MS NOW95%

Democrats flipped another GOP seat  but not for the reason you’d think 0%

By Ryan Teague Beckwith0%

3/31/2026, 10:26:11 AM

Topics: Opinion

BS Summary: This article contains 26 faulty reasoning types, including Ambiguity (Equivocation), Hasty Generalization, and Appeal to Authority, with Post Hoc (False Cause) as the most egregious example at 17.9% saturation with 144 hits. Analysis detected 1,241 faulty-reasoning hits from 804 analyzed words, generating a BS Score of 0% and a BS Rank of 0% (0 of 21,887 articles). This article is better (less manipulative) than 100.00% of the article peer group.

Emily Gregory’s win in a special election in Florida last week was bad news for President Donald Trump for several reasons. 
Last Tuesday, the Democratic candidate won the state legislative district that includes Trump’s Palm Beach estate, Mar-a-Lago; beat a Republican candidate whom Trump had just wholeheartedly endorsed, flipping the district from GOP control; and had a 2-point winning margin in a district Trump won by 17 points in 2024. 
After her win, Gregory told MS NOW that she was “pretty shocked” and “having a fairly out-of-body experience.” 
While the race made national news, the practical consequences are minimal. 
After Gregory was sworn in, the Republican supermajority in the state House went down to 85 seats, with Democrats holding 34. 
And the GOP remains firmly in control of the state Senate as well as the governor’s mansion. 
Even apart from the election’s unusually direct tie to Trump, though, there’s a reason why special elections like Gregory’s  and another in Florida that flipped a state Senate seat Monday  get such outsize attention. 
Research shows they really are predictive of what may happen in the midterm elections, but not for the reason you may think. 
Not that long ago, when a district or a state flipped parties, commentators focused on the “swing voters” who had changed their minds. 
Pollsters and strategists focused on the issues they thought crucial to these groups of voters, with names like “Reagan Democrats” and “soccer moms,” who were believed to be decisive in close races. 
Panels of undecided voters were a staple of news coverage leading up to an election. 
While there are still swing voters  think of the Obama-Trump voters of the 2016 election  partisan polarization has changed how elections are won, if not the way we talk about those wins. 
These days, a winning campaign doesn’t change minds as much as it moves hearts. 
Successful candidates are the ones who inspire their side to turn out, sometimes getting a boost when enthusiasm is dampened on the other side by a lackluster economy or just general indifference. 
In short, campaigning has shifted from persuasion to mobilization. 
You can see this in the data. 
These days, rates of “partisan defection” are historically low: very few Democratic voters break with the party to back a Republican presidential candidate, and vice versa. 
Ticket-splitting  the once-common practice of voting for a president from one party and a senator from the other  is increasingly rare, as are states that have one senator from each party. 
And people who identify with a political party are much more likely to describe the other side as immoral and dishonest. 
In interviews, Gregory certainly sounded like someone trying to persuade voters. 
She said her campaign strategy was to “block out the noise” from national politics and zero in on issues like property insurance, health care and education, castigating her opponent for leaning heavily on his endorsement from Trump. 
