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INGELRII

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Ed25519-CLI – command-line interface for the Ed25519 signature system (2024)

lib25519.cr.yp.to
97 分·作者 INGELRII·7个月前·53 评论

AI Slop Is Ruining Reddit for Everyone

wired.com
36 分·作者 INGELRII·7个月前·6 评论

Reanimation of pseudoscience in machine learning and its ethical repercussions

cell.com
8 分·作者 INGELRII·8个月前·1 评论

BBC tells staff they cannot quote Trump line removed from Reith Lecture

theguardian.com
33 分·作者 INGELRII·8个月前·5 评论

Money talks: the deep ties between Twitter and Saudi Arabia

theguardian.com
3 分·作者 INGELRII·8个月前·0 评论

Unraveling of workplace protections for delivery drivers:Amazon vs. UPS model

theconversation.com
1 分·作者 INGELRII·8个月前·0 评论

Why Sustainable Civilizations Must Be Democratic

jmkorhonen.net
3 分·作者 INGELRII·9个月前·0 评论

mRNA Vaccines and Immuno-Oncology: Good News by Derek Lowe

science.org
23 分·作者 INGELRII·9个月前·2 评论

评论

INGELRII
·7个月前·讨论
There is idea behind that, but continuous is not enough.

The variable is all transfers, taxes and benefits T = [all taxes - all benefits] as function of income per person (including children). T starts negative (benefits are negative taxes).

Goal: monotonously increasing effective marginal T rate.
INGELRII
·9个月前·讨论
The productivity paradox (also the Solow computer paradox) is the business process analysis observation that, as more investment is made in information technology, worker productivity may go down instead of up. This observation has been firmly supported with empirical evidence from the 1970s to the early 1990s.

Before investment in IT became widespread, the expected return on investment in terms of productivity was 3-4%. This average rate developed from the mechanization/automation of the farm and factory sectors. With IT though, the normal return on investment was only 1% from the 1970s to the early 1990s.

https://en.wikipedia.org/wiki/Productivity_paradox

Measurement or Management?: Revisiting the Productivity Paradox of Information Technology. http://www.diw.de/documents/publikationen/73/38739/v_00_4_9....

Then in the 2000 to 2020s productivity slowdown aka productivity paradox 2.0. https://en.wikipedia.org/wiki/Productivity_paradox#2000_to_2...
INGELRII
·10个月前·讨论
Always visualize first. Human 'eyballing' is a good pattern detector.

Linear correlation is just one pattern the data can have.

Unfortunately many social science publications have reviewers who know only the basics and can't judge or accept statistically valid analysis that is outside their competence. Fit it into line or nothing.