In practice, now with ~5 substantive texts written in the medium, it's pretty consistent that ~2-5% of readers engage with the prompts; 25-50% answer ~all (very length dependent); around half of those do any reviews.
What are the implications for authors and their incentives?
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If you have thousands of readers, only a few tens might actually review your material over time. Writing those prompts takes a lot of effort—is it "worth it"?
It's an easier case to make for "platform knowledge" like Quantum Country, which can draw 100k's of readers.
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But of course "visitor" numbers are misleading. For every 100 unique visitors an article's analytics count, it wouldn't surprise me if 80 bounce without reading much and 10+ read shallowly. So maybe this is actually reaching most of the serious readers.
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I suspect it's hard for authors to think this way. It's unsettling to spend hundreds of hours writing something which only a few hundred people might engage with deeply. It's easier and more pleasant to think in terms of (inflated) visitor counts, copies sold, etc.
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Citation counts make this obvious in academic publishing. I suppose the mnemonic medium is just making the phenomenon clearer for non-academics. But this doesn't have to be sad! There's something very intimate about spending many many hours writing something for a few people.
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On another note: analytics from the mnemonic medium may be very interesting to authors. They can highlight when prompts are badly-written, but patterns show where people might be struggling to grasp material—and where they're getting bored.
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I'm also excited about the prospect of adding feedback mechanisms to prompts. The obvious benefit is for catching errors or confusions. But maybe useful to see what readers skip (if that mechanism is added) or how they remix prompts (if privacy settings permit).
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A different way to look at all this is to say: people forage when they read. Most people won't and shouldn't collect ~all prompts (except for platform learning), and the medium should offer an affordance to let people effortlessly indicate "I want to remember this bit."
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Would be interested to know if you think sophisticated NLP models will ever have a role to play in generating prompts. Seems it could bring spaced repetition and enhanced understanding to a wider audience if it was made easier for writers.
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Yes, probably. I've experimented here with little success so far. Polar has shipped a prompt generator based on GPT-3, but it has a long way to go before being very useful, I think.
ATM I'm focused on making the ideal case as powerful as possible, then I'll worry about scaling.
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