KQED61%

Northern California Security Officers Campaign for Better Wages, Training 0%

By Ella Jackson68%

4/16/2026, 5:04:19 PM

BS Summary: This article contains 21 faulty reasoning types, including Anecdotal, Appeal to Emotion, and Composition/Division, with Negativity Bias as the most egregious example at 17.9% saturation with 91 hits. Analysis detected 723 faulty-reasoning hits from 507 analyzed words, generating a BS Score of 0% and a BS Rank of 0% (0 of 21,887 articles). This article is better (less manipulative) than 100.00% of the article peer group.

Security officers from across Northern California rallied with labor leaders and officials on Thursday in downtown San Francisco, calling for better pay, improved labor standards and more comprehensive training. 
Security officers represented by the Service Employees International Union are currently fighting to win a new contract, in hopes of securing benefits such as employer-paid health care, retirement and better working conditions. 
“We’re dealing with bad, bad conditions,” said Latasha Reed, a security officer in San Leandro. 
“We have officers that [have] been slashed on their arm where they have to get 23 stitches. 
We have security officers that have been knocked down.” 
The rally also championed proposed state legislation that aims to review pay and set enhanced training standards for private security officers. 
David Huerta, president of the SEIU, said there are over 330,000 private security guards compared to 90,000 badged police officers in California. 
This disparity often places a strain on security guards to act as first responders, despite receiving less training. 
Security officers drum and march behind an SEIU United Service Workers West banner during a rally demanding fair contracts, better pay and improved safety standards on April 16, 2026, at Mechanics Monument Plaza in San Francisco. 
(Gustavo Hernandez/KQED) 
California currently requires licensed guards to receive just 32 hours of training within six months of registration and an additional eight hours of yearly use-of-force and power to arrest training. 
This primarily happens online, without many opportunities for officers to ask questions. 
It’s often insufficient practice for the complex, interpersonal tasks they’re asked to perform every day, said Charles Person, a security officer, union shop steward and member of the bargaining committee for the upcoming contract negotiation. 
By comparison, the San Francisco Police Department requires a 34-week-long Basic Academy training and an additional 40 hours of training every two years. 
The lack of training can have dire consequences. 
In June 2023, a security guard shot and killed 24-year-old Banko Brown after police said he shoplifted $14 worth of merchandise. 
Months after Brown’s death, the state began mandating use-of-force training for security guards. 
Earlier this year, a security officer shot and killed a man in a Tenderloin parking lot. 
“We’re the first line of defense,” Person said. 
“We’re the ones that respond to emergencies  but we’re being treated as if we’re not important.” 
The private security industry predominantly employs Black and brown workers, often for substandard wages, officials said. 
In California, security guards had an annual mean wage of just $21.61 in 2023. 
By comparison, police officers have an annual mean wage of $53.74 in 2023. 
A Fairfield resident, Person said that with gas and toll prices increasing, every time he commutes to his security work in Richmond, he gets “hit in the pocket.” 
“Their wages are well below what it takes to really be able to live and provide for their families, yet they’re protecting multi-billion dollar facilities,” Huerta said ahead of Thursday’s rally. 
“They’re protecting multi-building dollar companies.” 
KQED’s Eliza Peppel contributed to the report. 
