex work and it's legality, 159 00:09:48,000 --> 00:09:50,520 Speaker 1: I hope that we can at least all agree that, 160 00:09:50,800 --> 00:09:52,959 Speaker 1: you know, we need to find some ways to make 161 00:09:53,000 --> 00:09:56,120 Speaker 1: it as safe as possible. Sex workers need to be 162 00:09:56,120 --> 00:09:58,839 Speaker 1: able to go to police to report crimes, and when 163 00:09:58,880 --> 00:10:02,840 Speaker 1: they do report crimes, they need to be taken seriously. Similarly, 164 00:10:03,040 --> 00:10:06,560 Speaker 1: when a sex worker disappears, police need to to take 165 00:10:06,600 --> 00:10:08,840 Speaker 1: that seriously as well, and and treat it the way 166 00:10:08,880 --> 00:10:12,280 Speaker 1: they would treat any other missing person case. We need 167 00:10:12,320 --> 00:10:14,520 Speaker 1: to demand that from the police, and we need to 168 00:10:14,559 --> 00:10:31,120 Speaker 1: hold the police accountable. So our next voicemail comes from Lima, Ohio, 169 00:10:32,040 --> 00:10:34,440 Speaker 1: which is the city in western Ohio where vond moved 170 00:10:34,440 --> 00:10:40,440 Speaker 1: as a teenager and went to high school. I'm currently 171 00:10:40,520 --> 00:10:45,680 Speaker 1: listening to your episode where mentioned bomb was dan Lima 172 00:10:45,760 --> 00:10:51,040 Speaker 1: and graduated in nine. I live outside of Lima. Lima 173 00:10:51,240 --> 00:10:55,079 Speaker 1: is a really freaking rous town. Um, I don't go 174 00:10:55,280 --> 00:10:57,839 Speaker 1: to Lima for anything. I know he would have been 175 00:10:57,840 --> 00:11:02,000 Speaker 1: a juvenile, but I just wondered if he checked any 176 00:11:02,040 --> 00:11:05,120 Speaker 1: and soul murders at that time that might have fit 177 00:11:06,240 --> 00:11:10,560 Speaker 1: m oh. I also wondered, um, did he ever come 178 00:11:10,640 --> 00:11:15,320 Speaker 1: back to Lina to visit his mother. It's a great podcast, 179 00:11:15,400 --> 00:11:19,720 Speaker 1: thank you. So in the interrogations, Vaughan didn't confess to 180 00:11:19,720 --> 00:11:22,520 Speaker 1: any murders in Lima. He does mention that during that 181 00:11:22,559 --> 00:11:25,400 Speaker 1: time he was arrested um. He says he was arrested 182 00:11:25,440 --> 00:11:28,199 Speaker 1: as a juvenile on a cun running charge. I was 183 00:11:28,240 --> 00:11:30,840 Speaker 1: going on probation in line as a juven I remember, 184 00:11:30,840 --> 00:11:33,840 Speaker 1: I all were you there because there was shiploaded guns. 185 00:11:33,920 --> 00:11:36,320 Speaker 1: I think we was we're shiving like two or three 186 00:11:36,360 --> 00:11:38,559 Speaker 1: hundred guns of money. I don't know what they gave, 187 00:11:38,720 --> 00:11:40,520 Speaker 1: but I know they dropped the gun charge because I 188 00:11:40,600 --> 00:11:43,960 Speaker 1: was the kids. Because they wanted to adults. I think 189 00:11:43,960 --> 00:11:46,320 Speaker 1: you a tl but he wanted to a dealt so 190 00:11:46,440 --> 00:11:48,440 Speaker 1: you think you dealt with a t F. I didn't 191 00:11:48,440 --> 00:11:50,760 Speaker 1: deal with him. I think they deal with other part 192 00:11:50,800 --> 00:11:52,440 Speaker 1: of the case. They just want to get the kids 193 00:11:52,440 --> 00:11:55,319 Speaker 1: out of the way. I got you because they wanted 194 00:11:55,360 --> 00:11:58,319 Speaker 1: the help. They wanted the people that was actually moving 195 00:11:58,360 --> 00:12:01,160 Speaker 1: in crazy and achievement. Right. What was the name of 196 00:12:01,160 --> 00:12:03,480 Speaker 1: the game that was doing old game? They hacked They 197 00:12:03,720 --> 00:12:07,080 