RedState91%

Seriously? Sacramento Deploys Climate Cops to Rifle Through Your Garbage Cans Like Common Thieves 75%

By Rusty Weiss90%

7/15/2026, 12:36:00 AM

BS Summary: This article contains 26 faulty reasoning types, including Appeal to Emotion, Negativity Bias, and Unattributed Quote, with Biased Writer Voice as the most egregious example at 53.8% saturation with 285 hits. Analysis detected 1,557 faulty-reasoning hits from 530 analyzed words, generating a BS Score of 67.1% and a BS Rank of 75% (5,474 of 21,887 articles). This article is worse (more manipulative) than 75.00% of the article peer group.

Sacramento is set to deploy its shiny new Climate Cops to start rifling through residents’ garbage cans like common thieves—because the world will end in chaos and despair if not for your improperly sorted banana peels ... or something. 
Now, I'm not going to lie. 
When I first came upon this story, I thought I had waded into Babylon Bee territory here. 
But nay, this is real. 
Garbage in, garbage out, taken to new, ridiculous levels in Sactown. 
Why is this happening? 
Well, pull up your little hemp yoga mat and let me tell you a little tale. 
Under California’s SB 1383 climate law, city crews will spend the next several months prying open thousands of curbside bins, rifling through Hefty bags, snapping photos, and leaving passive-aggressive tags that will either admonish the homeowner or give them a pat on the head. 
Upon waking the next morning, you might find your trash bins bearing a "Great job!" 
sticker. 
Or, if you're one of the heathens who put their coffee grounds in with the regular trash, you might earn yourself a "Let's sort this out" tag. 
It's hard to believe that news segment is real and not something from The Daily Show or Saturday Night Live. 
Residents will be treated as if they're back in Kindergarten in River City. 
Don't believe me? 
Check out one of the tags from CalRecycle, which features a cartoon of trash and recycling bin characters, Big Blue and Binnie, who try to entice customers with prizes. 
Your very old Gold Star! 
Is this even real life anymore? 
Don't worry, officials are almost certainly working tirelessly behind the scenes to address the city's high crime and homelessness problem. 
It's just that the trash is more important right now. 
Under SB 1383, cities and counties are legally required to run "contamination monitoring" programs to ensure folks are sorting their waste correctly. 
Jesa David, a city representative, explained that the city has actually conducted this type of review in the past in an interview with KCRA 3. 
"We conducted the same reviews last June, and we found high contamination levels of, you know, issues like plastic bags in recycling, garbage in the organics," she said. 
"Any container that we touch will either get a 'great job' tag or a 'let's sort this out' tag," David added. 
"But either way, we want to provide education and make sure everyone knows the resources that they have available to sort their waste correctly." 
"When you sort your waste incorrectly, it does cost us more to dispose of it." 
Okay, Captain Compost. 
After taking pictures of your garbage, CalRecycle explains that the law requires they "educate residents and businesses if there is contamination identified in their containers on the route reviews." 
Like I said, back-to-school time. 
Crews will actually have badges to identify themselves as garbage inspectors. 
Their super-spiffy uniforms will also include high-visibility vests. 
Mall cops are going to point and laugh at these people. 
Residents won't have to worry too much, though, as fines will not be levied for offenders. 
Yet, anyway. 
You just know it's coming. 
Article reasoning-pattern comparisonThis article: 4.7%Rusty Weiss: 9.6%RedState: 7.0%Confirmation Bias4.7%This article: 0.9%Rusty Weiss: 0.7%RedState: 0.8%Anchoring Bias0.9%This article: 18.5%Rusty Weiss: 3.9%RedState: 3.4%Availability Heuristic18.5%This article: 0.0%Rusty Weiss: 0.8%RedState: 1.0%Representativeness Heuristic0.0%This article: 0.0%Rusty Weiss: 0.8%RedState: 0.9%Hindsight Bias0.0%This article: 0.9%Rusty Weiss: 1.2%RedState: 1.9%Overconfidence Bias0.9%This article: 18.3%Rusty Weiss: 13.0%RedState: 9.2%Framing Effect18.3%This article: 0.0%Rusty Weiss: 0.5%RedState: 0.5%Loss Aversion0.0%This article: 0.0%Rusty Weiss: 0.4%RedState: 0.4%Status Quo Bias0.0%This article: 0.0%Rusty Weiss: 0.0%RedState: 0.1%Sunk Cost Effect0.0%This article: 6.8%Rusty Weiss: 0.4%RedState: 1.7%Optimism Bias6.8%This article: 10.6%Rusty Weiss: 2.0%RedState: 1.7%Pessimism Bias10.6%This article: 33.4%Rusty Weiss: 19.8%RedState: 13.9%Negativity Bias33.4%This article: 0.0%Rusty Weiss: 1.9%RedState: 1.8%Self-Serving Bias0.0%This article: 0.0%Rusty Weiss: 2.2%RedState: 2.0%Fundamental Attribution Error0.0%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.3%Actor-Observer Bias0.0%This article: 3.0%Rusty Weiss: 4.5%RedState: 4.7%In-Group Bias3.0%This article: 0.0%Rusty Weiss: 2.5%RedState: 3.