She said the results from her race might be a sign. 
“I think there are a lot of voters in South Florida rethinking their choice in the last presidential,” she said. 
There’s not much evidence of Trump regret. 
But there’s not much evidence of Trump regret. 
In April of last year, just 2% of 2024 Trump voters nationwide surveyed by the University of Massachusetts Amherst Poll said they regretted their vote. 
The number was 5% in a similar poll released Monday, but the difference is well within the poll’s margin of error. 
That’s still not enough to explain why a state legislative district in Florida would shift by double digits. 
If Trump voters are feeling remorseful, it’s far more likely that they just stayed home, while those who voted against Trump (or would have if they hadn’t been, like, super busy on Election Day) would be far more likely to show up now, especially in a low-turnout special election. 
By the same token, Gregory’s opponent, Jon Maples, probably didn’t lose swing voters by leaning on his endorsement from Trump, either. 
He was just trying to motivate more Republican voters to show up at the polls. 
It may have worked, too, just not enough to make a difference in such an unfavorable environment. 
Gregory was right about the trend, though. 
Since the 2024 election, Democrats have flipped 30 seats that were held by Republicans everywhere from New Hampshire to Texas, including a state Senate seat in Florida declared on Monday, according to a tally by The New York Times. 
For their part, Republicans have not flipped any Democratic seats. 
That’s a bad sign for Republicans running in November, but not because their supporters have changed their mind about voting for them. 
It’s because their supporters have changed their mind about voting at all. 
Article reasoning-pattern comparisonThis article: 8.8%Ryan Teague Beckwith: 12.7%MS NOW: 7.5%Confirmation Bias8.8%This article: 0.0%Ryan Teague Beckwith: 2.6%MS NOW: 1.2%Anchoring Bias0.0%This article: 8.0%Ryan Teague Beckwith: 3.6%MS NOW: 3.7%Availability Heuristic8.0%This article: 8.1%Ryan Teague Beckwith: 2.7%MS NOW: 1.1%Representativeness Heuristic8.1%This article: 0.9%Ryan Teague Beckwith: 3.3%MS NOW: 1.3%Hindsight Bias0.9%This article: 1.1%Ryan Teague Beckwith: 6.6%MS NOW: 2.6%Overconfidence Bias1.1%This article: 6.1%Ryan Teague Beckwith: 23.1%MS NOW: 15.4%Framing Effect6.1%This article: 0.0%Ryan Teague Beckwith: 0.1%MS NOW: 0.7%Loss Aversion0.0%This article: 2.1%Ryan Teague Beckwith: 2.9%MS NOW: 0.9%Status Quo Bias2.1%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.1%Sunk Cost Effect0.0%This article: 3.9%Ryan Teague Beckwith: 1.1%MS NOW: 2.4%Optimism Bias3.9%This article: 3.0%Ryan Teague Beckwith: 3.4%MS NOW: 3.2%Pessimism Bias3.0%This article: 8.0%Ryan Teague Beckwith: 26.5%MS NOW: 19.2%Negativity Bias8.0%This article: 0.0%Ryan Teague Beckwith: 5.9%MS NOW: 2.4%Self-Serving Bias0.0%This article: 3.4%Ryan Teague Beckwith: 5.0%MS NOW: 3.1%Fundamental Attribution Error3.4%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.3%Actor-Observer Bias0.0%This article: 2.6%Ryan Teague Beckwith: 2.9%MS NOW: 3.5%In-Group Bias2.6%This article: 0.0%Ryan Teague Beckwith: 3.8%MS NOW: 2.5%Out-Group Homogeneity Bias0.0%This article: 1.4%Ryan Teague Beckwith: 3.6%MS NOW: 2.2%Halo Effect1.4%This article: 0.0%Ryan Teague Beckwith: 2.9%MS NOW: 1.8%Horn Effect0.0%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.0%Dunning-Kruger Effect0.0%This article: 3.2%Ryan