Speaker 1: didn't have games. Then. We just had a bunch of 198 00:12:07,080 --> 00:12:09,559 Speaker 1: trades in his white boys. When I hooked up with 199 00:12:09,960 --> 00:12:13,240 Speaker 1: they wanted on some of my classmates, bigger brothers and 200 00:12:13,360 --> 00:12:16,800 Speaker 1: uncles and stuff like that, right, because they've been eyeballing 201 00:12:16,840 --> 00:12:19,040 Speaker 1: them for a while. I don't know if ant F 202 00:12:19,160 --> 00:12:21,040 Speaker 1: gun was they one of them, but I know it 203 00:12:21,160 --> 00:12:23,599 Speaker 1: was a whole, big old mess a valley. Was it 204 00:12:23,720 --> 00:12:27,920 Speaker 1: in the favorite No, they kept pretty quiet. They rated 205 00:12:27,920 --> 00:12:31,000 Speaker 1: on two or three houses. We had guns all over 206 00:12:31,000 --> 00:12:33,880 Speaker 1: the place. I remember that, and I was like, Hey, 207 00:12:33,880 --> 00:12:36,199 Speaker 1: told him mom, he's a kid. We don't even want him. 208 00:12:36,360 --> 00:12:39,200 Speaker 1: We got the delts. I know they wanted our guy 209 00:12:39,320 --> 00:12:42,160 Speaker 1: because our guy had the nations to other guy. You 210 00:12:42,160 --> 00:12:44,720 Speaker 1: know what I'm saying. Yeah, they're trying to move up 211 00:12:44,720 --> 00:12:46,880 Speaker 1: because I'm one of my best friends. I never speak 212 00:12:46,920 --> 00:12:51,680 Speaker 1: to you again, said you brought that trader to us. Really, dude, 213 00:12:51,840 --> 00:12:54,439 Speaker 1: that broke into it, Like I told you, droking to 214 00:12:54,480 --> 00:12:58,080 Speaker 1: his stepfather. I've had all the guns, um, he told 215 00:12:58,240 --> 00:13:02,160 Speaker 1: He told him everybody. Essentially, there's a kid he was 216 00:13:02,200 --> 00:13:05,440 Speaker 1: friends with. His dad owned a ton of guns, and 217 00:13:05,480 --> 00:13:08,120 Speaker 1: they stole those guns and sold them, and then the 218 00:13:08,200 --> 00:13:11,160 Speaker 1: kid's mom found out, and you know, the the kid 219 00:13:11,280 --> 00:13:13,760 Speaker 1: ended up getting them all in trouble. That's the only 220 00:13:13,840 --> 00:13:17,200 Speaker 1: crime he mentions from that time period. But at the 221 00:13:17,240 --> 00:13:19,960 Speaker 1: same time, just because he didn't confess any murders doesn't 222 00:13:20,000 --> 00:13:24,679 Speaker 1: mean they didn't happen, especially because he wanted the death penalty. Um. 223 00:13:24,800 --> 00:13:27,720 Speaker 1: And he said he didn't want to involve other jurisdictions, 224 00:13:27,760 --> 00:13:30,640 Speaker 1: so he was only going to confess to murders in Indiana. 225 00:13:31,200 --> 00:13:34,520 Speaker 1: So Von would have been in Lima from around seven 226 00:13:34,559 --> 00:13:38,400 Speaker 1: to and according to Hargrove's data set, there is one 227 00:13:38,520 --> 00:13:42,199 Speaker 1: unsolved strangulation from this time period. It's a thirty five 228 00:13:42,240 --> 00:13:47,080 Speaker 1: year old black woman who was strangled in it's anonymized data, 229 00:13:47,160 --> 00:13:49,400 Speaker 1: so you don't have a name, you don't have a month. 230 00:13:49,440 --> 00:13:52,480 Speaker 1: That makes it hard to find articles about it. But 231 00:13:52,600 --> 00:13:56,240 Speaker 1: I did find the Ohio Attorney General's office lists of 232 00:13:56,360 --> 00:13:58,520 Speaker 1: unsolved homicides, and I tried to look it up on 233 00:13:58,600 --> 00:14:01,400 Speaker 1: there and it didn't show up, you know, So I'm 234 00:14:01,400 --> 00:14:03,400 Speaker 1: not exactly sure what that means. It might mean it 235 00:14:03,480 --> 00:14:06,640 Speaker 1: was originally entered into the database is unsolved, but it's 236 00:14:06,679 --> 00:14:09,840 Speaker 1: been solved um sometime later, and that's why it's not 237 00:14:09,960 --> 00:14:13,840 Speaker 1: on this cold case database that the higher Attorney General keeps. 