The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Mark Kaganovich no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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1exchanges match
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Answered raw tape D 4 · C 4 · P 4 · Cm 4 4.00

Q Thank you, Mark. Um, so the, the, uh, fundamental driver why people in the clinical world and, and, and other parts of the ecosystem would want to work with a platform is that, um, it's not really better, better tools, better experience, because it's been very hard historically to make all those people work together, right? It's not just a question of format. Yeah.

A Right, exactly. So there are, there are, there are a number of factors. So when it comes to, Ingesting data, you know, there, there are, there is one approach where everything can be built vertically, so you take all the data that's available from the outside world, um, yourself, and maybe you make partnerships with people that provide that data, and, uh, up till now that's, in some sense, been the case. 23 Media has their own curation, et cetera, et cetera. Uh, but the way we kind of see it is that, that, that inflection point has already happened. It's somewhat akin to the microprocessor that forced a lot of horizontalization, de-aggregation of the industry. I think that's happening in genetics, and this is a great building to be in to talk about that, because that, in finance, that exact thing happened. I mean, the idea of, ah, a platform delivering data to an industry and making, ah, every player in the industry better, I think that's not, not too crazy when you're sitting here. Um, so that's, that's one of the motivations, yeah.

AI assessment note: “a platform delivering data to an industry and making, ah, every player in the industry better”

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