Ryan Briggs ✓
@ryancbriggs · Toronto, Ontario · joined 11 Mar 2007
Raising kids & bread & grant money. Cleaning data & diapers & fish. EA (bed nets not light cone). Social scientist. https://t.co/g8teKfCf91
13 403Followers
1 535Following
18 912Posts total
338KViews on collected posts
Последние посты
Ищем новые посты…
As GiveWell scales, my team’s trying to figure out how to main research quality (by collecting tons of new on-the-ground data, red teaming/critiquing our work, using AI tools, and more). We’re hiring, too!
4.2K views · 20 likes
· 3 reposts · 1 replies
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@ryancbriggs Me too! I always think like an economist. https://t.co/j1etThC6pF
190 views · 2 likes
· 0 reposts · 0 replies
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@ryancbriggs You guys think like people? https://t.co/LQEt5kKYAR
231 views · 6 likes
· 0 reposts · 2 replies
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GIF
@ryancbriggs as long as you are being paid like one!
259 views · 4 likes
· 0 reposts · 1 replies
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you think that's bad? I think like an economist
6.1K views · 49 likes
· 2 reposts · 6 replies
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I realized today that I tend to think like a lawyer
This can’t be good
5.2K views · 13 likes
· 0 reposts · 8 replies
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IF ONLY A NEIGHBOURING COUNTRY PRODUCED A LOT OF ALUMINUM AND WANTED TO SELL IT TO AMERICANS
1.1K views · 34 likes
· 0 reposts · 1 replies
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The haskaps finally came in at my parent’s place! We made jam and haskap sour cocktails and a haskap pie is in the oven. https://t.co/aaKpCELJjy
438 views · 5 likes
· 0 reposts · 0 replies
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@ryancbriggs I don't believe this but in the ideal world where science is good and the filtering process near perfect and everything is cute kittens frolicking in gardens this is also the expected results
999 views · 6 likes
· 0 reposts · 2 replies
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The whole paper is here: https://t.co/4GSlgQvcZc. It's early work so we're very interested in comments! Note: I wrote the parts that you like and @jon_mellon and @VincentAB wrote the parts you did not like.
4.8K views · 64 likes
· 3 reposts · 5 replies
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It's ironic to see a discipline care **so much** about unbiasedness (causal inference!) at the level of a single test but then have a research production system and culture that is basically a ferocious bias generation machine. This is not good.
16.5K views · 211 likes
· 21 reposts · 5 replies
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Overall, I think our results are:
1. Believable. I think deep down we all know we publish "too many" significant results.
2. Disastrous. Selecting on significance in this way biases results across our whole literature away from zero & it stops us from learning what doesn't work.
5.4K views · 79 likes
· 5 reposts · 1 replies
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We coded our ~100k articles using LLMs. Should you believe them? To answer this, we benchmarked 4 human RAs against 3 LLMs on their ability to recover ground truth article data. Details in the paper and appendices, but the LLMs did well and handily beat the highly trained humans.
72.8K views · 149 likes
· 18 reposts · 6 replies
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When we look across journals, we see the same patterns repeated. The main exception is the Journal of Experimental Political Science, which has the highest rate of null-only reporting and lowest rate of rejection-only reporting. Kudos to them. https://t.co/hStNcbmcLA
7.5K views · 71 likes
· 6 reposts · 1 replies
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We can't say why this is happening, but we can descriptively look to see if it's better or worse in some corners of the discipline. It's similarly bad almost everywhere. The main exception is that pre-registered work looks better, but even there the filter is strong https://t.co/
5.9K views · 38 likes
· 3 reposts · 2 replies
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How extreme is this filter? We develop a simple publication model & we calibrate it using a mix of generous assumptions & available evidence. Across reasonable (generous) parameter values, null rejections are 1-2 orders of magnitude more likely to survive than are nulls.
5.8K views · 40 likes
· 0 reposts · 1 replies
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I have a new paper. We look at ~all stats articles in political science post-2010 & show that 94% have abstracts that claim to reject a null. Only 2% present only null results. This is hard to explain unless the research process has a filter that only lets rejections through. htt
198.1K views · 774 likes
· 186 reposts · 32 replies
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На фоне аккаунтов своего размера
9 постов за последние 90 дней рядом с диапазоном 10K–100K подписчиков. показывается широко, но откликается мало кто.
Медианные просмотры1 139этот аккаунт924медиана для 10K–100K
Охват, %8.50%этот аккаунт3.62%медиана для 10K–100K
Вовлечённость, %1.05%этот аккаунт1.52%медиана для 10K–100K
| Показатель | Этот аккаунт | Медиана для 10K–100K | Отношение |
|---|---|---|---|
| Медианные просмотры на пост | 1 139 | 924 | 1.23× |
| Охват (просмотры ÷ подписчики) | 8.50% | 3.62% | 2.35× |
| Вовлечённость | 1.05% | 1.52% | 0.69× |
Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
72.8K11 Feb
5.4K
16.5K
4.8K
999
4381 Sep
1.1K
5.2K2 Sep
6.1K
259
231
1903 Sep
4.2K4 Sep
2.3K5 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.24%11 Feb
1.59%
1.45%
1.49%
0.80%
1.14%1 Sep
3.07%
0.42%2 Sep
0.96%
1.93%
3.46%
1.05%3 Sep
0.61%4 Sep
1.05%5 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes65.3%1 587 in total
Reposts10.2%248 in total
Replies3.1%75 in total
Quotes1.7%42 in total
Bookmarks19.7%480 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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