238 00:14:14,320 --> 00:14:19,360 Speaker 1: Or it might be that that jurisdiction which isn't actually Lima, 239 00:14:19,680 --> 00:14:23,880 Speaker 1: but it is Fort Shawnee, um, you know. So it 240 00:14:23,920 --> 00:14:27,600 Speaker 1: could be something where the Trinee General asked Evere to 241 00:14:27,640 --> 00:14:31,120 Speaker 1: submit their cold cases. They didn't submit it. Um. So 242 00:14:31,160 --> 00:14:34,200 Speaker 1: if anyone knows anything about the strangulation of a woman 243 00:14:34,480 --> 00:14:39,600 Speaker 1: in Fort Shawnee. Um. In, if someone else wants to 244 00:14:39,640 --> 00:14:41,640 Speaker 1: take up this lou thing and tell me what they find, 245 00:14:41,680 --> 00:14:46,760 Speaker 1: I'd really appreciate that, alright. So the next message comes 246 00:14:46,800 --> 00:14:50,480 Speaker 1: from Facebook. It's a message from someone who came across 247 00:14:50,480 --> 00:14:54,520 Speaker 1: the podcast. They're talking about Vaughan and they say he 248 00:14:54,640 --> 00:14:57,320 Speaker 1: was a door fiend who used to hang out. I 249 00:14:57,320 --> 00:15:00,360 Speaker 1: don't know what that means. Door fiend, drug fiend. Um. 250 00:15:00,400 --> 00:15:02,160 Speaker 1: He was a door fiend who used to hang out 251 00:15:02,240 --> 00:15:05,760 Speaker 1: in a crack house off Broadway and forty three. Whenever 252 00:15:05,760 --> 00:15:08,720 Speaker 1: he got high, he tapped into satan. He had a routine. 253 00:15:09,080 --> 00:15:12,760 Speaker 1: He appeared disoriented so that a crack would leave with him. 254 00:15:12,840 --> 00:15:16,400 Speaker 1: After he flashed some money and hit the dope, they leave. 255 00:15:16,600 --> 00:15:20,000 Speaker 1: They were never missed. I told Gary at a recorded 256 00:15:20,000 --> 00:15:23,400 Speaker 1: council meeting about sanitation that they had a serial killer. 257 00:15:24,000 --> 00:15:27,000 Speaker 1: He lived off a fifty second. He had been killing women. 258 00:15:27,440 --> 00:15:31,480 Speaker 1: His partner is a serial killer too. UM. So this 259 00:15:31,520 --> 00:15:34,560 Speaker 1: person wanted to remain anonymous. I've actually gotten back in 260 00:15:34,600 --> 00:15:38,680 Speaker 1: touch with her. UM heard her story. It is pretty wild, 261 00:15:38,920 --> 00:15:42,080 Speaker 1: so look forward to that in one of these upcoming episodes. 262 00:15:42,840 --> 00:15:48,240 Speaker 1: She encountered Vaughan during this period when he was committing 263 00:15:48,240 --> 00:15:50,600 Speaker 1: a bunch of these crimes. And some of it seems 264 00:15:50,640 --> 00:15:54,880 Speaker 1: to also verify UM information I've gotten from other sources. 265 00:15:55,120 --> 00:15:58,120 Speaker 1: So yeah, alright, here's here's when I've gotten a couple 266 00:15:58,120 --> 00:16:00,360 Speaker 1: of times I've gotten a couple of people will ask 267 00:16:00,400 --> 00:16:02,840 Speaker 1: about my accent. I also know the way I talk 268 00:16:03,000 --> 00:16:05,760 Speaker 1: can sound different when I'm in the interviews versus when 269 00:16:05,760 --> 00:16:09,440 Speaker 1: I'm narrating. It is harder than you might think to 270 00:16:09,600 --> 00:16:12,800 Speaker 1: sound natural and keep your voice consistent across the thirty 271 00:16:12,800 --> 00:16:17,080 Speaker 1: minute episode. And as for the accent, I grew up 272 00:16:17,080 --> 00:16:20,800 Speaker 1: outside DC in northern Virginia. I did live for a 273 00:16:20,840 --> 00:16:22,800 Speaker 1: couple of years in Mexico when I was a kid, 274 00:16:23,000 --> 00:16:24,800 Speaker 1: you know, around the time I was first learning how 275 00:16:24,800 --> 00:16:28,040 Speaker 1: to speak, So some of my speech patterned might come 276 00:16:28,080 --> 00:16:32,400 Speaker 1: from that experience as well. Some people have been asking 277 00:16:32,440 --> 00:16:35,440 Speaker 1: for more nuts and bolts information about the algorithm and 278 00:16:35,800 --> 00:16:38,560 Speaker 1: how it works. Uh, if you go back to episode two, 279 00:16:38,640 --> 00:16:41,640 Speaker 1: I think you can get a fuller explanation there. But 280 00:16:41,680 --> 00:16:44,160 Speaker 1: I think it's maybe just people thinking that the algorithm 281 00:16:44,400 --> 00:16:47,760 Speaker 1: is more complicated than it is. Um. The first thing 282 00:16:47,800 --> 00:16:51,040 Speaker 1: it does is it groups together murders based on geography, 283 00:16:51,360 --> 00:16:54,520 Speaker 1: the victim's gender, and the method of killing. There're been 284 00:16:54,560 --> 00:16:56,680 Speaker 1: a couple of different versions of the algorithm. I think 285 00:16:56,680 --> 00:17:00,680 Speaker 1: the original one also factored in the victim's age. UM. 286 00:17:00,800 --> 00:17:04,080 Speaker 1: Now it's simpler and it just focuses on geography, gender, 287 00:17:04,200 --> 00:17:07,800 Speaker 1: method of killing. You know, they they've compiled over seven 288 00:17:07,840 --> 00:17:13,080 Speaker 1: hundred thousand homicides mainly from FBI data UM and you 289 00:17:13,119 --> 00:17:16,280 Speaker 1: can then kind of divide those up into a hundred 290 00:17:16,280 --> 00:17:19,520 Speaker 1: thousand different groups, right, and so in each one of 291 00:17:19,560 --> 00:17:23,080 Speaker 1: these groups, it will be the same place, all the 292 00:17:23,160 --> 00:17:26,400 Speaker 1: victims will be the same gender, killed in the same way. 293 00:17:26,920 --> 00:17:29,600 Speaker 1: Now you have these a hundred thousand different groups, and 294 00:17:29,640 --> 00:17:33,160 Speaker 1: you can rank those by the percentage of murders that 295 00:17:33,280 --> 00:17:37,720 Speaker 1: were solved, and you can see which clusters have extremely 296 00:17:37,760 --> 00:17:41,680 Speaker 1: low solution rates, right, so where they haven't made an 297 00:17:41,760 --> 00:17:44,000 Speaker 1: arrest or or they at least didn't make an arrest 298 00:17:44,600 --> 00:17:47,080 Speaker 1: at the time that they had entered it into the 299 00:17:47,160 --> 00:17:51,080 Speaker 1: FBI's Supplemental homicide reports UM, and you can look for 300 00:17:51,119 --> 00:17:54,520 Speaker 1: clusters that have extremely low solution rates, and you can 301 00:17:54,600 --> 00:17:57,359 Speaker 1: look at that kind of across the entire time period 302 00:17:57,400 --> 00:17:59,760 Speaker 1: that they have data for, or you can do this 303 00:18:00,080 --> 00:18:05,080 Speaker 1: lighting window analysis where you look for a specific time 304 00:18:05,119 --> 00:18:09,440 Speaker 1: period where that area had, you know, an extremely low 305 00:18:09,520 --> 00:18:13,919 Speaker 1: solution rate for that particular type of murder. Right. And 306 00:18:13,960 --> 00:18:16,399 Speaker 1: one of the explanations for why they might have that 307 00:18:16,520 --> 00:18:19,400 Speaker 1: low solution rate is because there's a serial killer who 308 00:18:19,440 --> 00:18:23,000 Speaker 1: is active, who is getting away with multiple crimes and 309 00:18:23,320 --> 00:18:26,719 Speaker 1: making that type of crime harder to solve. So Hargrove 310 00:18:26,760 --> 00:18:30,119 Speaker 1: believes that these clusters that have very low solution rates, 311 00:18:30,560 --> 00:18:34,040 Speaker 1: those are more likely to contain victims of serial killers. 