Kansas City's next big opera star is 12 years old, 2 feet tall and covered in hair (it's a dog) 89%

By Julie Denesha25%

5/1/2026, 9:00:00 AM

BS Summary: This article contains 26 faulty reasoning types, including Attempt to Sell a Product or Service, Halo Effect, and Framing Effect, with Appeal to Authority as the most egregious example at 15.3% saturation with 150 hits. Analysis detected 1,071 faulty-reasoning hits from 982 analyzed words, generating a BS Score of 82.3% and a BS Rank of 89% (2,412 of 21,887 articles). This article is worse (more manipulative) than 89.00% of the article peer group.

When Scot Pondelick found out a Kansas City opera was looking for four-legged actors for a new production, he knew he had to take his 7-year-old service dog to try out. 
“Oh, Venus would be perfect for this role,” he says, on one of two nights of auditions at the Lyric Opera of Kansas City’s production building in the Crossroads. 
“Cause she is extremely slow and lazy.” 
Pondelick, an Army veteran, says Venus trains once a week with the group Dogs 4 Valor, which provides trained service dogs to help veterans and first responders manage anxiety and depression. 
“She got bit by a spider like last year,” Pondelick says. 
“She refused to walk. 
I had to carry her in and out of the vet. 
Oh man, my back was killing me.” 
Last month, eight dogs and their owners showed up to audition for a special role in the opera’s rendition of “Of Mice and Men,” which opens Friday. 
Each audition took just 10 minutes. 
During Venus’ shot at the spotlight, she was led to a bed where she had to sit quietly while resident artist Alex Smith performed an aria from the Italian opera “I Puritani.” 
Director of Production Tracy Davis-Singh, a dog owner herself, says it’s important to see how each one responds to the environment around them  the bright lights, the action onstage and the sound of the singers. 
“We need to make sure that we have a dog, first of all, that doesn't want to sing with the singers,” Davis-Singh says, “but also, can either sit or stay or stand there comfortably for 10 minutes.” 
Led by Davis-Singh, Venus lumbered through her audition before being sent home with a tasty bag of dog treats. 
‘No labradoodles, no fancy, designer dogs’ 
The Lyric Opera has previously featured dogs in several productions, mostly in walk-on roles. 
This time around, the dogs are competing for the part of Old Dog. 
(A smaller role in the opera went to a 6-week-old Saint Bernard and Bernese mountain dog puppy named Waverly, from Wayside Waifs, who did not audition.) 
The opera is based on John Steinbeck's 1937 novella, "Of Mice and Men." 
It follows two migrant workers through California during the Great Depression. 
Because the opera’s aesthetic draws from Dust Bowl-era photography, when Stage Director Kristine McIntyre was considering a dog for the production, she had a specific look in mind. 
“We have to believe that it's a dog that could possibly have lived on a ranch in the mid 20th century,” McIntyre says. 
“So no doodles, no labradoodles, no fancy designer dogs.” 
For McIntyre, who directed the production at Houston Grand Opera before bringing it to Kansas City, the authentic appearance was important because the role is not just window-dressing. 
“The dog is very much a symbol of the inhumanity of the people who work on this ranch, and society in general,” she says. 
“How we don't value age. 
We don't value experience in others, be they animals or humans.” 
Davis-Singh says this is the first time a dog has had a chance for some real stage time in a Lyric Opera production. 
“What I appreciate in this story and doing these dog auditions is that the dog is essential to the story,” Davis-Singh says. 
“It's not just a convention.” 
A tragic conclusion 
Lynn Gowler brought her tawny, 9-year-old English Labrador to audition for the critical role after her husband saw the announcement on Instagram and thought the dog should try out. 
“I've been a high school English teacher for 33 years, and it is my favorite story to teach with students,” Gowler says. 
“And I answered him back and I said, ‘You know, that dog gets shot, don't you?’” 
Gowler says her husband was shocked. 
“Well, of course they won't do that, but that's what happens in the book,” she remembers telling him. 
Gowler says she adopted Bear from KC Pet Project. 
He loves attention and knows a trick or two, she says. 
Before he headed out onto the audition stage, Bear lay down to ask for a nice, long belly rub. 
“Bear will give you a paw for a shake, and he absolutely loves playing with children,” Gowler says. 
“All the children in our neighborhood get very excited when they see Bear outside.” 
Cue the orchestra, costumes and stage lighting 
One month later, Elena Owens waits in the wings of the rehearsal hall with 12-year-old Charlee Blue. 
Turns out this salt-and-pepper blue heeler has the right look and the right training for the part  an important part of which is staying mostly silent. 
“My daughter is an acting major at the University of Minnesota and has been in shows like ‘A Christmas Carol’ several times,” says Owens. 
“I'm kind of used to the stage mom gig.” 
Charlee started out with the Owens family as a foster dog, and he’s been with them for more than a decade, she says. 
