It’s hard to believe that scientists would deliberately manipulate their findings. The risk of getting caught and discredited is just too high – oh wait.
Thanks for the feedback, the examples you cited are really cool. I didn’t know about akinetes but I’m reading more about them now. For the general point of the article, they might actually be too good examples of multicellularity: there is a pretty strong case that these are multicellular organism by the usual definition. What I wanted to emphasize here is that, even for species like E. coli that are most definitely not multicellular, we can still force ourselves to look at it through a multicellular lens and find interesting things. I agree that it’s awkward to change the definition of a technical term as liberally as I did, so I’ll try to see if I can come up with a better phrasing.
I might be misunderstanding this, but it looks like humans would either:
Suppress T-cells and B-cells that react against transposase at the negative selection step during maturation, making vaccination impossible
Already be immune to the antigen (maybe that would be possible if the transposon is expressed very rarely, otherwise it would be recipe for auto-immune damage)
Full disclosure: I’ve forgotten everything about immunology since ~5 minutes after my last immunology exam.
I think this works well to describe the behavior of small, well-mixed groups, but as you look at larger societies, it gets more complicated because of the structure of social networks. You don’t get to see how many people overall are wearing face-masks in the whole country, only among the people you interact with in your life. So it’s totally possible that different equilibria will be reached in different locations/socio-economic classes/communities. That’s probably one reason why revolutions are more likely to fizzle out than it looks. Another problem arising from the structure of social networks is that the sample of people your interact with is not representative of your real surroundings: people with tons of friends are over-represented among your friends (I had a blog post about this statistical phenomenon a while ago). I’m not sure how one could expand the social behavior curve model to account for that, but it would be interesting.
2. The survey report you link to includes the following figures: (1) about half of all respondents in their survey who had experienced >= 4 instances of discrimination and violence in the last year attempted suicide in that year; (2) among all respondents in their survey, 7.3% attempted suicide in the last year. To me, that looks as if suicide rates among trans people are much more to do with actually being treated badly than with fearing they will be treated badly. (If so, I am cautiously optimistic that those terrible trans activists trying so hard to raise awareness of transness and reduce the extent to which trans people are regarded as strange and sinister are in fact making it less likely that any given trans person attempts suicide.)
Here is a possible counter-argument to this: if social pressure and discrimination cause suicides, we would expect the suicide rates of trans people to increase after hormonal treatment or surgery. After all, before transition, gender dysphoria is not particularly visible. From the point of view of most people who are not intimately familiar with the person, a pre-transition trans looks just like someone cis. After transition, however, they may or may not “pass”, and in many cases it is immediately obvious that they are trans (e.g. MtF still having a male voice unless they do the fancy vocal cords surgery). But we observe exactly the opposite: gender-affirming surgery greatly reduces the suicide rate of trans people.
Note that I don’t think that trans activists are causing the suicides either. My working hypothesis is that gender dysphoria (as in, not feeling at ease in your own body) is horrible by itself, and is the cause of suicides. Hormones and surgery might make the trans-ness more visible, but if it alleviates the mismatch between your body map and your actualy body, it might still be a net benefit.
This is both messed up and not surprising.
It’s funny because, in your farming example, we could also accuse Person B of non-central fallacy: torture and child abuse are just particularly extreme, non-central forms of cruelty, but most cruelty is more subtle – like a manager belittling employees. In some way, you could say that the center lies in the eye of the beholder. Perhaps the best way to deal with that is to always evaluate things in comparison to their alternatives: everything might involve some cruelty to some degree, but maybe there are easy ways to make farming comparatively less cruel?
That’s a bold statement! The wiki article has a  and that sounds wild. Typically, if height was heavy-tailed, we would expect the tallest person to be more than twice as big as the second tallest person. But then, Jeff Bezos is not twice as rich as Elon Musk, so it doesn’t always work...
As far as I understand, the tails coming apart and the moment attribution are two different, superimposed problems. The tails coming apart is “Nigeria has the best Scrabble players in the world, but the persons with the richest English vocabulary in the world are probably not Nigerian”. The moment attribution is “the best Scrabble players in the world are Nigerian, but Nigerians are probably not the best Scrabble players in the world”. In the first case, we are talking about the distribution of country scores for two correlated variables, in the second we are talking about the distribution of individuals within a country for a single variable.
Also, thank you for bringing up Nigerian Scrabble, that would have made a somehow funnier example than NK’s math olympiads.
Standford’s collection of ancient data visualizations
Complexity Explorables: interactive toys to learn about complex dynamical systems.
The current state of AIDS in the European Union
Evolution of word usage in Scientific American over the last 150 years
Which and how body parts are described in literature, according to gender
Heatmap of mortality rates over the last centuries, by age, country and genderhttps://jschoeley.shinyapps.io/hmdexp/
Historic usage of the word “ass”
https://pudding.cool/2019/10/slang/ (and anything by The Pudding really)
Simulated dendrochronology of USA immigration