KUOW72%

Should Seattle schools have weapons detectors, fences? Superintendent considers safety upgrades 33%

By Noel Gasca0%

6/16/2026, 10:35:48 PM

BS Summary: This article contains 23 faulty reasoning types, including Availability Heuristic, Anecdotal, and Appeal to Emotion, with Ambiguity (Equivocation) as the most egregious example at 18.9% saturation with 107 hits. Analysis detected 1,011 faulty-reasoning hits from 565 analyzed words, generating a BS Score of 41.6% and a BS Rank of 33% (14,243 of 21,175 articles). This article is better (less manipulative) than 67.30% of the article peer group.

The last day of classes for Seattle Public Schools is tomorrow. 
But the head of the district is already discussing campus safety changes students may see in the fall. 
At a media roundtable on Tuesday, Superintendent Ben Shuldiner said new fencing and security cameras could be put up at schools over the summer. 
"Some schools have been asking for fencing for years," Shuldiner said. 
"I just think that this district hasn't prioritized safety, and so that's something that I'm really thinking about." 
Ingraham High School stands out as a particular safety concern. 
Shuldiner said he visited the school on Monday, and the principal emphasized the need for fences. 
Ingraham has an open campus structure with multiple buildings. 
Shuldiner said the principal told him that random people walk into the school's buildings "at least five times a year." 
For the upcoming school year, the district will focus on enhancing perimeter support with fencing and cameras. 
Shuldiner said many schools already have cameras, but they're not always in the right place. 
Could weapon detectors be coming to Seattle schools? 
School safety has shadowed Shuldiner's first months as superintendent of Seattle schools, even before he officially started the job. 
In February, two teenage boys were killed at a bus stop near South Shore PreK-8 and Rainier Beach High School, just days before Shuldiner took over. 
During Shuldiner's tenure as the superintendent of the Lansing School District in Central Michigan, he said bringing schools up to meet high security standards was a priority. 
As Shuldiner told KUOW back in February, that meant making sure every school had a single point of entry, as well as installing security cameras and fences. 
On Tuesday, he said he's also open to discussing another security tool with students and families: weapon detectors. 
"I want schools to feel like where rich people live, in cathedrals, T-Mobile Park," Shuldiner said. 
"When you go through T-Mobile, you've got to go through weapons detectors. 
There's multiple points of entry, but somebody's always there, and it's a way to actually make folks feel safe." 
In Seattle, Shuldiner said, there's an idea that fences, cameras, or weapon detectors make schools feel like "prisons." 
He believes it's "the exact opposite." 
Communities at Ingraham High School and Garfield have been rocked by gun violence in recent years. 
A metal detector or weapon detection system may have been able to prevent the 2022 shooting death of 17-year-old Ingraham student Ebenezer Haile inside Ingraham High School. 
But it's unclear whether the technology would have been able to prevent the killing of 17-year-old Amarr Murphy Paine in 2025, who was shot just outside Garfield High School. 
Shuldiner said he won't "force" the detection system on schools or mandate their installation  he'll wait for schools to approach him with an ask. 
For now, the detectors are part of "ongoing conversations," but Shuldiner said families and students have already reached out to advocate for them. 
"If it's an outside entity, that's one thing," Shuldiner said. 
"But if it's our own people, if it's an assistant principal, if it's a principal, if our own safety and security folks  those are people that kids really know and trust and work with." 
He also pointed to positive feedback on the use of weapon detectors at graduation ceremonies for Ingraham High School and Nathan Hale last week. 
