Then why don’t we use any Indian names for the very many famines in India due to British occupation?
Do you mean dramatized names like the Great Bengal Famine? The Bengali name is “Chiẏāttōrēr mônbôntôr (lit. 'Famine of ‘76’)”, which is pretty vague given how many famines have happened in the world. Probably it merits the fancier name because it was the first one under british rule. Or did you perhaps mean the Doji bara / Skull Famine (bengali: lit ‘many skulls’), which you know, not very dramatic at all and a pretty fair example of us using the indian name.
Demographic extrapolations and comparative economics.
Hi! I’m a data scientist specializing in public health data modeling and I’m sorry, that was a little mean of me to bait you like that, it’s a trick question: proving lives saved is the classic example of bad statistics and proving negatives. The assumptions required to make a definite statement about lives saved in a historical event are easy to make, but are necessarily so restrictive that they render any conclusions valueless unless you have definite conditions within a narrow time scope (like in a vaccine rollout or cholera outbreak). That’s why meaningless phrases like “Demographic extrapolations and comparative economics” are such an easy thing to parrot - you’re just saying “and then we do statistics, QED” without having to engage with the actual difficult part (the math).
Does comparative economics correlate to deaths? Sure! It correlates to just about everything you could ever want! The most famous example is the hemline index, which has spurred over a century of debate as to the actual causal connections (and if the theory itself even has merit). But proving that causal link to lives saved? Now that’s a damn tricky problem, and some really promising methodology has only recently arisen from the management of ventilator shortages during covid in the US (and it’s still being developed!) I highly recommend looking into it, it’s a fascinating field of research right now.
Edit: Wow, you know what, I’m gonna just point to the entire sections of the wikipedia article you got that graphic from titled “Population Decline” and “Fertility and natalist policies” to address the population decline, instead of just redundantly addressing all the… uh… rigorously cited claims you just laid out.
Or did you perhaps mean the Doji bara / Skull Famine
Hmmm. Fair enough. Now let’s do an exercise: let’s go to lemmy.world search, and look for the words “skull famine”, see how many results we get. Oh, we get exactly 2 results containing the words “skull famine”, two copypastas from 2 years ago which are simply a list of western atrocities. I wonder why a famine in India with 10+ million deaths has only 2 results in lemmy.world… Compare that to the search of the word “holodomor”. My point stands, doesn’t it?
That’s why meaningless phrases like “Demographic extrapolations and comparative economics” are such an easy thing to parrot - you’re just saying “and then we do statistics, QED” without having to engage with the actual difficult part (the math)
Good that you’re a data scientist specializing in public health data modeling! Will be interesting. The thing is, you can easily do these studies for the particular case of the transition to capitalism, because you can use many metrics: alcohol consumption, violent crime statistics, drug use, deaths from certain diseases, expenditure in healthcare, number of suicides… etc. You can take all of those metrics and see how they all vastly increase in the transition to capitalism. Sure, if it were just one of those metrics, then you maybe would be able to say it’s because of another reason, but when all of these metrics consistently rise sharply during a horrifying economic crisis byproduct of capitalism in several post-soviet republics at the same time, you can quite confidently both calculate numbers, and blame them on capitalism. As a matter of fact, this has been done widely for modern capitalist Russia, with this study talking of 3.5 million probable deaths between 1990 and 1998 alone, and this other study by Paul Cockshott reaching the figure of 12 million excess deaths between 1986 and 2008, though this latter one using much simpler methodology. Similar studies can be carried out for Ukraine, which suffered even harder since the crisis took longer to recover, and either way the numbers point towards the millions. And this is only excess deaths, not including lack of childbirth and economic migrations, both also counting in the millions.
Do you mean dramatized names like the Great Bengal Famine? The Bengali name is “Chiẏāttōrēr mônbôntôr (lit. 'Famine of ‘76’)”, which is pretty vague given how many famines have happened in the world. Probably it merits the fancier name because it was the first one under british rule. Or did you perhaps mean the Doji bara / Skull Famine (bengali: lit ‘many skulls’), which you know, not very dramatic at all and a pretty fair example of us using the indian name.
Hi! I’m a data scientist specializing in public health data modeling and I’m sorry, that was a little mean of me to bait you like that, it’s a trick question: proving lives saved is the classic example of bad statistics and proving negatives. The assumptions required to make a definite statement about lives saved in a historical event are easy to make, but are necessarily so restrictive that they render any conclusions valueless unless you have definite conditions within a narrow time scope (like in a vaccine rollout or cholera outbreak). That’s why meaningless phrases like “Demographic extrapolations and comparative economics” are such an easy thing to parrot - you’re just saying “and then we do statistics, QED” without having to engage with the actual difficult part (the math).
Does comparative economics correlate to deaths? Sure! It correlates to just about everything you could ever want! The most famous example is the hemline index, which has spurred over a century of debate as to the actual causal connections (and if the theory itself even has merit). But proving that causal link to lives saved? Now that’s a damn tricky problem, and some really promising methodology has only recently arisen from the management of ventilator shortages during covid in the US (and it’s still being developed!) I highly recommend looking into it, it’s a fascinating field of research right now.
Edit: Wow, you know what, I’m gonna just point to the entire sections of the wikipedia article you got that graphic from titled “Population Decline” and “Fertility and natalist policies” to address the population decline, instead of just redundantly addressing all the… uh… rigorously cited claims you just laid out.
Hmmm. Fair enough. Now let’s do an exercise: let’s go to lemmy.world search, and look for the words “skull famine”, see how many results we get. Oh, we get exactly 2 results containing the words “skull famine”, two copypastas from 2 years ago which are simply a list of western atrocities. I wonder why a famine in India with 10+ million deaths has only 2 results in lemmy.world… Compare that to the search of the word “holodomor”. My point stands, doesn’t it?
Good that you’re a data scientist specializing in public health data modeling! Will be interesting. The thing is, you can easily do these studies for the particular case of the transition to capitalism, because you can use many metrics: alcohol consumption, violent crime statistics, drug use, deaths from certain diseases, expenditure in healthcare, number of suicides… etc. You can take all of those metrics and see how they all vastly increase in the transition to capitalism. Sure, if it were just one of those metrics, then you maybe would be able to say it’s because of another reason, but when all of these metrics consistently rise sharply during a horrifying economic crisis byproduct of capitalism in several post-soviet republics at the same time, you can quite confidently both calculate numbers, and blame them on capitalism. As a matter of fact, this has been done widely for modern capitalist Russia, with this study talking of 3.5 million probable deaths between 1990 and 1998 alone, and this other study by Paul Cockshott reaching the figure of 12 million excess deaths between 1986 and 2008, though this latter one using much simpler methodology. Similar studies can be carried out for Ukraine, which suffered even harder since the crisis took longer to recover, and either way the numbers point towards the millions. And this is only excess deaths, not including lack of childbirth and economic migrations, both also counting in the millions.