10 ways Democrats could make healthcare cheaper after their huge election wins

What was read

2744 words in the outlet's own voice, plus 12 in the headline. 5% of the text is in quotes, with 2 listed words inside quotes. 14 attribution markers (such as "said" or "according to") found.

Listed words inside quotes (not scored; quoting is reporting, but which quotes to use is an editorial choice): actually, obviously.

Scare quotes (a phrase of one to three words in quotation marks with no speaker in the same sentence): facility fee. Since method 3.1.0 their words count as the outlet's own.

Words found

In the headline: huge 1 point Raises the temperature
make healthcare cheaper after their huge election wins
Word choice: actually 1 point Editorializing
in smaller-scale changes that could actually pass in the next few years
actually 1 point Editorializing
consensus that allows them to actually pass a bill in a few
actually 1 point Editorializing
is how US healthcare could actually change at the federal and state
actually 1 point Editorializing
problems, the two sides are actually pretty aligned on a need to
of course attributed to a speaker, not counted Editorializing
denied coverage for medical services. Of course , fixing healthcare is easier said than
actually 1 point Editorializing
Tuesday night. In Wisconsin, Democrats actually secured full control of the state
Raises the temperature: massive 1 point Raises the temperature
See our ethics statement. Democrats’ massive wins on election night in Congress
massive attributed to a speaker, not counted Raises the temperature
would try to undo the massive Medicaid cuts that the GOP Congress
massive attributed to a speaker, not counted Raises the temperature
get Trump’s signature. Given how massive the Democratic wins were, Republicans in
huge 1 point Raises the temperature
clinic. The reason for that huge discrepancy is that Medicare allows hospitals
enormous negated, not counted Raises the temperature
the effect may not be enormous , lower hospital spending could also help
massive 1 point Raises the temperature
that Americans didn’t get a massive bill if they were treated by
huge 1 point Raises the temperature
a mess , with a huge backlog waiting to be cleared. Monk
sweeping 1 point Raises the temperature
coverage in Georgia. The most sweeping policy change proposed across multiple states
very 1 point Raises the temperature
scores. Once again, on a very good night for Democrats nationwide, all
Attribution phrases: insiders 2 points Unnamed source
But DC insiders believe the appetite could be growing
insiders 2 points Unnamed source
certainly going to press: DC insiders told me they expect Democrats to

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	5	2744	1.8	15.0 (words 351 to 550)	8.4	0 to 40	79
Temperature	6	2744	2.2	10.0 (words 801 to 1000)	6.1	0 to 40	85
Flagged attribution phrases	4	2744	1.5	10.0 (words 601 to 800)	5.7	0 to 20	71
Headline wording	1	25	40.0	headline only	40.0	0 to 240	83

Densest stretch: Word choice, words 351 to 550 of 2744, with actually, actually, actually; Temperature, words 801 to 1000 of 2744, with massive, huge; Flagged attribution phrases, words 601 to 800 of 2744, with insiders.

Shown, not scored: the headline's rate minus the rate of the story's first 80 words is 70.5 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 79 + 10)^0.4 × (Temperature 85 + 10)^0.3 × (Flagged attribution phrases 71 + 10)^0.2 × (Headline wording 83 + 10)^0.1 − 10 = 79. 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.

What a reader could check

People named as sources	3 (Larry Levitt, Keisha Lance Bottoms, Health Politics)
Unnamed sources	2 (40% of all sources found)
Links to primary records (government, court, filings, PDFs)	8 hbhi.jhu.edu, democrats-energycommerce.house.gov, www.warnock.senate.gov, www.cornyn.senate.gov, www.welch.senate.gov, debhaaland.com, www.sanders.senate.gov, www.finance.senate.gov
Documents cited in the text (a report, bill, ruling, filing)	0
Checkable specifics (numbers and dates)	32
Data shown (a table or chart)	no
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 Wed, Nov 4, 1:20 PM ET by text extractor version 4. Fingerprint of the text read: fe29c9845d2bff39. 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.

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