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 →
Mike Curtis no published score: no usable exchanges 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.
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q we're essentially making emotions more machine readable, and that's a really useful thing, but you don't get those signals, actually. Cause you're talking about people both in the physical world and maybe, I don't know if you guys actually have the range of emoji, not just a star for how you liked or not like something or pinned or not pin something. How do you guys think about that dimension?
A No, I, I think, I think there's actually a lot of facets to this. Could be like, you know, I'm looking for something that's a little bit more secluded and out of the way, or I'm actually looking for something that's like in the middle of the nightlife. And like, we can start picking up on those signals again, based on like how you're searching and browsing through it. And then, so that's sort of at the front end, but then, you know, all the way at the other end, we can look at, again, with that review information, there's some of it that is structured, right? Like give a star rating, but then there's also the content of the review itself. So we can do sentiment analysis. And, you know, some natural language processing on that to sort of suss out which aspects of this, like what feelings did it evoke? You might've given it a five on cleanliness, but maybe you felt like, oh, it wasn't really like the right neighborhood.
AI assessment note: “we can do sentiment analysis. And, you know, some natural language processing”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q we're essentially making emotions more machine readable, and that's a really useful thing, but you don't get those signals, actually. Cause you're talking about people both in the physical world and maybe, I don't know if you guys actually have the range of emoji, not just a star for how you liked or not like something or pinned or not pin something. How do you guys think about that dimension?
A No, I, I think, I think there's actually a lot of facets to this. Could be like, you know, I'm looking for something that's a little bit more secluded and out of the way, or I'm actually looking for something that's like in the middle of the nightlife. And like, we can start picking up on those signals again, based on like how you're searching and browsing through it. And then, so that's sort of at the front end, but then, you know, all the way at the other end, we can look at, again, with that review information, there's some of it that is structured, right? Like give a star rating, but then there's also the content of the review itself. So we can do sentiment analysis. And, you know, some natural language processing on that to sort of suss out which aspects of this, like what feelings did it evoke? You might've given it a five on cleanliness, but maybe you felt like, oh, it wasn't really like the right neighborhood.
AI assessment note: “we can do sentiment analysis. And, you know, some natural language processing”
