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 →

Kieran Snyder 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.

clear all ✕
1exchanges match
1on raw tape
0redirected or not addressed
Answered raw tape D 5 · C 4 · P 4 · Cm 4 4.30

Q Thank you very much. One quick question from me and then I'll pass it on to people. Um, how does the, how do you get the feedback loop? How do you know that this works?

A That's a good question. So we get it from our customers. So there's a couple of ways. Um, of course, fundamentally we get a lot of our data from applicant tracking systems. So, uh, applicant, I don't know how familiar people are with applicant tracking systems in the room, but large companies use them to track all the applicants who come through their job funnel. And so for every job that goes out, uh, they have pretty good intelligence on how many people applied, who got through which Stage in the funnel. What was the demographic mix of people who eventually got hired? So that's, that's our richest and best data. We do still use to corroborate some of that data, ah, some publicly available data sources, but it's really interesting. Like one of my favorite examples that I, I talk about, ah, you do sometimes, so you see industry trends, but you see sometimes the language that matters depends on the company. So Expedia is one of our customers. And it turns out you're, you know, looking for engineer for Expedia, yep, you want great, ah, pro-engineering vocabulary, but it turns out at Expedia in particular, travel vocabulary matters a lot. So, not surprisingly, if you're an engineer in the Pacific Northwest and you choose to work at Expedia compared to Microsoft or Amazon, maybe you're interested in travel. That might be something that motivates you. When, um, Mozilla job listings,…

AI assessment note: “fundamentally we get a lot of our data from applicant tracking systems.”

page 1
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.