The arithmetic for one report
Diversity, decency and a new south: who were the real winners in the US midterms? |
84Moderate density of flagged expressions
What was read
1101 words in the outlet's own voice, plus 15 in the headline. 1% of the text is in quotes. 1 attribution marker (such as "said" or "according to") found.
Scare quotes (a phrase of one to three words in quotation marks with no speaker in the same sentence): research triangle
, disgusting
, stop whining
. Since method 3.1.0 their words count as the outlet's own.
Insults found but not counted: 7 (losers
, losers
, losers
, losers
, losers
, loser
, losers
). Insult words are experimental until the reader panel rates them, so they are shown and not scored.
Words found
- Word choice:
losers
experimental, shown but not counted Insult or name-calling
hate and fear. The biggest losers ? Megadonors and Super Pacs R atherlosers
experimental, shown but not counted Insult or name-calling
the largest overall winners and losers for the country as a whole.actually
1 point Editorializing
the Democrat Scott Colom may actually win the Mississippi Senate seat heldlosers
experimental, shown but not counted Insult or name-calling
Republican Cindy Hyde-Smith. Even Democratic losers in the south came remarkably closeremarkably
2 points Editorializing
losers in the south came remarkably close to winning: the Democratic Senateclaimed
1 point Expression of doubt
against Michigan’s Dr El-Sayed. Trump claimed if El-Sayed won, he’d . RFKobviously
2 points Editorializing
the Muslim Brotherhood. Their fear-mongering obviously didn’t work. Vance suggested that thedisgusting
attributed to a speaker, not counted Editorializing
the ad, calling their criticism disgusting and telling them to stop whininglosers
experimental, shown but not counted Insult or name-calling
So who were the biggest losers ? Besides Trump and his Republican sycophants,losers
experimental, shown but not counted Insult or name-calling
his Republican sycophants, the biggest losers are the mega donors and Superloser
experimental, shown but not counted Insult or name-calling
own special place in the loser ’s circle. The richest person in thelosers
experimental, shown but not counted Insult or name-calling
considering the overall winners and losers from the 2026 midterms, I’d say - Raises the temperature:
nightmare
2 points Raises the temperature
America. It’s Donald Trump’s DEI nightmare . Georgia voters elected Keisha Lance Bottoms,stunning
1 point Raises the temperature
and Georgia Democrats also scored stunning victories. The biggest surprise was Annieblasted
1 point Raises the temperature
just fine, and Vance even blasted fellow Republicans who spoke out against
Layer scores
Rates are counted points per 1,000 words of the story's own text. For a story longer than 200 words, the rate used is the average of the whole-story rate and the rate in its densest 200-word stretch, so neutral filler cannot dilute a loaded passage away. Score = 100 × (loaded end − rate used) ÷ loaded end, kept between 0 and 100. Headline words count only in the headline layer.
| Layer | Points | Words | Whole-story rate | Densest 200 words | Rate used | Scale | Score |
|---|---|---|---|---|---|---|---|
| Word choice | 6 | 1101 | 5.4 | 15.0 (words 251 to 450) | 10.2 | 0 to 40 | 74 |
| Temperature | 4 | 1101 | 3.6 | 10.0 (words 1 to 200) | 6.8 | 0 to 40 | 83 |
| Flagged attribution phrases | 0 | 1101 | 0.0 | 0.0 (words 1 to 200) | 0.0 | 0 to 20 | 100 |
| Headline wording | 0 | 25 | 0.0 | headline only | 0.0 | 0 to 240 | 100 |
Densest stretch: Word choice, words 251 to 450 of 1101, with actually
, remarkably
; Temperature, words 1 to 200 of 1101, with nightmare
.
Shown, not scored: the headline's rate minus the rate of the story's first 80 words is 0.0 points per 1,000 words.
Overall
Weighted geometric mean of the layers that are established, with 10 added to each layer first and taken away at the end: (Word choice 74 + 10)0.4 × (Temperature 83 + 10)0.3 × (Flagged attribution phrases 100 + 10)0.2 × (Headline wording 100 + 10)0.1 − 10 = 84. Adding 10 means one layer at 0 lowers the total steeply without collapsing it. A weak layer pulls the total down.
Lowered the score most: Word choice.
Name-swap test: with party, politician and institution names swapped, this report's score moves by 0.0 points.
The outlet labels this report as opinion, so it does not count toward the outlet's news score.
What a reader could check
| People named as sources | 0 |
|---|---|
| Unnamed sources | 0 |
| Links to primary records (government, court, filings, PDFs) | 0 |
| Documents cited in the text (a report, bill, ruling, filing) | 0 |
| Checkable specifics (numbers and dates) | 8 |
| Data shown (a table or chart) | yes |
| Correction or clarification noted | no |
What a reader could check, counted by published patterns. Shown beside the score, never part of it. How each count works
Checked against the official record
No vote count or bill number found in the text read.
Record of this measurement
Method 3.1.0, word-list fingerprint 807a17189402a416, read by text extractor version 4. Fingerprint of the text read: 7aaaf15b1b05e545. Internet Archive copy nearest the time it was read. The text read was not kept (read before method 3.1.0, or from the feed only), so a later rule change can be applied only while the report is still in the outlet's feed.