“We're theater people, but not as much opera. 
I think it's been fascinating to hear them singing and how amazing their voices are,” Owens says. 
“What a gift, you know?” 
Davis-Singh says rehearsals like these will help Charlee get comfortable before his big debut. 
“You practice it in the rehearsal hall, then you practice it on stage,” Davis-Singh says. 
“You add costumes, you add lighting, then you add orchestra.” 
Opening night, of course, is something that can’t be rehearsed, Davis-Singh says. 
So, when Charlee Blue steps into the spotlight for real, it will be his very first time in front of an audience. 
Lyric Opera of Kansas City's “Of Mice and Men” runs May 1-3 at the Kauffman Center for the Performing Arts, 1601 Broadway Blvd., Kansas City, Missouri 64108. 
For more information visit the Lyric Opera of Kansas City website. 
Article reasoning-pattern comparisonThis article: 5.2%Julie Denesha: 1.4%Columbia Missourian: 1.7%Confirmation Bias5.2%This article: 4.6%Julie Denesha: 1.2%Columbia Missourian: 0.8%Anchoring Bias4.6%This article: 4.9%Julie Denesha: 2.3%Columbia Missourian: 2.7%Availability Heuristic4.9%This article: 5.1%Julie Denesha: 1.4%Columbia Missourian: 0.9%Representativeness Heuristic5.1%This article: 0.0%Julie Denesha: 0.4%Columbia Missourian: 0.4%Hindsight Bias0.0%This article: 1.4%Julie Denesha: 2.1%Columbia Missourian: 1.3%Overconfidence Bias1.4%This article: 6.0%Julie Denesha: 2.0%Columbia Missourian: 5.5%Framing Effect6.0%This article: 0.0%Julie Denesha: 0.4%Columbia Missourian: 1.0%Loss Aversion0.0%This article: 0.0%Julie Denesha: 0.3%Columbia Missourian: 0.7%Status Quo Bias0.0%This article: 0.0%Julie Denesha: 0.8%Columbia Missourian: 0.3%Sunk Cost Effect0.0%This article: 4.5%Julie Denesha: 6.2%Columbia Missourian: 4.5%Optimism Bias4.5%This article: 0.0%Julie Denesha: 0.3%Columbia Missourian: 1.6%Pessimism Bias0.0%This article: 4.0%Julie Denesha: 0.9%Columbia Missourian: 5.4%Negativity Bias4.0%This article: 2.2%Julie Denesha: 1.8%Columbia Missourian: 1.7%Self-Serving Bias2.2%This article: 3.7%Julie Denesha: 0.7%Columbia Missourian: 0.6%Fundamental Attribution Error3.7%This article: 0.0%Julie Denesha: 0.2%Columbia Missourian: 0.2%Actor-Observer Bias0.0%This article: 0.0%Julie Denesha: 0.9%Columbia Missourian: 1.6%In-Group Bias0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.4%Out-Group Homogeneity Bias0.0%This article: 6.6%Julie Denesha: 7.3%Columbia Missourian: 2.4%Halo Effect6.6%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.0%Horn Effect0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.0%Dunning-Kruger Effect0.0%This article: 2.7%Julie Denesha: 1.1%Columbia Missourian: 0.9%Recency Bias2.7%This article: 2.0%Julie Denesha: 0.1%Columbia Missourian: 0.3%Primacy Effect2.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.0%Blind-Spot Bias0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.5%Ad Hominem0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.2%Straw Man0.0%This article: 15.3%Julie Denesha: 3.3%Columbia Missourian: 2.9%Appeal to Authority15.3%This article: 5.3%Julie Denesha: 0.7%Columbia Missourian: 1.1%False Dilemma5.3%This article: 1.0%Julie Denesha: 0.1%Columbia Missourian: 1.0%Slippery Slope1.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.1%Circular Reasoning0.0%This article: 4.4%Julie Denesha: 3.1%Columbia Missourian: 3.6%Hasty Generalization4.4%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.2%Red Herring0.0%This article: 0.0%Julie Denesha: 0.6%Columbia Missourian: 0.7%Bandwagon0.0%This article: 1.2%Julie Denesha: 4.0%Columbia Missourian: 5.2%Appeal to Emotion1.2%This article: 3.4%Julie Denesha: 0.3%Columbia Missourian: 0.6%Begging the Question3.4%This article: 0.0%Julie Denesha: 2.3%Columbia Missourian: 2.0%Post Hoc (False Cause)0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.0%Tu Quoque0.0%This article: 0.0%Julie Denesha: 0.2%Columbia Missourian: 0.4%Burden of Proof0.0%This article: 2.3%Julie Denesha: 0.2%Columbia Missourian: 0.2%Appeal to Nature2.3%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.2%Composition/Division0.0%This article: 5.8%Julie Denesha: 3.2%Columbia Missourian: 2.6%Anecdotal5.8%This article: 0.0%Julie Denesha: 0.1%Columbia Missourian: 0.1%No True Scotsman0.0%This article: 3.9%Julie Denesha: 1.7%Columbia Missourian: 1.3%Ambiguity (Equivocation)3.9%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.1%Middle Ground0.0%This article: 0.0%Julie Denesha: 0.2%Columbia Missourian: 0.1%Personal Incredulity0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.1%Special Pleading0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.1%Genetic Fallacy0.0%This article: 3.0%Julie Denesha: 0.5%Columbia Missourian: 0.8%Unattributed Quote3.0%This article: 2.0%Julie Denesha: 0.6%Columbia Missourian: 0.7%Quote-first Misdirection2.0%This article: 0.6%Julie Denesha: 0.8%Columbia Missourian: 2.9%Biased Writer Voice0.6%This article: 0.0%Julie Denesha: 1.3%Columbia Missourian: 1.3%Indoctrination0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.7%Politically Left Leaning Bias0.0%This article: 0.0%Julie Denesha: 0.0%Columbia Missourian: 0.2%Politically Right Leaning Bias0.0%This article: 7.9%Julie Denesha: 1.3%Columbia Missourian: 1.7%Attempt to Sell a Product or S…7.9%