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Rusty Weiss: 1.0%RedState: 2.0%Halo Effect0.0%This article: 0.0%Rusty Weiss: 0.8%RedState: 0.8%Horn Effect0.0%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Rusty Weiss: 1.5%RedState: 1.2%Recency Bias0.0%This article: 5.1%Rusty Weiss: 0.9%RedState: 0.5%Primacy Effect5.1%This article: 0.0%Rusty Weiss: 0.0%RedState: 0.1%Blind-Spot Bias0.0%This article: 8.1%Rusty Weiss: 10.0%RedState: 6.9%Ad Hominem8.1%This article: 0.0%Rusty Weiss: 2.3%RedState: 2.6%Straw Man0.0%This article: 10.9%Rusty Weiss: 3.4%RedState: 3.3%Appeal to Authority10.9%This article: 9.2%Rusty Weiss: 2.7%RedState: 2.4%False Dilemma9.2%This article: 0.0%Rusty Weiss: 1.9%RedState: 1.4%Slippery Slope0.0%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.2%Circular Reasoning0.0%This article: 4.2%Rusty Weiss: 9.6%RedState: 10.1%Hasty Generalization4.2%This article: 1.9%Rusty Weiss: 1.3%RedState: 0.7%Red Herring1.9%This article: 0.0%Rusty Weiss: 1.5%RedState: 0.9%Bandwagon0.0%This article: 36.4%Rusty Weiss: 9.3%RedState: 8.5%Appeal to Emotion36.4%This article: 0.6%Rusty Weiss: 2.1%RedState: 1.8%Begging the Question0.6%This article: 2.8%Rusty Weiss: 4.9%RedState: 2.5%Post Hoc (False Cause)2.8%This article: 0.0%Rusty Weiss: 3.1%RedState: 0.9%Tu Quoque0.0%This article: 7.7%Rusty Weiss: 2.5%RedState: 1.2%Burden of Proof7.7%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.1%Appeal to Nature0.0%This article: 0.0%Rusty Weiss: 0.4%RedState: 0.3%Composition/Division0.0%This article: 7.0%Rusty Weiss: 2.0%RedState: 2.6%Anecdotal7.0%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.3%No True Scotsman0.0%This article: 7.4%Rusty Weiss: 1.8%RedState: 1.5%Ambiguity (Equivocation)7.4%This article: 0.0%Rusty Weiss: 0.0%RedState: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Rusty Weiss: 0.1%RedState: 0.0%Middle Ground0.0%This article: 9.2%Rusty Weiss: 0.3%RedState: 0.2%Personal Incredulity9.2%This article: 0.0%Rusty Weiss: 0.2%RedState: 0.2%Special Pleading0.0%This article: 0.0%Rusty Weiss: 0.7%RedState: 0.3%Genetic Fallacy0.0%This article: 22.6%Rusty Weiss: 3.8%RedState: 2.4%Unattributed Quote22.6%This article: 5.5%Rusty Weiss: 2.3%RedState: 1.8%Quote-first Misdirection5.5%This article: 53.8%Rusty Weiss: 24.5%RedState: 21.5%Biased Writer Voice53.8%This article: 0.0%Rusty Weiss: 4.5%RedState: 4.8%Indoctrination0.0%This article: 4.2%Rusty Weiss: 0.3%RedState: 0.4%Politically Left Leaning Bias4.2%This article: 0.0%Rusty Weiss: 14.5%RedState: 15.4%Politically Right Leaning Bias0.0%This article: 0.0%Rusty Weiss: 4.2%RedState: 3.6%Attempt to Sell a Product or S…0.0%

530 words analyzed.

Speakers

2speakers27%attributed speech388writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 1 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 39 words • 100.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 44 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 6 words • 100.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageJesa David • 25 words • 100.0% coverageJesa David • 28 words • 0.0% coverageJesa David • 21 words • 100.0% coverageJesa David • 24 words • 0.0% coverageJesa David • 15 words • 0.0% coverageWriter's voice • 3 words • 100.0% coverageCalRecycle • 29 words • 100.0% coverageWriter's voice • 5 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 5 words • 100.0% coverage
Selected voice

CalRecycle

100%flagged-word coverage
29 attributed words20% of attributed speech92% writer coverage
0%50.0%100.0%Unattributed Quote+88.4 ptsWriter: 11.6%CalRecycle: 100.0%100.0%Biased Writer Voice-73.5 ptsWriter: 73.5%CalRecycle: 0.0%0.0%Quote-first Misdirection-7.5 ptsWriter: 7.5%CalRecycle: 0.0%0.0%Politically Left Leaning B-5.7 ptsWriter: 5.7%CalRecycle: 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.