They call her the Delco Dog Detective. She’s spent over a decade finding the Philly area’s hardest to catch lost dogs 11%

By Dana Munro0%

7/12/2026, 2:01:00 AM

BS Summary: This article contains 32 faulty reasoning types, including Halo Effect, Anecdotal, and Hasty Generalization, with Appeal to Emotion as the most egregious example at 13% saturation with 274 hits. Analysis detected 2,609 faulty-reasoning hits from 2,102 analyzed words, generating a BS Score of 27.4% and a BS Rank of 11% (19,641 of 21,887 articles). This article is better (less manipulative) than 89.70% of the article peer group.

Colleen Bell picks up a dog trap to stow in her van after setting up a different trap by the Woodford Mansion in Fairmount Park. 
Bell, her brother, and a friend run the nonprofit GoodBoy Dog Recovery. 
Read more Allie Ippolito / For The Inquirer 
Published July 12, 2026, 5:01 a.m. 
ET 
His eyes were smeared with black goo, his legs were caked in dirt, his back was riddled with ticks, and a chunk of fur was missing from his upper lip. 
And yet, Oreo, a 4-year-old shih tzu, blissfully bounded up the front porch steps of his Mantua home on a Sunday morning in April. 
He wedged his tiny frame between the door and the molding, a crumpled purple leash trailing from his neck, waiting until the knob turned and 22-year-old Jayda Pizarro appeared in a black hoodie and yoga pants. 
Her braces sparkled as she beamed widely and greeted her pup, who’d been lost in the woods for the last 15 days. 
Tears dripped from Pizarro’s eyes onto the tile foyer floor as she nestled Oreo between her cheek and shoulder. 
“Thank you,” she sniffed out as she cradled his body and rocked him in her arms. 
“We’ve been crying every day.” 
It was all in a morning’s work for 53-year-old Colleen Bell, who estimates she’s recovered nearly 1,000 dogs, both strays and family pets, over the last roughly 14 years. 
Bell doesn’t do this work for money, acclaim, or internet fame. 
She does it purely for a love of the game, the thrill of the hunt, the satisfaction of capturing a petrified pup, and the fulfillment of reuniting it with a frenzied family. 
Over the last several years, she’s become a trusted and critical resource for the Animal Care & Control Team of Philadelphia and many others in the region and across the globe. 
She’s also become a fearless dog rescuer, who’s traversed mountains, driven through blizzards, sifted through building rubble, and charged through rainstorms with soccer nets to retrieve retrievers, shepherd shepherds, and master mastiffs. 
Puppy love for a new pursuit in life 
Bell spent much of her Lower Chichester upbringing tearing through Nancy Drew books as a steady procession of stray dogs came and went from her Delaware County home. 
However, this specific fusion of her love of dogs and solving mysteries didn’t set in until decades later. 
It started about 14 years ago with a black pug named Lilly. 
When her neighbor with dementia accidentally let Lilly out the front door, Bell, a single mother with two adult daughters and some time on her hands, volunteered to help. 
She and others weren’t able to find the dog, but the experience, Bell said, “kind of sparked something in me.” 
When another opportunity arose to reunite a lost dog with its family, she was determined to do so. 
So she contacted Justice Rescue, a Pennsylvania-based animal welfare group, which had special traps designed to catch animals. 
The dog was caught within five minutes. 
From then on, “I was kind of hooked,” she said. 
Slowly, she started collecting her own equipment  some traps, some cameras, and some dog food. 
With a little outside advice, a whole lot of dogged persistence, and through good old-fashioned trial and error, she refined her approach. 
She learned what entices fearful dogs and what scares them off. 
She studied their body language and behaviors and miraculously kept all her fingers and toes in the process. 
She filled her free evenings after her day job as a supervisor at a health insurance company with stakeouts. 
Most people in her life didn’t know she was doing any of this until one fateful midnight about two years ago. 
Tanwyn the golden retriever was missing in Chester County ’s White Clay Creek Preserve. 
Bell tried every tactic in her arsenal. 
She even purchased a stuffed golden retriever toy that breathed and cried in hopes it would spark Tanwyn’s maternal instincts. 
She set up traps and slept in her car overnight in case the dog appeared. 
But nothing worked. 
Bell needed to get closer. 
She procured a duck blind and positioned it near the trap so she could hide behind it and grab the leash if the dog came close enough. 
Around 12:30 a.m., as she sat alone in the preserve, fear set in. 
“What the hell am I doing?” 
she thought to herself. 
“I’m scared to death. 
Nobody knows I’m here.” 
But she couldn’t leave. 
It wasn’t in her nature. 
It could be the night the dog came. 
So she called her brother, Brian. 
Of her three older brothers, Brian had always been the “dependable” one, she said. 
“What are you doing?” 
she asked him. 
“I’m sleeping,“ he replied. 
”What’s going on?” 
Within the hour, he was there. 
And, for the most part, he hasn’t left her side since. 
“I’m an older brother,” said Brian Bell, 55. 
“It’s my job to look out for her.” 
Previously, the two had gone years without speaking. 
They were raised in an abusive household, Colleen said, which led her to distance herself from her siblings. 
