Martín

I think this dynamics shows up with masto as well. When the response on the other service does not tag the community, it doesn’t arrive here.

Thanks. I didn’t know this and it is very useful information.

Even if so, your unreasonably pessimistic assumption is that this would be an exclusive source of revenue. Once content is created, cross-posting is free.

Thanks for doing the maths. Actually, it does show that there’s a small, but unexploited market here. $2-3K a month is a very good income for the most of the world. And this doesn’t have to be the only revenue stream.

Could you elaborate, please. I’m genuinely interested

I guess you misunderstood my providing illustrative examples in parentheses. Replace or remove the examples, the argument is still valid.

In another subthread they’ve pointed out that processing food also changes its protein density, most obviously by water transfer.

This is not a problem with the nutrition of foods, it is the metric that is poorly designed. One more argument against the chart

Your seem to insist to twist this towards vegan wars, but this is you. It’s not the graphics, it’s not me.

What’s wrong with reducing density through absorption (of water)?

To me it seems that your interpretation completely disregards the Y-axis. On the other hand, I wouldn’t think the colour coding does a good job in separating along the carnivorous-vegetarian-vegan scale.

So much wrong about this chart. It is factually correct, but it answers the wrong question.

This chart makes it way too easy to optimise for cheap protein, which is misleading. It is not this what it takes to have a healthy organism. It takes a varied diet, with balanced quantities of liquids (see milk), vitamins (see sprouts), fatty acids (see salmon), minerals (see shrimps, eggs, walnuts), actually carbs (potatoes, rice, spaghetti), and much more…

I keeps amazing me how one could criticise capitalism and still talk exclusively in terms of capitalism.

Not a single word of the accelerating extreme deforestation of the world’s forests all over the planet. And this is just an example. The same holds about drilling and plastics, about industrial farming, construction,… I don’t care if they are profitable. They’re just aggravating the problem and there are alternatives that reduce the problem. These need to be enforced, regardless whether they are profitable (some of them are, but they still don’t overtake the problematic ones). We don’t have collective enforcement and we need it. Call it green new deal if you want, call it anarcho-communism, whatever. As long as it is just theory and no practice, it’s pointless.

Politics and growth are irrelevant if they are so detached from the problem.

Let’s say that for millions of years a healthy biosphere grew around forests and the balance worked. Now you come to tell us it doesn’t. Wouldn’t you think it’s a bit unconvincing?

I wonder why forestation is not present in this chart, as it is a low-cost carbon capture with side benefits. Sure, it is hard to scale, but reducing current deforestation rates would be a big step.

Why would the logs be emitting CO2 (rotting?) if they are alive and growing?

https://github.com/pgp/XFiles is what I’ve been using and am pretty happy about it

I work in the Digital Humanities and my experience is that typically Computer Science, Information Science and Data Science are not well prepared to work with Humanities data. Some commonplace challenges:

• the methodologies used in the humanities like semiotics, phenomenology, etc. often do not allow for the level of formalisation that a computer science model would require
• (probably a consequence of the above) data in the humanities is rarely quantitative and much more often qualitative, i.e. nominal and categorical if structured at all. That’s why for example a lot of attention is paid recently to language models, but repeatedly we find out that these have undesirable (inadequate) biases
• a particularly big issue is that historical data is much more scarce than data scientists would like, and often it is not digitised or digitised with poor quality. As a consequence established machine learning approaches cannot be trained

There’s much more to it, but these are the most immediate challenges that come to my mind.