The Cult Of Diversity & The Corruption Of Science: #ShutDownSTEM

By the logic being used here, the NBA needs to let short white and short Asian people play on every team so that way they can have a chance to be in the NBA. So the NBA can be completely mediocre. Just like they want everything else to be. Lets not appreciate people for their talents. Lets appreciate people for being whatever people they are and give them the status of having the talents. That way we can make minimal advancements, if any, instead of great steps forward for ALL of humankind. That’s what they seem to be working towards. This cult of diversity as you put it.

One of the sadder displays on “Science Twitter“ is those against whom the GRE plots asking to be added as authors to forthcoming prominent papers and the original authors occasionally pledging not only to add them but to list them first. Isn’t this perhaps a sign that academia has died and that the buzzards are out rather than that academia is alive and well but being preyed upon by eagles?

Thanks for again trying to articulate sanity, although it’s nearly impossible today.

By coincidence, I just came across these sources addressing the definition of “racism” today: https://nypost.com/2020/06/09/merriam-webster-will-update-definition-of-racism-after-a-request/ . Of course, if some racial group is superior, others are inferior.

The Roman Catholic Bishop Olmstead of Phoenix delivered this homily on the sin of racism on June 8: https://dphx.org/father-forgive-the-sin-of-racism/ . He adds “unjust” which justifies calling it a sin.

I wish more people using the “R” word would at least try to define it, as its meaning continues to shift.

I’ve been thinking the same thing about Zimbabwe over the recent events. Do people know what the black people did to white farmers? They sought revenge for the sins of others on innocent white men.
That’s black justice.
…
True Justice is blind.
I don’t know if she’s blonde, but blondes do have more fun, evidently.
Regarding statues:
You have one in New York that’s a bit old now. She’s suggesting something about equality and ‘bring me your tired people and them with sore feet…..” or something. I’d like to see people try and take that down. Perhaps start an outrage group from the right? Blame the French! You people backed the wrong horse.

Enough with the silly talk about science. It’s sour grapes.
How do you think they made it back to the moon with space suits like Buzz lightyear?
THAT was cool. I say sack all the scientists just keep the engineers. They have more fun too

Okay, nearly the moon. Maybe next time?

Science is dead eh?

Why?
Because a sizable number willfully corrupt it, or merely attach that label on something else to give an aura of credibility, or make honest mistakes, or cannot distinguish between credible work and incomplete or flawed or corrupt work.

When the practitioner errs or even corrupts the use of the tool, the problem is the tool when the tool is science. Not the practitioner or propagandist.

But when the practitioners of statistics, another intellectual tool, keep doing it wrong — miss-applying or using at all p-values, for example, the problem is not “statistics but the practitioner. Even as we simultaneously learn that the discipline of statistics itself is the source and provider of defective tools like p-values.

The problem is assigned where we want, not where the evidence really points.

Sheri
We all know what you meant, so I’m being completely pedantic.
It’s just you’re confusing your fascists.
Hitler did have a diversity of science problem because he chased the best and the brightest out of science because they were not the right kind of people.
Hitler very much needed science to help shore up resources that he was lacking. Hitler had lots of scientists working on lots of “solutions” to those lack of resources; unfortunately for him, those resource problems meant that he had to back the right horses.
No, you’re thinking of Stalin and Lysenko, who despite failure after failure trusted the “science” they were pursuing and reality fell to the back

10/10 to Ken and a Gold star.
It’s a piece of work for a statistician to untangle. A challenge of a lifetime.
Maybe do like the celebrities do, dress up like batman and handglide off the Empire State Building. To raise awareness of the P value.
Better still, print T-shirts. Give them out for free at your conferences and secret meetings.

I AM of course completely ignoring Hitler’s version of Medical Science and THAT is a case you COULD make, but that’s for another day

Ironically, the higher education system in communist china is a merciless meritocracy.

