Parties work a bit differently in the US vs e.g. Israel.

In Israel, party insiders choose their politicians. If you want different candidates than an existing party is offering, you have to make your own new party with your own new list.

By contrast, in the US, parties run primary elections where voters pick the candidates. The specifics depend on the state, but in most states the election is held for registered members of that party.

Americans aren’t idiots. Most know third party candidates don’t do well in plurality elections. So smart progressives, alt-right etc. politicians don’t run as a third party candidate against mainstream Democrats and Republicans. Instead, they primary an incumbent Democrat or Republican, like Alexandria Ocasio Cortez, or join the primary when the incumbent retired like Marjorie Taylor Greene.

Somewhere like Israel, Alexandria Ocasio Cortez and Joe Manchin would be in two very different parties. In the US, they’re in the same party.

In places where RCV is passed, you absolutely see more candidates running and getting decent percentages of the vote. Because that isn’t a terrible strategy any more. Someone like AOC might have run as a Progressive or something rather than primarying the Democrat.

Look at third parties and their success in the UK and Canada.

The last general election in the UK was 2019. Conservatives got 43.6% of the vote but 56.2% of the seats. Labor got 32.1% of the votes and 31.1% of the seats.

The biggest national third party, the Liberal Democrats, got 11.6% of the vote but a mere 1.7% of the seats.

In comparison, look at regional third parties. The Scottish National Party got 3.9% of the vote and a whopping 7.4% of the seats. Irish regional parties like Sinn Feinn and the Democratic Unionist Party got a combined 2.3% of the seats with a combined 1.4% of the seats.

Previous elections have been quite similar. In 2015, the far right UKIP won only a single seat after getting a whopping 12.6% of the vote.

Canada is quite similar. The Bloc Quebecois consistently gets more votes than the national New Democratic Party, despite having gotten less than half as many votes.

If you’re on propane, it’s more likely to be cheaper. Particularly over the course of an entire heating season, because they’re more efficient in fall and spring than the coldest part of winter.

But yeah, this study wasn’t looking at cost per therm but just raw COP, which is a pointless metric. It doesn’t even compare the number of watts of heat from burning natural gas in a furnace vs in a modern power plant that supplies a heat pump. Although since we don’t have a carbon tax, that’s only a theoretically interesting comparison.

Heat pumps work fine for most people in the north. Mitsubishi’s cold climate heat pumps supply 85% of their rated heat at -13F. Buffalo is a city known for its winters, and the last time Buffalo’s lowest temperature was below that was 1982. They’re just going to be a more expensive option for most people right now.

Of the 25 dogs identified as pit bull-type dogs by breed signature, 12 were identified by shelter staff as pit bull-type dogs at the time of admission to the shelter (prior to the study visit), including five labeled American Staffordshire terrier mix, four pit bull mix, two pit bull, and one American Staffordshire terrier. During the study, 20/25 dogs were identified by at least one of the four staff assessors as pit bull-type dogs, and five were not identified as pit bull-type dogs by any of the assessors. …

Of the 95 dogs (79%) that lacked breed signatures for pit bull heritage breeds, six (6%) were identified by shelter staff as pit bull-type dogs at the time of shelter admission, and 36 (38%) were identified as pit bull-type dogs by at least one shelter staff assessor at the time of the study visit

So, at intake, 18 dogs were identified as pit bulls but only 2/3rds were at least 12% pit bull.

During the study, 56 dogs were identified as being pit bulls, but only about 1/3rd were in fact at least 12% pit bull.

This is the classic ‘base rate fallacy’. The false positive rate isn’t that high, and the false negative rate isn’t that high either. But because the true positive rate is pretty low, the ratio of true positives to false positives is much worse than you’d intuitively think.

Tests for rare diseases and attempts to behaviorally profile terrorists at airports runs into the same problem. Sometimes, a 99.9% accurate test just moves you from searching for a needle on a farm to a needle in only a single haystack.

Celsius and Fahrenheit have nearly identical definitions.

In Fahrenheit, 0 is the temp of a mixture of ice and a particular brine. In Celsius, it’s the temp of a mixture of ice and water.

In Fahrenheit, there’s 180 degrees between boiling and freezing. In Celsius, it’s 100.

It’s not like distance, where mile comes from the Latin “mille passus”, “thousand paces”. Originally, Roman legions would place mile markers on roads by literally counting out their steps and placing them appropriately.

Meanwhile, a kilometer is a thousand meters, where a meter was originally defined as one ten-millionth of the distance from the equator to the North Pole along a great circle.

Mile and kilometer are defined based on competely different things - a human step vs the circumference of the earth.

And these people are buying tens of thousands of feet of lumber solely to burn it away in the middle of nowhere where there’s little vegetation to absorb the excess CO2 waste.

That’s not really how plants work.

Photosynthesis turns co2 + water into sugar + oxygen. Cellular respiration turns sugar + oxygen into co2 + water.

The total co2 absorbed by a plant is exactly equal to the amount of co2 used to make all the sugar, cellulose, etc. the plant currently has. Digestion, decomposition, fires etc. undo that.

A mature forest or lawn is carbon neutral: new growth is balanced out by decomposition of old growth.

Distance to plants doesn’t matter. What matters is if and how the trees they’re burning are being replanted or replaced. .

That really depends on what his particular diagnosis is - are these mini strokes, seizures, or something else?

If he’s having seizures, that doesn’t mean he needs to be in a home.

For what it’s worth, a chain is a literal standardized metal chain that surveyors used when physically staking out parcels. It’s not a unit normal people have ever used.

An acre is a chain by a furlong because a furlong is the distance you’d plow with an ox, and an acre is about the area you’d plow in a day. They derived the standard chain from that, much as metric chains are 20 meters or 30 meters. France used to use 10 meter chains, with 20cm links.

Normal people don’t measure things in chains, whether metric chains or imperial.

It’s a simple idea, but it’s not quite that simple.

When it gets to be around freezing outside, you have to deal with frost buildup on the outdoor unit.

And as temperatures fall, output and efficiency generally falls. So you need an oversized unit to heat your house on the coldest days, but an oversized unit isn’t great the rest of the year.

Historically, heat pumps were only good if it never got down below freezing. Now, modern cold- climate heat pumps are efficient well below freezing and Mitsubishi’s models advertise that they deliver 100% of their output down to -23F/-30C. Between adding variable inverters, better defrosting, etc they’ve come a really long way in the past decade.

It’s not necessarily a matter of fact checking, but of correcting for systemic biases in the data. That’s often not the easiest thing to do. Systems run by humans often have outcomes that reflect the biases of the people involved.

The power of suggestion runs fairly deep with people. You can change a hiring manager’s opinion of a resume by only changing the name at the top of it. You can change the terms a college kid enrolled in a winemaking program uses to describe a white wine using a bit of red food coloring. Blind auditions for orchestras result in significantly more women being picked than unblinded auditions.

Correcting for biases is difficult, and it’s especially difficult on very large data sets like the ones you’d use to train chatgpt. I’m really not very hopeful that chatgpt will ever reflect only justified biases, rather than the biases of the broader culture.

Assume the dataset will grow and eventually mimic reality.

How would that happen, exactly?

Stereotypes themselves and historical bias can bias data. And AI trained on biased data will just learn those biases.

For example, in surveys, white people and black people self-report similar levels of drug use. However, for a number of reasons, poor black drug users are caught at a much higher rate than rich white drug users. If you train a model on arrest data, it’ll learn that rich white people don’t use drugs much but poor black people do tons of drugs. But that simply isn’t true.