CT Mirror19%

PODCAST: In New Haven, a push to identify unmarked graves 24%

By CT Mirror's Long Story Short0%

7/20/2026, 7:40:14 PM

BS Summary: This article contains 22 faulty reasoning types, including Halo Effect, Unattributed Quote, and Availability Heuristic, with Negativity Bias as the most egregious example at 11.3% saturation with 129 hits. Analysis detected 1,274 faulty-reasoning hits from 1,139 analyzed words, generating a BS Score of 36.9% and a BS Rank of 24% (16,117 of 21,171 articles). This article is better (less manipulative) than 76.10% of the article peer group.

Hundreds of headstones in a New Haven cemetery are marked only with numbers. 
Some community members are working to change that. 
WSHU’s Ebong Udoma spoke with CT Mirror’s John Moritz to discuss his article, “The story behind rows of unmarked stones at New Haven cemetery,” as part of the collaborative podcast Long Story Short. 
Read John’s story here . 
WSHU: Hi John, this is a fascinating story, but it’s not your beat. 
So how did you come across it? 
JM: So I came across this story because I was living in New Haven, and some people from the city and local community who I’ve been following had been posting some blogs that this woman Sherill Baldwin had been writing on her website, where she’d been detailing the lives and the history of some of the people that she’d been discovering who were buried at Blake Street Cemetery, which is an old pauper cemetery, a place where the city buried poor and indigent people between 1884 and 1931. 
And I was really interested, and so I contacted Sherill to learn more about her work. 
WSHU: And you got to visit with her a couple of times at the cemetery. 
Tell us a little bit about this. 
Describe the cemetery to us first. 
JM: So, for people who may be more familiar with Westville Cemetery in New Haven, it’s a fairly large cemetery off Whaley Avenue. 
There’s also a connecting cemetery owned by the Congregation Mishkan Israel. 
So it’s a it’s a big piece of property. 
There’s actually several cemeteries located on the property. 
But around the back on a side street called Blake Street, there’s a small hill, and on that hill are about 2,200 gravestones, and they’re small, you know, little, you know, bland stones that are just, for the most part, inscribed with numbers, because the people who were buried there didn’t even have their names etched onto the stones. 
The city kept records of who was buried there, but only inscribed the stones with the numbers that corresponded with a name that was on a piece of paper. 
WSHU: So let’s start with Sherill. 
Who is she, and give us a little bit of her background. 
JM: So Sherill is an amateur genealogist from the city of New Haven. 
She told me she’s been interested in genealogy her whole life, from when she grew up on Cape Cod, she would, you know, research old family family plots and cemeteries around there. 
She later went out and she got a research certificate from Boston University. 
But for her, genealogy has always been kind of a side project. 
It’s not her, you know, full time career. 
And what she told me was that during the pandemic, during the lockdown, she was unable to make it back home to Massachusetts to continue some of the research on her own family. 
So she started exploring cemeteries, old cemeteries around New Haven where she lives, and that’s how she came upon Blake Street. 
WSHU: Now you tell us the story of one of the gravestones, Anna Horan. 
Could you just tell us about her story and how Sherill was able to get the family to know what had happened to their ancestors? 
JM: Yeah. 
So this is one of the stories that really got me interested in Sherill’s work. 
Anna Horan was a child of Irish immigrants who was born, I believe, in the 1880s in New Haven, and she lived a fairly rough life. 
Her father died in her infancy, and her mother died just before she was married, I believe, in her early 20s, and she was married to a man named Thomas Horan, who came from New York City. 
And by all accounts, you know the family never had much money, but settled in New Haven, had a child. 
At one point, Thomas left the family in such circumstances that it actually made the local newspapers that reported him as a cruel husband for leaving his family, taking all the furniture and essentially leaving them out in the streets. 
Based on the records Sherill was able to find, at some point the couple got back together, had a few more children. 
He left again. 
Anna Horan ended up in the Springside Almshouse, which was the place for poor people who essentially couldn’t afford to care for themselves back in the day, and her children were sent to the orphanage. 
WSHU: And something else that’s really fascinating is that the oldest person buried there was actually one of the last people sold on the New Haven Green as a slave. 
JM: Yes, so you’re referring to the story of Lois Tritton. 
She was buried in the cemetery in 1894, and she died at the age of 95. 
So that made her the oldest person buried at Blake Street. 
And it was interesting. 
I spoke to another woman named Jill Snyder, who’s been researching a lot of the history of Lois Triton and her mother Lucy. 
Who, as you mentioned, were the last slaves who were sold at auction on the New Haven Green in 1825. 
It wasn’t until 1848 that Connecticut formally abolished the practice of slavery. 
And at the time, you know, what Snyder has found is there seems to be some discrepancy in the record of whether they were actually freed upon sale. 
