How we reported on undercover police operations in California jails 4%

By CalMatters Staff2%

7/14/2026, 12:10:00 PM

BS Summary: This article contains 13 faulty reasoning types, including Confirmation Bias, Appeal to Authority, and Negativity Bias, with Framing Effect as the most egregious example at 9.4% saturation with 47 hits. Analysis detected 301 faulty-reasoning hits from 499 analyzed words, generating a BS Score of 18.6% and a BS Rank of 4% (21,026 of 21,886 articles). This article is better (less manipulative) than 96.10% of the article peer group.

Last fall, CalMatters criminal justice reporter Cayla Mihalovich received a tip about a case pending before the California Supreme Court. 
It involved a man named David Allen, who was arrested on suspicion of murder in 2016 in Los Angeles County when he was 28. 
After his arrest, he invoked his right to silence four times. 
He was then targeted in a so-called  Perkins operation ,” a controversial law enforcement tactic in which a police officer or civilian poses as an incarcerated person to elicit incriminating statements from a suspect. 
Statements Allen made to the Perkins operatives were, according to court records, “the centerpiece of the prosecution’s case” and ultimately helped lead to his conviction. 
He was sentenced to 45 years to life in prison. 
In his petition to the high court, he argued that his Fifth Amendment and federal due process rights were violated and called for his convictions to be reversed. 
As Mihalovich looked into the case, she learned the California Supreme Court had begun accepting more and more petitions like Allen’s. 
The cases involved defendants who claimed undercover agents coerced them into making incriminating statements after they invoked their Miranda rights  or persuaded them to waive those rights before a formal police interrogation. 
To date, the court has accepted at least nine Perkins cases behind Allen’s. 
Mihalovich and her editors wanted to know more about Perkins operations. 
What is involved? 
How do they provide law enforcement with a way around Miranda protections? 
How much money do agencies spend on them? 
How are undercover agents recruited and trained? 
What are the demographics of those who are targeted? 
Finding answers to many of our questions was challenging despite the voluminous court records we reviewed. 
Law enforcement and district attorney offices closely guard information surrounding their highly secretive operations. 
CalMatters filed nearly two dozen formal public records act requests with law enforcement agencies in Los Angeles, Riverside, San Diego, San Bernardino, Orange and Santa Clara counties. 
Almost all were denied. 
After consulting with the First Amendment Coalition, CalMatters asked attorneys from the Covington & Burling law firm to press for more transparency. 
Over the course of several months, their efforts yielded a handful of exclusive law enforcement records. 
As this story publishes, they are continuing to press for more documents and are consulting with CalMatters editors about next steps. 
Today’s story is the most comprehensive look yet at Perkins operations. 
Mihalovich reviewed over 5,000 pages of court records and conducted more than 40 interviews with scholars, public defenders, district attorneys, prosecutors, lawmakers, advocates and incarcerated people. 
“It’s psychological war,” said Michelle Luna Reynoso, a criminal defense attorney in San Diego. 
“How is this not considered cruel and unusual punishment?” 
If you have a tip, email the reporter at cayla@calmatters.org 
This project was completed with the support of a grant from Columbia University’s Ira A. 
Lipman Center for Journalism and Civil and Human Rights in conjunction with Arnold Ventures. 
Article reasoning-pattern comparisonThis article: 6.6%CalMatters Staff: 2.2%CalMatters: 2.5%Confirmation Bias6.6%This article: 2.6%CalMatters Staff: 0.9%CalMatters: 0.9%Anchoring Bias2.6%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 2.4%Availability Heuristic0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.0%Representativeness Heuristic0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.4%Hindsight Bias0.0%This article: 2.2%CalMatters Staff: 1.5%CalMatters: 0.8%Overconfidence Bias2.2%This article: 9.4%CalMatters Staff: 3.7%CalMatters: 5.7%Framing Effect9.4%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.8%Loss Aversion0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.6%Status Quo Bias0.0%This article: 2.8%CalMatters Staff: 0.9%CalMatters: 0.2%Sunk Cost Effect2.8%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.9%Optimism Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 2.0%Pessimism Bias0.0%This article: 6.4%CalMatters Staff: 3.1%CalMatters: 8.0%Negativity Bias6.4%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.6%Self-Serving Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.0%Fundamental Attribution Error0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.2%Actor-Observer Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.8%In-Group Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.3%Out-Group Homogeneity Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.4%Halo Effect0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Horn Effect0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.1%Recency Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.2%Primacy Effect0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.6%Ad Hominem0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.3%Straw Man0.0%This article: 6.6%CalMatters Staff: 2.2%CalMatters: 3.2%Appeal to Authority6.6%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.0%False Dilemma0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.3%Slippery Slope0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.1%Circular Reasoning0.0%This article: 6.0%CalMatters Staff: 2.0%CalMatters: 3.7%Hasty Generalization6.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.1%Red Herring0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.4%Bandwagon0.0%This article: 4.6%CalMatters Staff: 3.1%CalMatters: 5.3%Appeal to Emotion4.6%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.8%Begging the Question0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 2.7%Post Hoc (False Cause)0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Tu Quoque0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.4%Burden of Proof0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.2%Appeal to Nature0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.2%Composition/Division0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.8%Anecdotal0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 5.0%CalMatters Staff: 1.7%CalMatters: 1.2%Ambiguity (Equivocation)5.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.1%Middle Ground0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.0%Personal Incredulity0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.3%Special Pleading0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.1%Genetic Fallacy0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.9%Unattributed Quote0.0%This article: 2.8%CalMatters Staff: 0.9%CalMatters: 0.7%Quote-first Misdirection2.8%This article: 3.2%CalMatters Staff: 1.8%CalMatters: 3.3%Biased Writer Voice3.2%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 1.0%Indoctrination0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.6%Politically Left Leaning Bias0.0%This article: 0.0%CalMatters Staff: 0.0%CalMatters: 0.2%Politically Right Leaning Bias0.0%This article: 2.0%CalMatters Staff: 0.7%CalMatters: 1.0%Attempt to Sell a Product or S…2.0%

499 words analyzed.

Speakers

1speaker4.6%attributed speech476writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageMichelle Luna Reynoso • 14 words • 100.0% coverageMichelle Luna Reynoso • 9 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverage
Selected voice

Michelle Luna Reynoso

100%flagged-word coverage
23 attributed words100% of attributed speech46% writer coverage
0%32.5%65.0%Quote-first Misdirection+60.9 ptsWriter: 0.0%Michelle Luna Reynoso: 60.9%60.9%Biased Writer Voice-3.4 ptsWriter: 3.4%Michelle Luna Reynoso: 0.0%0.0%Attempt to Sell a Product -2.1 ptsWriter: 2.1%Michelle Luna Reynoso: 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.