Calif. eliminates consumer ‘sell by’ food labels 58%

By OAN Staff Lillian Mann0%

7/1/2026, 6:15:57 PM

BS Summary: This article contains 16 faulty reasoning types, including Post Hoc (False Cause), Negativity Bias, and Hasty Generalization, with Biased Writer Voice as the most egregious example at 44% saturation with 184 hits. Analysis detected 1,097 faulty-reasoning hits from 418 analyzed words, generating a BS Score of 54.9% and a BS Rank of 58% (9,186 of 21,887 articles). This article is worse (more manipulative) than 58.00% of the article peer group.

To cut down on food waste and prevent unnecessary confusion for shoppers, California is drastically changing how grocery items are labeled under a new consumer protection law. 
By banning misleading “sell-by” labels, California’s Assembly Bill 660 aims to stop billions of pounds of unspoiled food from needlessly ending up in landfills. 
The legislation replaces inconsistent wording on packaging with standard, consumer-friendly labels. 
Manufacturers selling in California are now required to use standardized labels: “Best if Used By” to indicate peak quality, and “Use By” to signify product safety. 
Officials note that “sell by” labels often seen on food packaging are intended for retail inventory management. 
However, such labels are often misinterpreted as a consumption safety label. 
The new legislation introduces uniform terminology to clearly distinguish food freshness from health risks, addressing a major driver of food waste in California where consumers frequently mistake “best if used by” dates for safety warnings. 
The measure emphasizes that while an item past this date may have passed its peak flavor profile, it remains entirely safe to consume and should not be needlessly discarded 
Nick Lapis, director of advocacy at Californians Against Waste, which co-sponsored the bill, said food labels are the number one cause of household food waste. 
He also added that the “sell by” date labels have been an ongoing issue at food banks in California since people think the dates mean the food has expired. 
“We don’t need to build some kind of huge infrastructure and invest tons of money to solve this. 
We just need companies to use the same words across brands,” he pressed. 
There are over 50 different date labels on packaged food sold in stores, according to a 2022 report on food waste by the University of Maryland. 
The information in the labels are largely unregulated and often do not relate to food safety. 
“Consumers get confused and they just default to assuming that whatever date is on the package means ‘don’t eat it and throw it away,'” said Kumar Chandran, policy director at ReFED, a nonprofit centered on reducing food waste. 
With no federal regulations dictating what information labels should include, the stamps have reportedly led to consumer confusion and nearly 20% of the nation’s food waste, according to the Food and Drug Administration (FDA). 
In California, that’s about 6 million tons of unexpired food that’s tossed in the trash every year. 
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Article reasoning-pattern comparisonThis article: 14.4%OAN Staff Lillian Mann: 5.0%One America News Network: 4.6%Confirmation Bias14.4%This article: 0.0%OAN Staff Lillian Mann: 1.1%One America News Network: 2.4%Anchoring Bias0.0%This article: 8.4%OAN Staff Lillian Mann: 2.7%One America News Network: 3.9%Availability Heuristic8.4%This article: 0.0%OAN Staff Lillian Mann: 0.8%One America News Network: 1.1%Representativeness Heuristic0.0%This article: 0.0%OAN Staff Lillian Mann: 0.4%One America News Network: 0.9%Hindsight Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 1.7%One America News Network: 2.9%Overconfidence Bias0.0%This article: 17.5%OAN Staff Lillian Mann: 12.0%One America News Network: 15.3%Framing Effect17.5%This article: 0.0%OAN Staff Lillian Mann: 0.3%One America News Network: 1.0%Loss Aversion0.0%This article: 0.0%OAN Staff Lillian Mann: 0.7%One America News Network: 1.1%Status Quo Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 0.0%One America News Network: 0.1%Sunk Cost Effect0.0%This article: 10.0%OAN Staff Lillian Mann: 2.9%One America News Network: 4.3%Optimism Bias10.0%This article: 0.0%OAN Staff Lillian Mann: 1.1%One America News Network: 1.8%Pessimism Bias0.0%This article: 25.4%OAN Staff Lillian Mann: 10.7%One America News Network: 11.8%Negativity Bias25.4%This article: 0.0%OAN Staff Lillian Mann: 2.2%One America News Network: 3.2%Self-Serving Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 1.1%One America News Network: 1.7%Fundamental Attribution Error0.0%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.2%Actor-Observer Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 