Article reasoning-pattern comparisonThis article: 8.7%Noel Gasca: 2.9%KUOW: 2.6%Confirmation Bias8.7%This article: 3.0%Noel Gasca: 1.8%KUOW: 1.3%Anchoring Bias3.0%This article: 18.8%Noel Gasca: 2.9%KUOW: 3.4%Availability Heuristic18.8%This article: 2.1%Noel Gasca: 0.9%KUOW: 1.2%Representativeness Heuristic2.1%This article: 4.8%Noel Gasca: 0.8%KUOW: 0.7%Hindsight Bias4.8%This article: 1.1%Noel Gasca: 1.6%KUOW: 1.4%Overconfidence Bias1.1%This article: 8.7%Noel Gasca: 7.9%KUOW: 7.4%Framing Effect8.7%This article: 0.0%Noel Gasca: 1.1%KUOW: 1.0%Loss Aversion0.0%This article: 0.0%Noel Gasca: 0.7%KUOW: 1.0%Status Quo Bias0.0%This article: 0.0%Noel Gasca: 0.3%KUOW: 0.2%Sunk Cost Effect0.0%This article: 3.4%Noel Gasca: 5.6%KUOW: 3.8%Optimism Bias3.4%This article: 0.0%Noel Gasca: 1.5%KUOW: 1.8%Pessimism Bias0.0%This article: 11.2%Noel Gasca: 3.5%KUOW: 8.0%Negativity Bias11.2%This article: 7.6%Noel Gasca: 1.6%KUOW: 2.0%Self-Serving Bias7.6%This article: 0.0%Noel Gasca: 0.8%KUOW: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Noel Gasca: 0.1%KUOW: 0.2%Actor-Observer Bias0.0%This article: 0.0%Noel Gasca: 2.9%KUOW: 2.0%In-Group Bias0.0%This article: 0.0%Noel Gasca: 0.5%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 12.4%Noel Gasca: 5.2%KUOW: 2.7%Halo Effect12.4%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.2%Horn Effect0.0%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 5.7%Noel Gasca: 1.0%KUOW: 1.1%Recency Bias5.7%This article: 5.5%Noel Gasca: 0.5%KUOW: 0.4%Primacy Effect5.5%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.0%Blind-Spot Bias0.0%This article: 0.0%Noel Gasca: 0.3%KUOW: 0.5%Ad Hominem0.0%This article: 3.2%Noel Gasca: 0.1%KUOW: 0.3%Straw Man3.2%This article: 7.6%Noel Gasca: 2.0%KUOW: 4.3%Appeal to Authority7.6%This article: 4.2%Noel Gasca: 0.7%KUOW: 1.4%False Dilemma4.2%This article: 0.0%Noel Gasca: 0.4%KUOW: 0.9%Slippery Slope0.0%This article: 0.0%Noel Gasca: 0.3%KUOW: 0.1%Circular Reasoning0.0%This article: 1.8%Noel Gasca: 2.4%KUOW: 4.1%Hasty Generalization1.8%This article: 0.0%Noel Gasca: 0.2%KUOW: 0.3%Red Herring0.0%This article: 0.0%Noel Gasca: 0.8%KUOW: 0.8%Bandwagon0.0%This article: 16.3%Noel Gasca: 6.5%KUOW: 6.1%Appeal to Emotion16.3%This article: 3.2%Noel Gasca: 1.1%KUOW: 0.8%Begging the Question3.2%This article: 9.4%Noel Gasca: 2.3%KUOW: 2.2%Post Hoc (False Cause)9.4%This article: 0.0%Noel Gasca: 0.1%KUOW: 0.1%Tu Quoque0.0%This article: 0.0%Noel Gasca: 0.3%KUOW: 0.3%Burden of Proof0.0%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.2%Appeal to Nature0.0%This article: 0.0%Noel Gasca: 0.1%KUOW: 0.2%Composition/Division0.0%This article: 17.2%Noel Gasca: 5.6%KUOW: 3.3%Anecdotal17.2%This article: 0.0%Noel Gasca: 0.3%KUOW: 0.1%No True Scotsman0.0%This article: 18.9%Noel Gasca: 1.2%KUOW: 1.4%Ambiguity (Equivocation)18.9%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Noel Gasca: 0.1%KUOW: 0.1%Middle Ground0.0%This article: 0.0%Noel Gasca: 0.0%KUOW: 0.1%Personal Incredulity0.0%This article: 4.4%Noel Gasca: 0.5%KUOW: 0.2%Special Pleading4.4%This article: 0.0%Noel Gasca: 0.1%KUOW: 0.2%Genetic Fallacy0.0%This article: 0.0%Noel Gasca: 0.9%KUOW: 1.0%Unattributed Quote0.0%This article: 0.0%Noel Gasca: 0.4%KUOW: 0.8%Quote-first Misdirection0.0%This article: 0.0%Noel Gasca: 1.9%KUOW: 3.2%Biased Writer Voice0.0%This article: 0.0%Noel Gasca: 0.6%KUOW: 1.5%Indoctrination0.0%This article: 0.0%Noel Gasca: 2.2%KUOW: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Noel Gasca: 0.6%KUOW: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Noel Gasca: 0.3%KUOW: 1.3%Attempt to Sell a Product or S…0.0%

565 words analyzed.

Speakers

1speaker64%attributed speech201writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 7 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageBen Shuldiner • 24 words • 0.0% coverageBen Shuldiner • 11 words • 0.0% coverageBen Shuldiner • 18 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageBen Shuldiner • 16 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageBen Shuldiner • 20 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageBen Shuldiner • 15 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageBen Shuldiner • 27 words • 0.0% coverageBen Shuldiner • 27 words • 0.0% coverageBen Shuldiner • 18 words • 0.0% coverageBen Shuldiner • 16 words • 0.0% coverageBen Shuldiner • 12 words • 0.0% coverageBen Shuldiner • 19 words • 0.0% coverageBen Shuldiner • 18 words • 0.0% coverageBen Shuldiner • 6 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageBen Shuldiner • 25 words • 0.0% coverageBen Shuldiner • 23 words • 0.0% coverageBen Shuldiner • 10 words • 0.0% coverageBen Shuldiner • 35 words • 0.0% coverageBen Shuldiner • 24 words • 0.0% coverage
Selected voice

Ben Shuldiner

88%flagged-word coverage
364 attributed words100% of attributed speech76% writer coverage

No manipulation-pattern hits were found in this speaker's attributed words or the writer's voice.

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

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Analysis

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