Gizmodo62%

Feds Misused Crowd-Control Weapons Over 400 Times During Immigration Protests, Doctors Report 71%

By Matthew Phelan37%

7/16/2026, 5:50:36 PM

BS Summary: This article contains 30 faulty reasoning types, including Hasty Generalization, Appeal to Authority, and Appeal to Emotion, with Negativity Bias as the most egregious example at 39.2% saturation with 265 hits. Analysis detected 2,303 faulty-reasoning hits from 676 analyzed words, generating a BS Score of 63.9% and a BS Rank of 71% (6,037 of 20,381 articles). This article is worse (more manipulative) than 70.40% of the article peer group.

“Every tool is a weapon, if you hold it right,” as that oft-quoted lyric by Ani DiFranco goes. 
But the irony may be even more apparent when it comes to so-called nonlethal weapons, which law enforcement officers in the U.S. have repeatedly used in ways that can cause serious injuries and, in some cases, death. 
The problem has gotten pretty bad in the past year, as you can imagine, thanks to the Trump administration’s brutal (and yet perversely haphazard) execution of its immigration enforcement agenda. 
Now that damage has been quantified. 
Doctors with the nonprofit Physicians for Human Rights (PHR) working with the University of California, Berkeley, have determined that law enforcement misused crowd-control weapons at least 412 times during immigration enforcement protests between June 2025 and May 2026. 
By PHR’s count, 119 people have been maimed by government agents unsafely firing nonlethal projectiles at close range, launching tear gas canisters directly at protestors, and/or other breaches of established protocol for these tools. 
“We documented over 100 cases of injuries caused when law enforcement agencies deployed crowd-control weapons in ways that violated manufacturer guidance, agency policies, widely accepted policing norms or international use-of-force standards,” emergency physician Rohini Haar, a medical advisor to PHR and lead author of the new report, said in a statement. 
Haar noted that these injuries—which led to medical care for roughly half (47.1%) of those harmed—raise “serious concerns under constitutional and international human rights law.” 
A (nonlethal) bullet to the head 
One disturbing trend that Haar and her research partners identified, according to their report, was a high number of head injuries: 19 impacting the brain, 10 damaging victims’ eyes, and at least one leading to hearing loss. 
The findings suggest “a pattern of force directed towards the head,” they wrote. 
And, whether premeditated or accidental, each case violated official use-of-force guidelines for deployment of these nonlethal weapons. 
“Harms caused by the misuse of crowd-control weapons are almost always foreseeable, particularly when these weapons are deployed against vulnerable populations like children or [….] contrary to manufacturers' guidance or international use-of-force standards,” Haar noted. 
The PHR and UC Berkeley team noted that its findings were almost guaranteed to be underestimates, given that “visual investigative techniques cannot adequately assess invisible injuries, such as chemical injury or chronic pain or hearing loss.” 
While the team recorded 119 people injured—via a dataset that included press photos, court filings, and police body-cam footage—a total of 203 sustained injuries were documented, as many individuals were attacked more than once. 
Haar added that these remote forensic methods were unfortunately the best accounting available of these probable human rights violations. 
“[M]eaningful public oversight of incidents during the recent surge in U.S. immigration enforcement protests is nearly impossible because of the very limited reliable official reporting,” she said. 
The Bovino effect 
“Surges in violence during operations, including those in Los Angeles, Chicago, and Minneapolis, can be directly attributed to the leadership of Border Patrol agent Greg Bovino and his supervisors,” according to the new report. 
“Each time Bovino was present at the site of major protests, incidents rose dramatically.” 
In fact, the researchers found that over 90% of these damaging incidents were documented in just five major U.S. cities: Chicago, Los Angeles, Minneapolis, Newark, and Portland. 
Among these injuries, 86% were directly attributable to Department of Homeland Security actions, as they came amid sieges DHS officials led themselves, each time with dimwitted, self-aggrandizing names like operations “Midway Blitz” (Chicago) and “Metro Surge” (Minneapolis). 
Law enforcement officers misusing chemical irritants, like tear gas, and kinetic impact projectiles, like rubber bullets, contributed to the vast majority of these avoidable injuries. 
But particularly damaging hybrid weapons, pepper balls, made up over 25% of the 203 total injuries, the data show. 
“We built our own dataset documenting severe injuries and permanent disabilities caused by the misuse of these weapons,” Haar said. 
“We invite the Department of Homeland Security, Congress and state authorities to examine these findings and act immediately to prevent further suffering.” 
Article reasoning-pattern comparisonThis article: 11.5%Matthew Phelan: 4.0%Gizmodo: 4.3%Confirmation Bias11.5%This article: 4.9%Matthew Phelan: 1.4%Gizmodo: 1.5%Anchoring Bias4.9%This article: 17.0%Matthew Phelan: 4.8%Gizmodo: 3.1%Availability Heuristic17.0%This article: 9.5%Matthew Phelan: 3.6%Gizmodo: 1.5%Representativeness Heuristic9.5%This article: 0.0%Matthew Phelan: 1.7%Gizmodo: 0.7%Hindsight Bias0.0%This article: 7.8%Matthew Phelan: 3.3%Gizmodo: 2.3%Overconfidence Bias7.8%This article: 4.4%Matthew Phelan: 3.3%Gizmodo: 5.8%Framing Effect4.4%This article: 0.0%Matthew Phelan: 0.9%Gizmodo: 0.6%Loss Aversion0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.4%Status Quo Bias0.0%This article: 0.0%Matthew Phelan: 1.1%Gizmodo: 0.3%Sunk Cost Effect0.0%This article: 3.3%Matthew Phelan: 1.2%Gizmodo: 4.0%Optimism Bias3.3%This article: 17.5%Matthew Phelan: 2.8%Gizmodo: 2.3%Pessimism Bias17.5%This article: 39.2%Matthew Phelan: 9.8%Gizmodo: 7.9%Negativity Bias39.2%This article: 3.0%Matthew Phelan: 2.7%Gizmodo: 0.8%Self-Serving Bias3.0%This article: 14.9%Matthew Phelan: 1.8%Gizmodo: 1.1%Fundamental Attribution Error14.9%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.2%Actor-Observer Bias0.0%This article: 0.0%Matthew Phelan: 0.5%Gizmodo: 0.9%In-Group Bias0.0%This article: 0.0%Matthew Phelan: 0.4%Gizmodo: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%Matthew Phelan: 1.3%Gizmodo: 2.9%Halo Effect0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.2%Horn Effect0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.0%Dunning-Kruger Effect0.0%This article: 8.4%Matthew Phelan: 1.6%Gizmodo: 1.5%Recency Bias8.4%This article: 2.7%Matthew Phelan: 0.8%Gizmodo: 0.4%Primacy Effect2.7%This article: 5.3%Matthew Phelan: 0.7%Gizmodo: 0.1%Blind-Spot Bias5.3%This article: 5.5%Matthew Phelan: 1.1%Gizmodo: 1.2%Ad Hominem5.5%This article: 0.0%Matthew Phelan: 0.6%Gizmodo: 0.2%Straw Man0.0%This article: 24.9%Matthew Phelan: 8.1%Gizmodo: 4.2%Appeal to Authority24.9%This article: 0.0%Matthew Phelan: 1.8%Gizmodo: 1.4%False Dilemma0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.7%Slippery Slope0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.2%Circular Reasoning0.0%This article: 34.6%Matthew Phelan: 4.3%Gizmodo: 5.5%Hasty Generalization34.6%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.3%Red Herring0.0%This article: 0.0%Matthew Phelan: 0.3%Gizmodo: 0.9%Bandwagon0.0%This article: 21.9%Matthew Phelan: 6.5%Gizmodo: 5.1%Appeal to Emotion21.9%This article: 2.5%Matthew Phelan: 1.0%Gizmodo: 1.0%Begging the Question2.5%This article: 17.5%Matthew Phelan: 3.1%Gizmodo: 3.4%Post Hoc (False Cause)17.5%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.1%Tu Quoque0.0%This article: 7.2%Matthew Phelan: 0.9%Gizmodo: 0.5%Burden of Proof7.2%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.2%Appeal to Nature0.0%This article: 0.0%Matthew Phelan: 0.4%Gizmodo: 0.3%Composition/Division0.0%This article: 6.4%Matthew Phelan: 3.2%Gizmodo: 2.1%Anecdotal6.4%This article: 5.2%Matthew Phelan: 0.4%Gizmodo: 0.1%No True Scotsman5.2%This article: 11.8%Matthew Phelan: 2.8%Gizmodo: 2.5%Ambiguity (Equivocation)11.8%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matthew Phelan: 0.2%Gizmodo: 0.1%Middle Ground0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.1%Personal Incredulity0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.1%Special Pleading0.0%This article: 0.0%Matthew Phelan: 0.0%Gizmodo: 0.1%Genetic Fallacy0.0%This article: 7.5%Matthew Phelan: 1.9%Gizmodo: 4.9%Unattributed Quote7.5%This article: 3.6%Matthew Phelan: 2.0%Gizmodo: 1.3%Quote-first Misdirection3.6%This article: 19.7%Matthew Phelan: 12.0%Gizmodo: 13.8%Biased Writer Voice19.7%This article: 7.7%Matthew Phelan: 2.3%Gizmodo: 1.7%Indoctrination7.7%This article: 10.9%Matthew Phelan: 2.4%Gizmodo: 1.5%Politically Left Leaning Bias10.9%This article: 4.4%Matthew Phelan: 0.3%Gizmodo: 0.1%Politically Right Leaning Bias4.4%This article: 0.0%Matthew Phelan: 0.4%Gizmodo: 3.2%Attempt to Sell a Product or S…0.0%