Article reasoning-pattern comparisonThis article: 6.9%Ella Jackson: 3.0%CalMatters: 1.9%Confirmation Bias6.9%This article: 7.1%Ella Jackson: 1.4%CalMatters: 0.9%Anchoring Bias7.1%This article: 8.9%Ella Jackson: 5.6%CalMatters: 3.0%Availability Heuristic8.9%This article: 7.5%Ella Jackson: 0.6%CalMatters: 1.0%Representativeness Heuristic7.5%This article: 2.6%Ella Jackson: 0.2%CalMatters: 0.5%Hindsight Bias2.6%This article: 0.0%Ella Jackson: 1.0%CalMatters: 1.2%Overconfidence Bias0.0%This article: 2.8%Ella Jackson: 7.4%CalMatters: 6.3%Framing Effect2.8%This article: 3.4%Ella Jackson: 1.4%CalMatters: 1.0%Loss Aversion3.4%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.7%Status Quo Bias0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.2%Sunk Cost Effect0.0%This article: 6.3%Ella Jackson: 3.4%CalMatters: 3.5%Optimism Bias6.3%This article: 0.0%Ella Jackson: 2.7%CalMatters: 1.4%Pessimism Bias0.0%This article: 17.9%Ella Jackson: 9.0%CalMatters: 6.4%Negativity Bias17.9%This article: 0.0%Ella Jackson: 2.1%CalMatters: 1.7%Self-Serving Bias0.0%This article: 0.0%Ella Jackson: 1.2%CalMatters: 0.7%Fundamental Attribution Error0.0%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.2%Actor-Observer Bias0.0%This article: 6.1%Ella Jackson: 2.5%CalMatters: 1.7%In-Group Bias6.1%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.4%Out-Group Homogeneity Bias0.0%This article: 1.6%Ella Jackson: 1.7%CalMatters: 2.7%Halo Effect1.6%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Horn Effect0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Ella Jackson: 1.1%CalMatters: 0.9%Recency Bias0.0%This article: 0.0%Ella Jackson: 0.5%CalMatters: 0.3%Primacy Effect0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 0.0%Ella Jackson: 0.5%CalMatters: 0.6%Ad Hominem0.0%This article: 0.0%Ella Jackson: 0.5%CalMatters: 0.2%Straw Man0.0%This article: 0.0%Ella Jackson: 4.8%CalMatters: 3.1%Appeal to Authority0.0%This article: 2.6%Ella Jackson: 1.6%CalMatters: 1.1%False Dilemma2.6%This article: 0.0%Ella Jackson: 1.2%CalMatters: 0.8%Slippery Slope0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Circular Reasoning0.0%This article: 3.2%Ella Jackson: 3.9%CalMatters: 3.6%Hasty Generalization3.2%This article: 0.0%Ella Jackson: 0.4%CalMatters: 0.2%Red Herring0.0%This article: 0.0%Ella Jackson: 0.9%CalMatters: 0.7%Bandwagon0.0%This article: 15.0%Ella Jackson: 9.5%CalMatters: 5.3%Appeal to Emotion15.0%This article: 0.0%Ella Jackson: 1.3%CalMatters: 0.6%Begging the Question0.0%This article: 6.1%Ella Jackson: 4.4%CalMatters: 2.0%Post Hoc (False Cause)6.1%This article: 0.0%Ella Jackson: 0.2%CalMatters: 0.1%Tu Quoque0.0%This article: 6.9%Ella Jackson: 0.7%CalMatters: 0.3%Burden of Proof6.9%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.2%Appeal to Nature0.0%This article: 10.7%Ella Jackson: 0.3%CalMatters: 0.2%Composition/Division10.7%This article: 17.9%Ella Jackson: 4.0%CalMatters: 3.1%Anecdotal17.9%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 1.0%Ella Jackson: 1.2%CalMatters: 1.2%Ambiguity (Equivocation)1.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Middle Ground0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Personal Incredulity0.0%This article: 0.0%Ella Jackson: 0.0%CalMatters: 0.1%Special Pleading0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Genetic Fallacy0.0%This article: 5.1%Ella Jackson: 0.9%CalMatters: 0.8%Unattributed Quote5.1%This article: 0.0%Ella Jackson: 0.9%CalMatters: 0.7%Quote-first Misdirection0.0%This article: 0.0%Ella Jackson: 2.3%CalMatters: 3.1%Biased Writer Voice0.0%This article: 0.0%Ella Jackson: 1.0%CalMatters: 1.9%Indoctrination0.0%This article: 3.2%Ella Jackson: 2.6%CalMatters: 1.1%Politically Left Leaning Bias3.2%This article: 0.0%Ella Jackson: 0.1%CalMatters: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Ella Jackson: 0.1%CalMatters: 1.2%Attempt to Sell a Product or S…0.0%

507 words analyzed.

Speakers

3speakers37%attributed speech320writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageLatasha Reed • 15 words • 0.0% coverageLatasha Reed • 17 words • 100.0% coverageLatasha Reed • 9 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageDavid Huerta • 22 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageCharles Person • 35 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageCharles Person • 8 words • 0.0% coverageCharles Person • 17 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageCharles Person • 28 words • 0.0% coverageDavid Huerta • 31 words • 0.0% coverageDavid Huerta • 5 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverage
Selected voice

David Huerta

100%flagged-word coverage
58 attributed words31% of attributed speech57% writer coverage
0%2.5%5.0%Politically Left Leaning B-5.0 ptsWriter: 5.0%David Huerta: 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.