I'm genuinely confused by this -- how does it refute "the scientific method"? Yes, most of the time you're trying to fix broken shit, and labs are more like workshops than anybody acknowledges, but don't lab scientists "try changing one thing and see if that fixes it"?
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Replying to @s_r_constantin
Ah, hmm, may be multiple disjunctures here… The point of the first tweet in the thread is not to *refute* “the scientific method” (it’s “more or less right, as far as it goes”) but to point out that there’s no overall formulation that is both nontrivial and empirically accurate.
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Replying to @Meaningness @s_r_constantin
So the question is “How actually do scientists gain knowledge, once we admit that there is no concise a priori answer? And how can we find that out?”
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Replying to @Meaningness @s_r_constantin
One obvious approach is to ask them “how did you determine this specific fact yesterday,” and then they launch into a story about chromatography columns and ethidium bromide or whatever.
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Replying to @Meaningness @s_r_constantin
Then instead of trying to turn that story into a tidy morality fable about The Scientific Method, you can take it seriously in its own terms. What specifically *is* the logic whereby that experiment shows protein A regulates protein B.
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Replying to @Meaningness @s_r_constantin
Another thing you can do is to hang out in labs watching scientists do science. Then what you see is “shop work” that is almost perfectly dissimilar to the fables you are taught in HS/undergrad about how science is done.
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Replying to @Meaningness @s_r_constantin
The actual work is mostly improvisational futzing around with materials and equipment, trying different things out, trying to coax them to produce an answer. And when you do that, you run into the “contingencies” Garfinkel enumerates.
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Replying to @Meaningness @s_r_constantin
Phil’s insight was that the contingencies are constraints on the form of a cognitive architecture.
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Replying to @Meaningness @s_r_constantin
E.g. if you assume knowledge consists of datastructures representing fopc wffs, you inevitably hit a combinatorial explosion. So we applied modus tolens, and concluded that knowledge can’t be datastructures or wffs or anything like that. Our program Pengi did fine without them.
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Replying to @Meaningness
ah, the thing where you can't proceduralize a scientist. (or an engineer or mechanic for that matter.) yes, that's quite true.
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Right! Although, if you say “you can’t proceduralize X” to a rationalist, they’ll launch into explaining Church-Turing to you, and will not listen when you try to explain how that’s irrelevant…
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Replying to @Meaningness
because it's at an unproductive level of abstraction, right? Church-Turing also says that there's a way to translate a rock into code, but we're not going to find it. We have read-access to human thought at a higher & more usable level of abstraction.
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