Semafor85%

Centrist Democrats back more candidates 29%

By Nicholas Wu0%

7/8/2026, 9:14:42 AM

BS Summary: This article contains 8 faulty reasoning types, including Overconfidence Bias, Unattributed Quote, and Quote-first Misdirection, with Primacy Effect as the most egregious example at 27.2% saturation with 40 hits. Analysis detected 209 faulty-reasoning hits from 147 analyzed words, generating a BS Score of 39.3% and a BS Rank of 29% (15,189 of 21,203 articles). This article is better (less manipulative) than 71.60% of the article peer group.

The centrist New Democrat Coalition’s political arm is backing more Democratic House candidates, including several in competitive primaries. 
Democrats Marlene Galán-Woods in Arizona, Richard Pan in California, Bale Dalton in Florida, Lindsay James in Iowa, and Jeremy Moss in Michigan are picking up support from the New Democrat Coalition Action Fund, according to details shared first with Semafor. 
The bloc’s chair, Rep. 
Brad Schneider, D-Ill., argued their candidates are competing where it matters to take back the House majority: “Winning a D+60 primary doesn’t win the majority; it just makes headlines and blue seats bluer.” 
The bloc’s backing could provide a boost to Galán-Woods, who is vying for the nomination for one of the most competitive seats in the country. 
She already has backing from the DCCC, though 2024 nominee Amish Shah is hoping for another shot at Arizona’s swingy 1st District. 
Article reasoning-pattern comparisonThis article: 0.0%Nicholas Wu: 7.2%Semafor: 4.7%Confirmation Bias0.0%This article: 0.0%Nicholas Wu: 5.6%Semafor: 1.6%Anchoring Bias0.0%This article: 0.0%Nicholas Wu: 6.4%Semafor: 5.4%Availability Heuristic0.0%This article: 0.0%Nicholas Wu: 3.3%Semafor: 1.4%Representativeness Heuristic0.0%This article: 0.0%Nicholas Wu: 1.1%Semafor: 1.0%Hindsight Bias0.0%This article: 22.4%Nicholas Wu: 3.0%Semafor: 2.4%Overconfidence Bias22.4%This article: 3.4%Nicholas Wu: 13.1%Semafor: 16.1%Framing Effect3.4%This article: 0.0%Nicholas Wu: 1.1%Semafor: 0.8%Loss Aversion0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.7%Status Quo Bias0.0%This article: 0.0%Nicholas Wu: 2.7%Semafor: 0.6%Sunk Cost Effect0.0%This article: 17.0%Nicholas Wu: 13.7%Semafor: 5.2%Optimism Bias17.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 4.0%Pessimism Bias0.0%This article: 0.0%Nicholas Wu: 3.0%Semafor: 12.7%Negativity Bias0.0%This article: 0.0%Nicholas Wu: 2.3%Semafor: 1.3%Self-Serving Bias0.0%This article: 0.0%Nicholas Wu: 1.6%Semafor: 0.8%Fundamental Attribution Error0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Actor-Observer Bias0.0%This article: 12.2%Nicholas Wu: 5.7%Semafor: 1.5%In-Group Bias12.2%This article: 0.0%Nicholas Wu: 0.9%Semafor: 0.9%Out-Group Homogeneity Bias0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 2.1%Halo Effect0.0%This article: 0.0%Nicholas Wu: 0.6%Semafor: 0.2%Horn Effect0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.1%Dunning-Kruger Effect0.0%This article: 15.0%Nicholas Wu: 2.1%Semafor: 3.2%Recency Bias15.0%This article: 27.2%Nicholas Wu: 1.6%Semafor: 0.8%Primacy Effect27.2%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Blind-Spot Bias0.0%This article: 0.0%Nicholas Wu: 0.6%Semafor: 0.5%Ad Hominem0.0%This article: 0.0%Nicholas Wu: 1.1%Semafor: 0.4%Straw Man0.0%This article: 0.0%Nicholas Wu: 2.0%Semafor: 7.1%Appeal to Authority0.0%This article: 0.0%Nicholas Wu: 2.7%Semafor: 2.6%False Dilemma0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 2.1%Slippery Slope0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.1%Circular Reasoning0.0%This article: 0.0%Nicholas Wu: 3.8%Semafor: 8.1%Hasty Generalization0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.3%Red Herring0.0%This article: 0.0%Nicholas Wu: 1.1%Semafor: 1.1%Bandwagon0.0%This article: 0.0%Nicholas Wu: 4.2%Semafor: 6.2%Appeal to Emotion0.0%This article: 0.0%Nicholas Wu: 1.9%Semafor: 1.2%Begging the Question0.0%This article: 0.0%Nicholas Wu: 3.9%Semafor: 5.0%Post Hoc (False Cause)0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.1%Tu Quoque0.0%This article: 0.0%Nicholas Wu: 1.4%Semafor: 0.3%Burden of Proof0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Appeal to Nature0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.6%Composition/Division0.0%This article: 0.0%Nicholas Wu: 2.0%Semafor: 2.0%Anecdotal0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.1%No True Scotsman0.0%This article: 0.0%Nicholas Wu: 1.7%Semafor: 2.8%Ambiguity (Equivocation)0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.1%Middle Ground0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Personal Incredulity0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.2%Special Pleading0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.0%Genetic Fallacy0.0%This article: 22.4%Nicholas Wu: 7.8%Semafor: 5.3%Unattributed Quote22.4%This article: 22.4%Nicholas Wu: 5.6%Semafor: 4.1%Quote-first Misdirection22.4%This article: 0.0%Nicholas Wu: 3.5%Semafor: 9.2%Biased Writer Voice0.0%This article: 0.0%Nicholas Wu: 1.1%Semafor: 1.8%Indoctrination0.0%This article: 0.0%Nicholas Wu: 8.2%Semafor: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.8%Politically Right Leaning Bias0.0%This article: 0.0%Nicholas Wu: 0.0%Semafor: 0.9%Attempt to Sell a Product or S…0.0%

147 words analyzed.

Speakers

1speaker22%attributed speech114writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 5 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 40 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageBrad Schneider • 33 words • 100.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverage
Selected voice

Brad Schneider

100%flagged-word coverage
33 attributed words100% of attributed speech96% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%Brad Schneider: 100.0%100.0%Quote-first Misdirection+100.0 ptsWriter: 0.0%Brad Schneider: 100.0%100.0%

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.