312 00:18:34,119 --> 00:18:37,320 Speaker 1: And that's in part because that's what you see with 313 00:18:37,359 --> 00:18:39,480 Speaker 1: the Green River killer, and a lot of the other 314 00:18:39,720 --> 00:18:43,040 Speaker 1: clusters that he looked at early on um seemed to 315 00:18:43,080 --> 00:18:47,040 Speaker 1: also match that pattern. Um. But this stuff isn't an 316 00:18:47,040 --> 00:18:50,680 Speaker 1: exact science, right, So just because one of these clusters 317 00:18:50,720 --> 00:18:53,800 Speaker 1: has a low solution rate doesn't mean that that area 318 00:18:53,920 --> 00:18:57,400 Speaker 1: necessarily had a serial killer, or that even if they 319 00:18:57,400 --> 00:19:01,120 Speaker 1: did have a serial killer active in that area during 320 00:19:01,160 --> 00:19:03,639 Speaker 1: that time period, it doesn't mean that the killer was 321 00:19:03,720 --> 00:19:09,399 Speaker 1: responsible for all of the murders in that cluster. Right. So, UM, imagine, 322 00:19:09,560 --> 00:19:13,080 Speaker 1: you know, Vaughan is killing all these people, right, but 323 00:19:13,240 --> 00:19:17,479 Speaker 1: it's still very possible that someone else could strangle someone. 324 00:19:18,200 --> 00:19:20,960 Speaker 1: UM in Lake County during that same time period, right, 325 00:19:21,040 --> 00:19:24,480 Speaker 1: and that murder also doesn't get solved, and the algorithm 326 00:19:24,520 --> 00:19:26,639 Speaker 1: has no way of separating them, right. So it's not 327 00:19:26,680 --> 00:19:32,080 Speaker 1: this magic bullet that only identifies murders by serial killers, 328 00:19:32,200 --> 00:19:34,960 Speaker 1: but but it can kind of flag that they're an 329 00:19:35,080 --> 00:19:38,800 Speaker 1: unusual number of you know, this certain kind of murder 330 00:19:39,160 --> 00:19:42,600 Speaker 1: that haven't been solved, and you know, I think it's 331 00:19:42,720 --> 00:19:46,080 Speaker 1: it's at least telling you that something is going on there. Right, So, 332 00:19:46,160 --> 00:19:48,479 Speaker 1: even if it's not a serial killer, why aren't these 333 00:19:48,560 --> 00:19:51,600 Speaker 1: murders being solved? And maybe someone should look into them? 334 00:19:51,640 --> 00:19:53,800 Speaker 1: And we're doing a deep dive right now into this 335 00:19:53,840 --> 00:19:57,040 Speaker 1: cluster and Gary, but I imagine that you would find, 336 00:19:57,440 --> 00:20:00,359 Speaker 1: you know, incredibly interesting stories, which I for one of 337 00:20:00,400 --> 00:20:14,639 Speaker 1: these clusters you looked into. I've also had some listeners 338 00:20:14,680 --> 00:20:16,919 Speaker 1: who are hoping that this would be less of a 339 00:20:16,960 --> 00:20:20,240 Speaker 1: deep dive into Vaughan and you know, this one specific 340 00:20:20,240 --> 00:20:23,760 Speaker 1: case and more more of a deep dive into the 341 00:20:23,800 --> 00:20:27,080 Speaker 1: algorithm and how it and other technology could be used 342 00:20:27,080 --> 00:20:31,000 Speaker 1: to find serial killers in different cities across the country. 