On the Fediverse also as @mapto@qoto.org

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Abito in Italia @mapto@feddit.it

  • 3 Posts
  • 14 Comments
Joined 11 months ago
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Cake day: January 12th, 2024

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  • Martín@lemmy.worldtoData Is Beautiful@lemmy.mlCost by Protein Source
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    6 months ago

    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.






  • 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.