They were sold to a man who claimed to be an abolitionist, but based on some of Snyder’s research and some newspaper articles at the time, it appears that Lois Tritton actually had to work off as an indentured servant the debts that were incurred to purchase her freedom. 
But she was actually a notable figure later in life in New Haven, there were several newspaper articles, not only in the city of New Haven but around the country, that were written about this woman who, you know, in 1895 was kind of a historical curiosity. 
But when she died, her family didn’t have enough money to pay for a private funeral, so she ended up at Blake Street, and recently, Trinity Church on the Green and St. 
Luke’s actually banded together and were able to purchase a new headstone for her and her family to honor their legacy. 
WSHU: And St. 
Luke’s Episcopal is one of the oldest Black churches in New Haven. 
This is really great stuff. 
Thank you so much, John, for doing this. 
JM: Well, I have to give a shout out to the work of local historians such as Sherill and Jill, who have been really uncovering some of the stories of you know the people, the everyday people who built up the city of New Haven. 
WSHU: Well, thank you so much, John. 
JM: Thank you for having me. 
Article reasoning-pattern comparisonThis article: 6.5%CT Mirror's Long Story Short: 3.6%CT Mirror: 2.7%Confirmation Bias6.5%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.5%Anchoring Bias0.0%This article: 9.5%CT Mirror's Long Story Short: 3.2%CT Mirror: 3.0%Availability Heuristic9.5%This article: 8.3%CT Mirror's Long Story Short: 2.8%CT Mirror: 1.0%Representativeness Heuristic8.3%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.3%Hindsight Bias0.0%This article: 2.5%CT Mirror's Long Story Short: 0.8%CT Mirror: 1.3%Overconfidence Bias2.5%This article: 3.5%CT Mirror's Long Story Short: 1.2%CT Mirror: 5.3%Framing Effect3.5%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.8%Loss Aversion0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.7%Status Quo Bias0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.3%Sunk Cost Effect0.0%This article: 2.5%CT Mirror's Long Story Short: 1.5%CT Mirror: 3.0%Optimism Bias2.5%This article: 2.3%CT Mirror's Long Story Short: 0.8%CT Mirror: 1.9%Pessimism Bias2.3%This article: 11.3%CT Mirror's Long Story Short: 11.4%CT Mirror: 7.0%Negativity Bias11.3%This article: 1.4%CT Mirror's Long Story Short: 0.5%CT Mirror: 2.1%Self-Serving Bias1.4%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.5%Fundamental Attribution Error0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.1%Actor-Observer Bias0.0%This article: 4.0%CT Mirror's Long Story Short: 2.6%CT Mirror: 1.1%In-Group Bias4.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Out-Group Homogeneity Bias0.0%This article: 10.7%CT Mirror's Long Story Short: 5.3%CT Mirror: 1.2%Halo Effect10.7%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.0%Horn Effect0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.0%Dunning-Kruger Effect0.0%This article: 1.8%CT Mirror's Long Story Short: 0.6%CT Mirror: 0.8%Recency Bias1.8%This article: 2.0%CT Mirror's Long Story Short: 2.0%CT Mirror: 0.4%Primacy Effect2.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.0%Blind-Spot Bias0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 1.0%Ad Hominem0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.3%Straw Man0.0%This article: 7.6%CT Mirror's Long Story Short: 2.5%CT Mirror: 3.7%Appeal to Authority7.6%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 1.6%False Dilemma0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.8%Slippery Slope0.0%This article: 1.8%CT Mirror's Long Story Short: 0.6%CT Mirror: 0.1%Circular Reasoning1.8%This article: 4.4%CT Mirror's Long Story Short: 2.9%CT Mirror: 4.0%Hasty Generalization4.4%This article: 2.6%CT Mirror's Long Story Short: 0.9%CT Mirror: 0.2%Red Herring2.6%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.8%Bandwagon0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 5.2%Appeal to Emotion0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.8%Begging the Question0.0%This article: 4.7%CT Mirror's Long Story Short: 1.6%CT Mirror: 2.2%Post Hoc (False Cause)4.7%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Tu Quoque0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.8%Burden of Proof0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.1%Appeal to Nature0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Composition/Division0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 2.6%Anecdotal0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.1%No True Scotsman0.0%This article: 6.5%CT Mirror's Long Story Short: 2.2%CT Mirror: 1.8%Ambiguity (Equivocation)6.5%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.0%Gambler’s Fallacy0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Middle Ground0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.0%Personal Incredulity0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.1%Special Pleading0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Genetic Fallacy0.0%This article: 10.7%CT Mirror's Long Story Short: 3.6%CT Mirror: 0.9%Unattributed Quote10.7%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 1.1%Quote-first Misdirection0.0%This article: 6.9%CT Mirror's Long Story Short: 2.5%CT Mirror: 2.8%Biased Writer Voice6.9%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 2.9%Indoctrination0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%CT Mirror's Long Story Short: 0.0%CT Mirror: 0.2%Politically Right Leaning Bias0.0%This article: 0.4%CT Mirror's Long Story Short: 0.1%CT Mirror: 0.5%Attempt to Sell a Product or S…0.4%