343 00:20:31,600 --> 00:20:34,160 Speaker 1: So I picked this case in particular because I think 344 00:20:34,320 --> 00:20:37,880 Speaker 1: it's very illustrative of the algorithm's potential. But I am 345 00:20:38,000 --> 00:20:42,639 Speaker 1: very interested in exploring clusters and other cities, um, you know, 346 00:20:42,800 --> 00:20:47,360 Speaker 1: especially kind of ongoing clusters where you know there might 347 00:20:47,400 --> 00:20:50,760 Speaker 1: be someone out there and an active right now, because 348 00:20:50,920 --> 00:20:53,359 Speaker 1: you know that's that's a place that we could really 349 00:20:53,920 --> 00:20:57,400 Speaker 1: potentially do some good. And I hope on future seasons 350 00:20:57,400 --> 00:20:59,760 Speaker 1: of the show we can do just that. You know. 351 00:20:59,800 --> 00:21:03,280 Speaker 1: An if you're enjoying the show and you're interested in 352 00:21:03,359 --> 00:21:06,879 Speaker 1: there being more seasons of Algorithm that explore other cities, 353 00:21:07,000 --> 00:21:09,399 Speaker 1: you know, please tell a friend about the show and 354 00:21:09,640 --> 00:21:12,159 Speaker 1: leave a review on Apple Podcasts. I know I'm always 355 00:21:12,240 --> 00:21:14,439 Speaker 1: asking you guys to do that stuff, and you know 356 00:21:14,480 --> 00:21:17,480 Speaker 1: it can feel like a drop in the bucket, but 357 00:21:17,600 --> 00:21:20,240 Speaker 1: when it comes to these companies making a decision about 358 00:21:20,320 --> 00:21:22,480 Speaker 1: whether or not to make another season of the show, 359 00:21:23,160 --> 00:21:25,960 Speaker 1: h that kind of stuff is really important. Um. And 360 00:21:26,000 --> 00:21:28,800 Speaker 1: also I'm looking for suggestions of cities that you think 361 00:21:28,840 --> 00:21:32,080 Speaker 1: should be investigated. So if you have any ideas, you know, 362 00:21:32,119 --> 00:21:34,720 Speaker 1: if you think there's something weird going on where you live, 363 00:21:35,119 --> 00:21:37,840 Speaker 1: or you've heard crazy stories about cities that might have 364 00:21:38,119 --> 00:21:41,240 Speaker 1: active serial killers right now, please let me know and 365 00:21:41,640 --> 00:21:44,560 Speaker 1: we can see what the algorithm says and look into it. 366 00:21:46,320 --> 00:21:48,960 Speaker 1: I really do appreciate all of you who listened and 367 00:21:49,320 --> 00:21:52,359 Speaker 1: left voicemails or reached out on Twitter. UM, if you 368 00:21:52,440 --> 00:21:55,400 Speaker 1: haven't yet, please do. I'm sure we'll have another episode 369 00:21:55,400 --> 00:21:58,440 Speaker 1: like this soon. So you can leave a voicemail at 370 00:21:58,480 --> 00:22:06,480 Speaker 1: eight five zero one zo nine. That's five zero one nine. UM. 371 00:22:06,560 --> 00:22:09,760 Speaker 1: You can message me on Twitter at b N underscore 372 00:22:09,960 --> 00:22:14,560 Speaker 1: KU E B R I c H. That's been underscore Keybrick. 373 00:22:15,320 --> 00:22:18,399 Speaker 1: So we'll be back very soon with some episodes that 374 00:22:18,400 --> 00:22:20,520 Speaker 1: are looking into some of the cold cases that the 375 00:22:20,560 --> 00:22:25,600 Speaker 1: Algorithm identified and looking into Vaughan's possible connection to those murders. 376 00:22:27,800 --> 00:22:30,880 Speaker 1: This episode was written and produced by me ben Key Brick. 377 00:22:31,320 --> 00:22:35,320 Speaker 1: Algorithm is executive produced by Alex Williams, Donald Albright, and 378 00:22:35,359 --> 00:22:40,679 Speaker 1: Matt Frederick. Production assistance in mixing by Eric Quintana. The 379 00:22:40,760 --> 00:22:44,159 Speaker 1: music is by Makeup and Vanity Set in Blue Dot Sessions. 380 00:22:44,720 --> 00:22:49,480 Speaker 1: Thanks to Christina Dana, Miranda Hawkins, Jamie Albright, Rema l 381 00:22:49,600 --> 00:22:53,080 Speaker 1: k Ali, Trevor Young, and Josh Thane for their help 382 00:22:53,160 --> 00:22:53,679 Speaker 1: and notes.