1.5%One America News Network: 3.9%In-Group Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 0.5%One America News Network: 1.9%Out-Group Homogeneity Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 2.4%One America News Network: 5.3%Halo Effect0.0%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.4%Horn Effect0.0%This article: 0.0%OAN Staff Lillian Mann: 0.0%One America News Network: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%OAN Staff Lillian Mann: 1.3%One America News Network: 2.0%Recency Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 0.6%One America News Network: 0.7%Primacy Effect0.0%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.1%Blind-Spot Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 2.7%One America News Network: 2.5%Ad Hominem0.0%This article: 0.0%OAN Staff Lillian Mann: 0.7%One America News Network: 0.6%Straw Man0.0%This article: 24.4%OAN Staff Lillian Mann: 5.9%One America News Network: 6.3%Appeal to Authority24.4%This article: 4.3%OAN Staff Lillian Mann: 1.6%One America News Network: 1.8%False Dilemma4.3%This article: 0.0%OAN Staff Lillian Mann: 0.8%One America News Network: 1.1%Slippery Slope0.0%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.1%Circular Reasoning0.0%This article: 25.4%OAN Staff Lillian Mann: 4.4%One America News Network: 5.4%Hasty Generalization25.4%This article: 0.0%OAN Staff Lillian Mann: 0.3%One America News Network: 0.5%Red Herring0.0%This article: 0.0%OAN Staff Lillian Mann: 0.6%One America News Network: 1.1%Bandwagon0.0%This article: 0.0%OAN Staff Lillian Mann: 9.5%One America News Network: 10.0%Appeal to Emotion0.0%This article: 0.0%OAN Staff Lillian Mann: 1.3%One America News Network: 1.7%Begging the Question0.0%This article: 29.2%OAN Staff Lillian Mann: 3.4%One America News Network: 3.0%Post Hoc (False Cause)29.2%This article: 0.0%OAN Staff Lillian Mann: 0.4%One America News Network: 0.2%Tu Quoque0.0%This article: 8.1%OAN Staff Lillian Mann: 1.0%One America News Network: 0.6%Burden of Proof8.1%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.2%Appeal to Nature0.0%This article: 0.0%OAN Staff Lillian Mann: 0.3%One America News Network: 0.3%Composition/Division0.0%This article: 9.1%OAN Staff Lillian Mann: 1.7%One America News Network: 1.6%Anecdotal9.1%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.1%No True Scotsman0.0%This article: 13.2%OAN Staff Lillian Mann: 1.8%One America News Network: 1.4%Ambiguity (Equivocation)13.2%This article: 0.0%OAN Staff Lillian Mann: 0.0%One America News Network: 0.0%Gambler’s Fallacy0.0%This article: 0.0%OAN Staff Lillian Mann: 0.0%One America News Network: 0.1%Middle Ground0.0%This article: 0.0%OAN Staff Lillian Mann: 0.1%One America News Network: 0.2%Personal Incredulity0.0%This article: 0.0%OAN Staff Lillian Mann: 0.3%One America News Network: 0.3%Special Pleading0.0%This article: 0.0%OAN Staff Lillian Mann: 0.3%One America News Network: 0.7%Genetic Fallacy0.0%This article: 12.2%OAN Staff Lillian Mann: 2.2%One America News Network: 1.7%Unattributed Quote12.2%This article: 13.4%OAN Staff Lillian Mann: 3.1%One America News Network: 1.5%Quote-first Misdirection13.4%This article: 44.0%OAN Staff Lillian Mann: 5.6%One America News Network: 5.2%Biased Writer Voice44.0%This article: 0.0%OAN Staff Lillian Mann: 2.4%One America News Network: 1.1%Indoctrination0.0%This article: 0.0%OAN Staff Lillian Mann: 0.4%One America News Network: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%OAN Staff Lillian Mann: 3.8%One America News Network: 3.8%Politically Right Leaning Bias0.0%This article: 3.6%OAN Staff Lillian Mann: 2.7%One America News Network: 1.5%Attempt to Sell a Product or S…3.6%

418 words analyzed.

Speakers

2speakers29%attributed speech295writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 7 words • 0.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 29 words • 100.0% coverageNick Lapis • 25 words • 0.0% coverageNick Lapis • 29 words • 100.0% coverageNick Lapis • 18 words • 100.0% coverageNick Lapis • 13 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageKumar Chandran • 38 words • 100.0% coverageWriter's voice • 34 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverage
Selected voice

Kumar Chandran

100%flagged-word coverage
38 attributed words31% of attributed speech89% writer coverage
0%50.0%100.0%Quote-first Misdirection+100.0 ptsWriter: 0.0%Kumar Chandran: 100.0%100.0%Biased Writer Voice-48.1 ptsWriter: 48.1%Kumar Chandran: 0.0%0.0%Unattributed Quote-17.3 ptsWriter: 17.3%Kumar Chandran: 0.0%0.0%Attempt to Sell a Product -5.1 ptsWriter: 5.1%Kumar Chandran: 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.