Anyone that thinks science is there to extend their lives needs to look
at the history of the eugenics movement from Malthus to Gates. The
original model of population control by setting the masses at each others
throats in one endless war upon the next having failed, despite 20th
century world conflagrations killing tens of millions, was replaced by
corporate medical and nutritional science. The two pillars of the eugenics
movement today are the emphasis on carbohydrates in the food pyramid
shoved down our throats from cradle to grave and the introduction of
processed hydrogenated vegetable oils and the concomitant demonizing
of saturated animal fats. These two highly publicized and medically endorsed
movements have probably killed more people than all the wars in the last five hundred years. The exponential increases in cardiovascular disease and
obesity throughout the 20 century are the only scientific facts you need to
know. A multi-billion dollar and deadly Statin drug industry has been built
around them and thrives today.
Empirical Research on Research and the Reproducibility Crisis – J. Ioannidis – 4/13/2016
https://www.youtube.com/watch?v=MO30X0YlZU0

I did not know this until recently: past ‘redlining’ not only had, but also continues to have, deleterious effects on the economic, psychological, and physical well-being of black Americans. For example, historical redlining helps to account for the higher present-day rate of COVID-19 infection among them.

However, discriminatory lending practices based on race rather than economic considerations (“redlining”) has a very shaky factual foundation. Indeed, the seminal 1992 Federal Reserve Bank of Boston study that claimed to discover racial discrimination in lending practices refuted itself. (Yes, really).

1.
The 1992 Boston Fed study itself noted that according to census tract data, the default rates of blacks and whites who actually received loans were equal.

https://pdfs.semanticscholar.org/f602/f9bbaacd93dc23579ff455ed9e159e27d79e.pdf

This should have been the decisive point. It proved that the banks were assessing the risk of lending accurately, and lending to applicants without regard to race. Racism in lending would have been demonstrated if the black default rate were generally lower than the white default rate on the loans made. This would have proved that the banks were “leaving money on the table” and spitefully denying loans to equally-qualified black applicants.

Instead, the Fed focused on the differing rejection rates of black and white loan applicants. The Fed deployed statistical models, which the Fed claimed “controlled” for all possible financial factors that might have explained the difference in rejection rates. After applying these adjustments, the Fed found a still-unexplained difference in loan rejection rates: blacks were still being rejected for loans (17% rejected after all confounders were eliminated) more often than whites (11%). This unexplained difference in loan rejection rates, the Fed study stated, was due to systematic racism among the banks in the study.

But in fact, the equal default rates that the Fed itself reported, proved that the people who actually got loans, black or white, were both equally reliable at paying them off. Even if the banks were rolling dice or looking at a Ouija board — whatever the banks were doing — they were getting it right, and being race-blind in their lending.

The Boston Fed made at least two fundamental errors. The reality of the actual census tract data was not as important or decisive to its analysts as generating models that expressed their own ideas (their ‘narrative’, if you will) about how banks ‘ought’ to make lending decisions. And they did this because they appeared to think that simply describing what actually happened (a process greatly distinct from predicting what future data might look like) requires elaborate statistical modeling. The Fed appeared to believe that simply looking at what happened — particularly if you suspect a difference, but don’t find one — can be extremely deceptive and fraught with errors. This appears to be a common belief among researchers.

2.
In the January 4 1993 issue of Forbes magazine (link unavailable), Boston Fed Senior Vice President and Research Director <a href="https://en.wikipedia.org/wiki/Alicia_Munnell"Alicia H. Munnell, the lead author of the 1992 study, acknowledged to two Forbes magazine reporters that the equal default rates of the black and white loan applicants who actually received loans did not exactly demonstrate that racial discrimination in lending existed. She then said:

Munnell: “I do believe that discrimination occurs.”
Forbes: “You have no evidence?”
Munnell: “I do not have evidence. … No one has evidence.”

3.
By 1996 Raphael W. Bostic, Division of Research and Statistics of the Federal Reserve, directly contradicted the 1992 Boston Fed study:

https://www.federalreserve.gov/pubs/feds/1997/199702/199702pap.pdf

“This paper reexamines claims that non-economic discrimination persists in mortgage loan origination decisions. … [T]he claim that non-economic discrimination is a general phenomenon is refuted.”

4.
In 1998, Economists and Theodore E. Day Stan L. Liebowitz examined the raw data from which the 1992 Boston Fed study drew its conclusions. They wrote:

https://personal.utdallas.edu/~liebowit/mortgage/mortgages.pdf

“This paper examines mortgage lending and concludes that studies based on data created by the Boston Fed should be reevaluated. A detailed examination of these data indicates that irregularities in these data [for instance, Day and Liebowitz found seven loans included in the Boston Fed’s analysis in which the expense to income ratio was reported as zero, an impossibility, Ed.], when combined with the most commonly used research methodology, appear to have biased previous research toward a finding of discrimination against minority applicants. When the most severe data irregularities are eliminated, evidence to support a hypothesis of discrimination disappears.”