982 words analyzed.

Speakers

5speakers58%attributed speech414writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 20 words • 100.0% coverageWriter's voice • 31 words • 0.0% coverageScot Pondelick • 29 words • 100.0% coverageScot Pondelick • 7 words • 0.0% coverageScot Pondelick • 31 words • 100.0% coverageScot Pondelick • 11 words • 0.0% coverageScot Pondelick • 4 words • 0.0% coverageScot Pondelick • 11 words • 0.0% coverageScot Pondelick • 7 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageTracy Davis-Singh • 36 words • 0.0% coverageTracy Davis-Singh • 37 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageKristine McIntyre • 23 words • 0.0% coverageKristine McIntyre • 9 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageKristine McIntyre • 24 words • 0.0% coverageKristine McIntyre • 5 words • 0.0% coverageKristine McIntyre • 11 words • 0.0% coverageTracy Davis-Singh • 23 words • 0.0% coverageTracy Davis-Singh • 22 words • 0.0% coverageTracy Davis-Singh • 5 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageLynn Gowler • 22 words • 0.0% coverageLynn Gowler • 16 words • 0.0% coverageLynn Gowler • 6 words • 0.0% coverageLynn Gowler • 18 words • 0.0% coverageLynn Gowler • 9 words • 100.0% coverageLynn Gowler • 11 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageLynn Gowler • 18 words • 0.0% coverageLynn Gowler • 14 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageElena Owens • 24 words • 0.0% coverageElena Owens • 9 words • 0.0% coverageElena Owens • 23 words • 0.0% coverageElena Owens • 8 words • 0.0% coverageElena Owens • 17 words • 0.0% coverageElena Owens • 5 words • 0.0% coverageTracy Davis-Singh • 14 words • 0.0% coverageTracy Davis-Singh • 15 words • 0.0% coverageTracy Davis-Singh • 10 words • 0.0% coverageTracy Davis-Singh • 12 words • 0.0% coverageTracy Davis-Singh • 22 words • 0.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverage
Selected voice

Scot Pondelick

96%flagged-word coverage
100 attributed words18% of attributed speech67% writer coverage
0%17.5%35.0%Attempt to Sell a Product +21.8 ptsWriter: 9.2%Scot Pondelick: 31.0%31.0%Unattributed Quote+29.0 ptsWriter: 0.0%Scot Pondelick: 29.0%29.0%Quote-first Misdirection-4.8 ptsWriter: 4.8%Scot Pondelick: 0.0%0.0%Biased Writer Voice-1.4 ptsWriter: 1.4%Scot Pondelick: 0.0%0.0%

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

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Analysis

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