For anyone else wondering:

Female fingerprints typically contain more densely packed ridges than male prints in the same area. These measurements were then compared against ridge density patterns found in contemporary Egyptian populations. … The sex could not be determined for children.

For anyone else also interested, I went and had a look at the links Dessalines kindly provided.

The source on the graphs says “Sources: Daniel Cox, Survey Center on American Life; Gallup Poll Social Series; FT analysis of General Social Surveys of Korea, Germany & US and the British Election Study. US data is respondent’s stated ideology. Other countries show support for liberal and conservative parties All figures are adjusted for time trend in the overall population.” Where FT is financial times.

It’s not clear how the words “liberal” and “conservative” were chosen, whether they’re intended to mean “socially progressive” and “socially traditional” or have other connotations bound with the political parties too, and whether the original data chose those descriptions or if they’re FT’s inference as being “close enough” for an American audience.

Unfortunately the FT data site is refusing to let me look at them without “legitimate interest” advertising cookies so I can’t tell you much more or if there’s any detail on methodology.

This list puts US at ~297m English speakers which is the largest group from one single country, that is true. But 297m / 1,537m = The US has 19.35% of English speakers globally.

You are likely also greatly underestimating current internet connectivity, older smartphones have changed things for poorer countries a lot over the past decade. For example, India has only 62.6% of people as internet users - but that’s still 880m people and probably most of their 125m English speakers. Nigeria has 63.8% internet users, but that’s 136m internet users. And they also have 125m English speakers, who again, are more likely to be the people who can afford an English education, and also a smartphone. And then there’s Pakistan with another 100m English speakers and 70.8% internet users, etc.

Just 3 countries, (2 of which were 1 country 80 years ago) and you’re close to that 300 million count already.

The list also gives US as 92.4% internet users, for what it’s worth. A little less than 97% and not even in the top 20 countries by percentage, which is surprising.

The internet is less American than ever. It’s just that most non-American people probably have non-English language spaces they can choose to gather in addition to the English-dominated spaces. Americans, on the other hand, are more likely to be monolingual English speakers and so they concentrate in the English-dominated spaces.

And non-Americans are all so used to people assuming American defaultism in English-dominated internet spaces because it was historically hugely expensive to get online and was overwhelmingly American English-speaking, that it’s not even worth correcting when it happens the millionth time.

I’ve also put non-metric and US currency conversions in posts online many times. Not because I’m American or use them in daily life. It was just less annoying to convert them when writing rather than hear the inevitable multiple complaints about not understanding things in meters and dessicated jokes like “that’s probably $2 in real money”.

You’re either overestimating the accuracy of your assumptions about your online interactions and/or seeing selection bias from your immersion in otherwise culturally isolated spaces.

List of sources quoted in this list of "push back:

• A group called “Republicans against Trump” which seems to just be this twitter account with a buymeacoffee link which posted “Republicans could choose this man to represent them, instead they preferred a corrupt racist buffoon with no morals. The party deserves a humiliating defeat in 2020.” In May 2020, making me suspect they’re not actually Republican or possibly a group at all.
• “A Republican accountability group called American Bridge 21st Century” which is described on Wikipedia as “a liberal American Super PAC that supports Democratic candidates and opposes Republican candidates.”.
• The American Federation of Labor and Congress of Industrial Organizations
• A “popular liberal commentator”
• A Democratic Representative
• The White House political director

So if you were hoping for actual consequences from his base or even just someone new and noteworthy criticizing him, this is not the article for you. I’m glad the Trade Unions are going to spread the word though, that will be a good thing.

How convenient that a counterexample can’t be named

I feel like Luthor was a better counterexample for this before the model for his billionaire redesign was elected President of the USA.

Even so, Luthor hasn’t had quite the same volume of appearances as Iron Man, Batman, Captain America and the other rich superhero tropes.

People have grown up reading comic books and watching movies about generous billionaire superhero saviors. They want to believe that exists because it’s what they’ve been taught justice looks like.

He’s still a party member, it’s listed in his candidate information sheet. Badly Scanned PDF

References weren’t paywalled, so I assume this is the paper in question:

Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature (2024).

Abstract

Hundreds of millions of people now interact with language models, with uses ranging from help with writing^1,2 to informing hiring decisions³. However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans^4,5,6,7. Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement^8,9. It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Some provide screen-reader instructions, but most places barely remember blind people exist. It’s another example of people with disabilities being ignored and marginalised.

And then even if they do remember blind people exist, they probably forget there are people who aren’t blind who can’t do their tests for other reasons, like dyslexia or dexterity impairments.

And then you have hCaptcha who makes disabled people to sign up to their database to use their cookie.