“I just couldn’t deal with talking to anybody” from that part of my life, she said. 
The dog recovery work reunited all four of them, Colleen said, but especially her and Brian. 
“He’s become one of my best friends,” she said. 
“We’re like inseparable,” Brian added. 
Soon their friend Alan Boates, 45, got involved as well. 
All three, who live minutes from one another in Chichester, adore dogs and the rush of capturing one on the run fending for its life. 
Quickly, Brian Bell and Alan Boates understood their assignment as the muscle of the operation. 
Colleen Bell was the brains, and neither hell (an unstable building or unscalable mountain) nor high water (frozen creeks and torrential downpours) were going to stand in her way. 
“There’s nothing that she won’t do to save a dog,” Boates said. 
“If she got to sacrifice us for a dog, it’s the dog.” 
Boates, who owns a construction company, leveled up their equipment. 
They built traps operated by remote control and others activated by laser beams. 
They upgraded items as they found more advanced versions online. 
They swapped DIY drop nets for net launchers that hang between trees and shoot nets at the ground. 
At one point, Colleen Bell came across literature about thermal drones and how they create digital maps that can help locate animals in dense woods so she bought one for $8,000 and roped a retired state trooper into teaching her to fly it. 
It shattered on the asphalt due to a faulty battery. 
Luckily, she’d already purchased a more sophisticated one for $18,000. 
While she worked alone, Colleen paid roughly $15,000 to $20,000 a year out of pocket. 
She even took a second job at a nursing home to cover the costs. 
But once her brother and Boates came on board, the operation became more advanced, and the equipment too expensive to afford on her own. 
So, about two years ago she created a 501(c)3, GoodBoy Dog Recovery. 
Donations rolled in for fuel, food, traps, cameras, and more. 
Last year she raised about $134,000, according to a 990 tax form. 
Much of the money came from her 75,000 Facebook followers. 
Never letting sleeping dogs lie ... alone in the woods 
Now, dog recovery is effectively Colleen’s No. 2 full-time job and No. 1 passion. 
Her days typically start with work at AmeriHealth Caritas at 7:30 a.m. 
She clocks out at 4 p.m., then texts her brother an eyeballs emoji to see if he’s free and up for an adventure. 
She’ll often reach out to Boates as well. 
On a recent Tuesday in June, she picked up Brian in a donated conversion van and drove to the Woodford Mansion near the Strawberry Mansion neighborhood. 
They often sleep in the van parked near a trap in the woods. 
That’s because sometimes the dogs enter the traps in the wee morning hours and if the traps were unattended, the dogs might get confused and scared. 
On this night they pulled into a driveway at around 7:15 p.m. and replenished the kibble in the trap they’d set weeks ago for Parker, a stray pit bull they were pursuing. 
Colleen Bell sprayed the area with a barbecue-scented liquid smoke, which she buys by the gallon. 
Some of these dogs, especially if they’ve been in the wild a long time, survive primarily on wild game. 
So the crew keeps deer meat and chicken feet on hand. 
They even have a moose dealer. 
After they prepared the trap area, the siblings sat in the van and waited. 
They scrolled their phones, sipped juice, and nearly popped out of their seats when a jingle emanated from Colleen’s phone, which told them motion was detected in the trap. 
“Be a dog. 
Please be a dog,” she muttered to herself. 
“Oh it’s a raccoon. 
Oh it’s two raccoons.” 
About half an hour later the phone tinkled again. 
She rushed to press the alert. 
“Damn,” she said. 
“It’s a fox.” 
An hour later, another alert sounded. 
This time, it was Brian Bell who shot out of his seat. 
“I got a feeling,” he said with a cockeyed smile. 
“Fox,” she responded, deflated, before pressing a button on her phone that allowed her to transmit an audio message into the trap. 
“Get out of there,” she warned the fox before its white frame scurried out of view. 
Many nights, she only sleeps a few hours. 
“We never stop until we catch the dog,” she said. 
When normal methods cease to work, Colleen Bell will throw in a number of toys of her own invention. 
Her tactical lamb chop, as she calls it, a stuffed Lamb Chop toy into which she sews a GPS tracker, can be especially effective in locating dogs. 
For Brian Bell, the most satisfying part is watching his sister’s face when they close in on a canine. 
“Once she sees it, she’s locked in,” he said. 
“To her, it’s like each dog that we catch is like the lottery.” 
When they do finally catch them, sometimes the dogs are in disturbingly poor condition, Boates said. 
One they caught recently “looked like it has 10 legs because it was so matted,” he said. 
Another had cigarette burns all over its head. 
Some dogs are dead when they find them. 
The trio will retrieve the body for the family to deal with as it sees fit. 
For the dogs who are alive, once they’re securely in the trap or net, Colleen Bell puts them in the back of her van. 
They’re usually asleep within minutes. 
“They’re just so tired of running,” Boates said. 
“I don’t know what it is, with Colleen, it’s like they know she’s there to help them.” 
For the professionals, such as those in charge of the Animal Care & Control Team of Philadelphia, Colleen Bell is a critical resource, said Sarah Barnett, executive director of ACCT Philly. 
