This is the Quality Contributions Roundup. It showcases interesting and well-written comments and posts from the period covered. If you want to get an idea of what this community is about or how we want you to participate, look no further (except the rules maybe--those might be important too).
As a reminder, you can nominate Quality Contributions by hitting the report button and selecting the "Actually A Quality Contribution!" option. Additionally, links to all of the roundups can be found in the wiki of /r/theThread which can be found here. For a list of other great community content, see here.
This month we have another special AAQC recognition for @drmanhattan16. This readthrough of Helen Joyce’s Trans: When Ideology Meets Reality garnered several AAQC nominations throughout the month:
Part 1 – The History of Transgenderism
Part 2 – The Causes and Rationalization of Transgenderism
Part 3 – How Transgenderism Harms Women And Children
Part 4 – How Transgenderism Took Over Institutions And How Some Women Are Fighting Back
Part 5 – Conclusion and Discussion
Now: on with the show!
Quality Contributions Outside the CW Thread
Contributions for the week of December 26, 2022
Contributions for the week of January 2, 2023
- "The Penfield Mood Organ and Me: Are We Already Transhuman by Chemistry and Mnemonics Rather than Engineering?"
Contributions for the week of January 9, 2023
Contributions for the week of January 16, 2023
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"Since the war has started, Ukraine has gotten not only increased aid, but increased attention and various oversight mechanisms."
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Notes -
I am not. We call it "random sampling" or "spot checking" in the professional context I inhabit (my role is dev / analyst (/ product manager / customer support / designer / qa / etc... can you tell I work at a small company?))
I do, in fact, do some fraud detection as part of my nebulously defined job responsibilities, though. For that my favorite heuristic is actually
Pick a metric. Any metric. The stupider the metric sounds, the better. If you're running a marketplace, "fraction of orders with a positive subsequent review from the customer" is a good metric, but "average time from order to shipping label printed" might actually be better by virtue of not particularly sounding like it points at anything valuable.
Rank all users by that metric.
Take the bottom and top 5 users (with a substantial amount of account activity) by that metric.
Most of those 10 users are probably trying to defraud you.
Ah, very cool. Sampling is a big part of auditing, as is directionality: vouching, for example, goes from final answer back to source documents, while tracing works the other way, from source to final, in order to verify existence and completeness respectively.
General auditing is directed more toward finding error than fraud, but forensic stuff interests me quite a bit.
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