5776
posted ago by -f-b-i- [M] +5777 / -1

Delete a tweet? No big deal. Fact check a fact? Who cares. Decide what is best for the country? Well that just makes sense. BULLSHIT!!! It is time to hold big tech accountable for their lies and manipulation of the truth. It is not the job of a publisher to be the orators of fact. PLATFORM OR PUBLISHER!!!

Links:


CSPAN

Senate

Ironically Youtube

Delete a tweet? No big deal. Fact check a fact? Who cares. Decide what is best for the country? Well that just makes sense. BULLSHIT!!! It is time to hold big tech accountable for their lies and manipulation of the truth. It is not the job of a publisher to be the orators of fact. PLATFORM OR PUBLISHER!!! Links: ___ [CSPAN](https://www.c-span.org/video/?476686-1/facebook-google-twitter-execs-testify-social-media-regulations) [Senate](https://www.commerce.senate.gov/) [Ironically Youtube](https://youtu.be/RueVNl4KaMc)
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GreatestAmericanEver 1 point ago +1 / -0

Men tend to think in curves, and women tend to think in straight lines. That might be a difficult visual to access without context, but the visual representation of the naXalt fallacy makes it pretty easy.

naXalt fallacy

You know what's funny - naXalt is actually multi-purpose - it also inadvertently tests for the presence of feminine cognition.

If I say to a typical woman "women are suckers for the naXalt fallacy" some percentage of women will, without fail, say "not all women are like that!", thus proving the validity of naXalt.

If you are interested in learning more about this topic, I highly recommend studying Curt Doolittle's work in the areas of cognition and the evolution of how consciousness is produced in the brain.

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WinterDog 1 point ago +1 / -0

Yes, the median defines the pattern... that was what I was saying. But patterns are a really inaccurate (although efficient) method of classifying things, especially when the pattern is not well understood or quantifiable. When two curves closely overlap and a candidate needs to be selected, the best candidate for a certain job could come from the median/outlier of one curve or the outlier of the other. The likelihood of either scenario depends on how many participants from each curve are considered and how close the medians of the two curves are. Since we can only broadly say that one curve's median is better suited than the other, but not quantify by how much or to what degree (and we do not even know the exact shape of those curves), it's impossible to determine with what confidence the best candidate has been selected if one curve is completely omitted.