Teague Beckwith: 1.3%MS NOW: 1.8%Recency Bias3.2%This article: 0.0%Ryan Teague Beckwith: 0.5%MS NOW: 0.6%Primacy Effect0.0%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.2%Blind-Spot Bias0.0%This article: 0.0%Ryan Teague Beckwith: 7.1%MS NOW: 4.7%Ad Hominem0.0%This article: 0.0%Ryan Teague Beckwith: 6.2%MS NOW: 1.3%Straw Man0.0%This article: 15.5%Ryan Teague Beckwith: 6.9%MS NOW: 5.4%Appeal to Authority15.5%This article: 8.8%Ryan Teague Beckwith: 3.4%MS NOW: 2.2%False Dilemma8.8%This article: 0.0%Ryan Teague Beckwith: 2.6%MS NOW: 2.2%Slippery Slope0.0%This article: 0.0%Ryan Teague Beckwith: 0.2%MS NOW: 0.2%Circular Reasoning0.0%This article: 16.8%Ryan Teague Beckwith: 11.3%MS NOW: 8.1%Hasty Generalization16.8%This article: 4.5%Ryan Teague Beckwith: 2.2%MS NOW: 0.7%Red Herring4.5%This article: 0.0%Ryan Teague Beckwith: 0.5%MS NOW: 0.9%Bandwagon0.0%This article: 0.0%Ryan Teague Beckwith: 4.2%MS NOW: 9.8%Appeal to Emotion0.0%This article: 0.9%Ryan Teague Beckwith: 2.5%MS NOW: 2.4%Begging the Question0.9%This article: 17.9%Ryan Teague Beckwith: 4.8%MS NOW: 3.2%Post Hoc (False Cause)17.9%This article: 0.0%Ryan Teague Beckwith: 0.1%MS NOW: 0.6%Tu Quoque0.0%This article: 2.2%Ryan Teague Beckwith: 0.7%MS NOW: 0.9%Burden of Proof2.2%This article: 0.0%Ryan Teague Beckwith: 0.2%MS NOW: 0.1%Appeal to Nature0.0%This article: 0.0%Ryan Teague Beckwith: 0.7%MS NOW: 0.3%Composition/Division0.0%This article: 2.2%Ryan Teague Beckwith: 2.4%MS NOW: 2.6%Anecdotal2.2%This article: 0.0%Ryan Teague Beckwith: 1.0%MS NOW: 0.2%No True Scotsman0.0%This article: 17.5%Ryan Teague Beckwith: 2.1%MS NOW: 1.7%Ambiguity (Equivocation)17.5%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.1%Middle Ground0.0%This article: 0.0%Ryan Teague Beckwith: 0.3%MS NOW: 0.3%Personal Incredulity0.0%This article: 0.0%Ryan Teague Beckwith: 0.3%MS NOW: 0.3%Special Pleading0.0%This article: 0.0%Ryan Teague Beckwith: 2.0%MS NOW: 1.3%Genetic Fallacy0.0%This article: 2.5%Ryan Teague Beckwith: 0.2%MS NOW: 2.5%Unattributed Quote2.5%This article: 2.2%Ryan Teague Beckwith: 0.2%MS NOW: 1.5%Quote-first Misdirection2.2%This article: 2.7%Ryan Teague Beckwith: 17.6%MS NOW: 14.2%Biased Writer Voice2.7%This article: 0.0%Ryan Teague Beckwith: 1.5%MS NOW: 2.1%Indoctrination0.0%This article: 0.0%Ryan Teague Beckwith: 7.9%MS NOW: 5.3%Politically Left Leaning Bias0.0%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.6%Politically Right Leaning Bias0.0%This article: 0.0%Ryan Teague Beckwith: 0.0%MS NOW: 0.4%Attempt to Sell a Product or S…0.0%

804 words analyzed.

Speakers

1speaker11%attributed speech718writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 49 words • 0.0% coverageEmily Gregory • 18 words • 100.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 11 words • 100.0% coverageEmily Gregory • 37 words • 0.0% coverageEmily Gregory • 11 words • 0.0% coverageEmily Gregory • 20 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 49 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 39 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverage
Selected voice

Emily Gregory

57%flagged-word coverage
86 attributed words100% of attributed speech89% writer coverage
0%12.5%25.0%Unattributed Quote+23.3 ptsWriter: 0.0%Emily Gregory: 23.3%23.3%Quote-first Misdirection+20.9 ptsWriter: 0.0%Emily Gregory: 20.9%20.9%Biased Writer Voice-3.1 ptsWriter: 3.1%Emily Gregory: 0.0%0.0%

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

Loading…
Loading…
Loading…
Loading…

Analysis

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