1139 words analyzed.

Speakers

2speakers97%attributed speech36writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWSHU • 33 words • 0.0% coverageWriter's voice • 5 words • 100.0% coverageWSHU • 13 words • 100.0% coverageWSHU • 7 words • 0.0% coverageJohn Moritz • 88 words • 0.0% coverageJohn Moritz • 16 words • 0.0% coverageWSHU • 15 words • 0.0% coverageWSHU • 7 words • 0.0% coverageWSHU • 6 words • 0.0% coverageJohn Moritz • 23 words • 0.0% coverageJohn Moritz • 11 words • 0.0% coverageJohn Moritz • 9 words • 0.0% coverageJohn Moritz • 8 words • 0.0% coverageJohn Moritz • 59 words • 0.0% coverageJohn Moritz • 29 words • 0.0% coverageWSHU • 6 words • 0.0% coverageWSHU • 12 words • 0.0% coverageJohn Moritz • 13 words • 0.0% coverageJohn Moritz • 32 words • 0.0% coverageJohn Moritz • 13 words • 0.0% coverageJohn Moritz • 12 words • 0.0% coverageJohn Moritz • 8 words • 0.0% coverageJohn Moritz • 33 words • 100.0% coverageJohn Moritz • 21 words • 0.0% coverageWSHU • 14 words • 0.0% coverageWSHU • 25 words • 0.0% coverageJohn Moritz • 2 words • 0.0% coverageJohn Moritz • 15 words • 0.0% coverageJohn Moritz • 26 words • 0.0% coverageJohn Moritz • 37 words • 0.0% coverageJohn Moritz • 20 words • 0.0% coverageJohn Moritz • 40 words • 100.0% coverageJohn Moritz • 22 words • 0.0% coverageJohn Moritz • 3 words • 0.0% coverageJohn Moritz • 35 words • 0.0% coverageWSHU • 30 words • 0.0% coverageJohn Moritz • 11 words • 0.0% coverageJohn Moritz • 16 words • 0.0% coverageJohn Moritz • 11 words • 0.0% coverageJohn Moritz • 4 words • 0.0% coverageJohn Moritz • 23 words • 0.0% coverageJohn Moritz • 20 words • 0.0% coverageJohn Moritz • 12 words • 0.0% coverageJohn Moritz • 28 words • 0.0% coverageJohn Moritz • 49 words • 100.0% coverageJohn Moritz • 47 words • 0.0% coverageJohn Moritz • 32 words • 0.0% coverageJohn Moritz • 21 words • 100.0% coverageWSHU • 3 words • 0.0% coverageWSHU • 12 words • 0.0% coverageWSHU • 5 words • 0.0% coverageWSHU • 8 words • 0.0% coverageJohn Moritz • 45 words • 100.0% coverageWSHU • 7 words • 0.0% coverageJohn Moritz • 6 words • 0.0% coverage
Selected voice

John Moritz

74%flagged-word coverage
900 attributed words82% of attributed speech64% writer coverage
0%7.5%15.0%Attempt to Sell a Product -13.9 ptsWriter: 13.9%John Moritz: 0.0%0.0%Unattributed Quote+13.6 ptsWriter: 0.0%John Moritz: 13.6%13.6%Biased Writer Voice+7.3 ptsWriter: 0.0%John Moritz: 7.3%7.3%

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.