676 words analyzed.

Speakers

2speakers51%attributed speech332writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 37 words • 100.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 6 words • 100.0% coveragePhysicians for Human Rights (PHR) • 38 words • 0.0% coveragePhysicians for Human Rights (PHR) • 34 words • 0.0% coverageRohini Haar • 51 words • 100.0% coverageRohini Haar • 25 words • 0.0% coverageWriter's voice • 6 words • 100.0% coverageRohini Haar • 37 words • 0.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageRohini Haar • 35 words • 0.0% coveragePhysicians for Human Rights (PHR) • 36 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageRohini Haar • 19 words • 100.0% coverageRohini Haar • 27 words • 0.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 37 words • 100.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageRohini Haar • 20 words • 0.0% coverageRohini Haar • 22 words • 100.0% coverage
Selected voice

Rohini Haar

100%flagged-word coverage
236 attributed words69% of attributed speech90% writer coverage
0%17.5%35.0%Biased Writer Voice-26.3 ptsWriter: 34.3%Rohini Haar: 8.1%8.1%Politically Left Leaning B-22.3 ptsWriter: 22.3%Rohini Haar: 0.0%0.0%Unattributed Quote+21.6 ptsWriter: 0.0%Rohini Haar: 21.6%21.6%Indoctrination+0.3 ptsWriter: 9.0%Rohini Haar: 9.3%9.3%Politically Right Leaning -9.0 ptsWriter: 9.0%Rohini Haar: 0.0%0.0%Quote-first Misdirection-7.2 ptsWriter: 7.2%Rohini Haar: 0.0%0.0%

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

Loading…
Loading…
Loading…

Analysis

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