Barnett’s team gets thousands of calls a month, many about lost dogs. 
Locating them all would be impossible for ACCT to do alone. 
Not only does Colleen Bell search for dogs in Philly and the suburbs, relieving some of the load from ACCT, she also lends her equipment to individuals with missing pets. 
Philadelphia is relatively privileged in terms of animal resources when compared with surrounding areas, Barnett said. 
Many Pennsylvania counties, such as Chester and Delaware, have spottier animal care and control coverage. 
In those counties, sometimes, “the dogs just stay wandering,” Barnett said. 
On the sunny April morning Oreo returned to his Mantua home, he quickly settled back into the loving and chaotic environment he’d wandered off from weeks before. 
“Come see Oreo, Meatball,” Jayda Pizarro shouted up the stairs to her 3-year-old cousin who all in the home refer to by the name of the spaghetti topper. 
A toddler in dinosaur pajamas and one soccer-printed sock and one striped sock came ambling down the staircase. 
As Meatball delightedly watched Oreo wriggle on the floor, a cat emerged from the living room to judgmentally gaze upon the scene. 
All seemed as it was meant to be. 
“I appreciate you,” Pizarro said with a smile for Colleen Bell before the dog detective climbed back into her van to continue her perennial search for dogs in distress. 
I cover breaking news and write general assignment stories. 
I'm interested in Philly's unique culture, fascinating characters and communities, and challenges faced by workers in the 21st century. 
Article reasoning-pattern comparisonThis article: 1.1%Dana Munro: 0.7%Inquirer.com: 2.1%Confirmation Bias1.1%This article: 0.0%Dana Munro: 1.0%Inquirer.com: 1.0%Anchoring Bias0.0%This article: 4.8%Dana Munro: 0.2%Inquirer.com: 2.5%Availability Heuristic4.8%This article: 4.0%Dana Munro: 0.0%Inquirer.com: 0.7%Representativeness Heuristic4.0%This article: 1.7%Dana Munro: 0.4%Inquirer.com: 0.8%Hindsight Bias1.7%This article: 6.2%Dana Munro: 0.0%Inquirer.com: 0.9%Overconfidence Bias6.2%This article: 5.2%Dana Munro: 0.6%Inquirer.com: 5.0%Framing Effect5.2%This article: 1.4%Dana Munro: 0.0%Inquirer.com: 0.5%Loss Aversion1.4%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.4%Status Quo Bias0.0%This article: 4.0%Dana Munro: 0.0%Inquirer.com: 0.3%Sunk Cost Effect4.0%This article: 8.0%Dana Munro: 0.4%Inquirer.com: 2.7%Optimism Bias8.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.8%Pessimism Bias0.0%This article: 7.5%Dana Munro: 2.5%Inquirer.com: 5.7%Negativity Bias7.5%This article: 2.6%Dana Munro: 0.3%Inquirer.com: 1.4%Self-Serving Bias2.6%This article: 2.9%Dana Munro: 0.0%Inquirer.com: 0.7%Fundamental Attribution Error2.9%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.2%Actor-Observer Bias0.0%This article: 0.2%Dana Munro: 0.0%Inquirer.com: 1.2%In-Group Bias0.2%This article: 0.5%Dana Munro: 0.0%Inquirer.com: 0.2%Out-Group Homogeneity Bias0.5%This article: 10.8%Dana Munro: 21.7%Inquirer.com: 4.4%Halo Effect10.8%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%Horn Effect0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%Dunning-Kruger Effect0.0%This article: 1.6%Dana Munro: 0.0%Inquirer.com: 1.4%Recency Bias1.6%This article: 0.3%Dana Munro: 0.0%Inquirer.com: 0.3%Primacy Effect0.3%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Blind-Spot Bias0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.6%Ad Hominem0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Straw Man0.0%This article: 6.3%Dana Munro: 0.7%Inquirer.com: 2.3%Appeal to Authority6.3%This article: 4.4%Dana Munro: 0.0%Inquirer.com: 1.1%False Dilemma4.4%This article: 1.2%Dana Munro: 0.0%Inquirer.com: 0.2%Slippery Slope1.2%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Circular Reasoning0.0%This article: 8.4%Dana Munro: 0.5%Inquirer.com: 3.3%Hasty Generalization8.4%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Red Herring0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.5%Bandwagon0.0%This article: 13.0%Dana Munro: 0.5%Inquirer.com: 3.1%Appeal to Emotion13.0%This article: 1.8%Dana Munro: 0.0%Inquirer.com: 0.7%Begging the Question1.8%This article: 6.1%Dana Munro: 0.0%Inquirer.com: 1.8%Post Hoc (False Cause)6.1%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%Tu Quoque0.0%This article: 0.2%Dana Munro: 0.0%Inquirer.com: 0.3%Burden of Proof0.2%This article: 1.0%Dana Munro: 0.0%Inquirer.com: 0.1%Appeal to Nature1.0%This article: 0.5%Dana Munro: 0.0%Inquirer.com: 0.1%Composition/Division0.5%This article: 10.2%Dana Munro: 0.4%Inquirer.com: 2.1%Anecdotal10.2%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%No True Scotsman0.0%This article: 2.2%Dana Munro: 0.0%Inquirer.com: 1.3%Ambiguity (Equivocation)2.2%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.2%Middle Ground0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.0%Personal Incredulity0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Special Pleading0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.1%Genetic Fallacy0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 1.3%Unattributed Quote0.0%This article: 0.7%Dana Munro: 0.0%Inquirer.com: 0.8%Quote-first Misdirection0.7%This article: 4.9%Dana Munro: 0.0%Inquirer.com: 4.4%Biased Writer Voice4.9%This article: 0.4%Dana Munro: 0.0%Inquirer.com: 1.5%Indoctrination0.4%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Dana Munro: 0.0%Inquirer.com: 1.5%Attempt to Sell a Product or S…0.0%

2102 words analyzed.

Speakers

5speakers24%attributed speech1,598writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 7 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageJayda Pizarro • 16 words • 0.0% coverageJayda Pizarro • 5 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageBrian Bell • 4 words • 0.0% coverageBrian Bell • 3 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageBrian Bell • 6 words • 0.0% coverageBrian Bell • 11 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageColleen Bell • 8 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageColleen Bell • 16 words • 0.0% coverageBrian Bell • 16 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageAlan Boates • 15 words • 0.0% coverageAlan Boates • 29 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 43 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 14 words • 100.0% coverageColleen Bell • 29 words • 0.0% coverageColleen Bell • 3 words • 0.0% coverageColleen Bell • 8 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageColleen Bell • 9 words • 0.0% coverageColleen Bell • 6 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageBrian Bell • 6 words • 0.0% coverageColleen Bell • 12 words • 0.0% coverageColleen Bell • 10 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageColleen Bell • 16 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageBrian Bell • 27 words • 0.0% coverageBrian Bell • 19 words • 0.0% coverageAlan Boates • 9 words • 0.0% coverageAlan Boates • 13 words • 0.0% coverageAlan Boates • 16 words • 0.0% coverageAlan Boates • 17 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageAlan Boates • 24 words • 0.0% coverageAlan Boates • 5 words • 0.0% coverageSarah Barnett • 8 words • 0.0% coverageSarah Barnett • 17 words • 0.0% coverageSarah Barnett • 31 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageSarah Barnett • 11 words • 0.0% coverageSarah Barnett • 30 words • 0.0% coverageSarah Barnett • 16 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageJayda Pizarro • 11 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageJayda Pizarro • 22 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverage
Selected voice

Alan Boates

100%flagged-word coverage
128 attributed words25% of attributed speech66% writer coverage
0%5.0%10.0%Biased Writer Voice-6.4 ptsWriter: 6.4%Alan Boates: 0.0%0.0%Quote-first Misdirection-0.9 ptsWriter: 0.9%Alan Boates: 0.0%0.0%Indoctrination-0.5 ptsWriter: 0.5%Alan Boates: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Loading…
Loading…
Loading…
Loading…

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.