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Posted on 19-06-18, 11:45 in Something about cheese! (revision 1)
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Yes, I argue that by focusing on the most brilliant minds, you miss all the late bloomers. And those do exist. How many times have you heard this story?

"Yeah, so I was approached by this guy who wanted me to invest in their company. Scruffy looking college kid, heck he had even dropped out recently. Of course I didn't invest a penny. That person was Mark Zuckerberg, and his project was Facebook."

There are way too many anecdotes like that which tells me, no it's not enough to simply focus on the brightest and the best. Everyone benefits if all children could understand Calculus. Not just the academically bright.

Unfortunately there are no silver bullets, but hard, nit gritting painstaking work that is straight up undermined by armchair nay-saying scientist such as yourself. But hey, don't let me ruin your fun. :)
Posted on 19-06-19, 08:14 in Something about cheese!
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Zuckerberg may have been intelligent all his life and did not need the college education since he already knew everything he needed to know, yes.

This is one example of many, however. While Zuckerberg is the extreme end of the spectrum, there are plenty of geniuses and successful men with an average IQ. Heck, even Albert Einstein was overlooked at first glance, and was a constant underperformer until 1905.

I know it is possible to lift any school class out of the trajectory it is set on. But it requires the right guidance at the right time.

If you do not believe me, then perform this experiment. Take one of the worst, underachieving classes in your town or county. Offer them one hour a week of your time for a year. I can guarantee you these kids will achieve a lot better at the end of that year.

However, you do have to approach these students with the mindset that they are not idiots - just poorly taught. And frankly, I do not think you are the kind of person that is up to that task.

Now, I'm sure you have all kinds of objections, but save them. I could pull out a hundred real world examples of where you are proven wrong, but finding these is like finding proof that water is wet. It is frankly not worth my time or effort, because you are not open to that kind of evidence. Which is a shame.

This means I am done here. :)
Posted on 19-06-20, 05:42 in Something about cheese!
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Posted by sureanem

I have never claimed otherwise. Of course it would (probably) be possible to teach them mathematics and have them reach a given benchmark, but it would be a far better use of time to help the gifted ones. In practice, I think both would be pretty bad because I have next to no teaching experience, but I digress.


So, wait wait wait...

Education is mainly a function of intelligence, but unintelligent people (e.g. poor people, since SES and IQ has a strong correlation) would become highly educated if given the chance? And you do not see this as a contradictory statement? 'k, fine. :)

Posted by sureanem

to create the intellectual equivalent of womens' soccer.


... I... Just... Wow.

"Hey everybody, I have an idea! Let's create a soccer team with 10% the funding and a second-rate coach and then wonder why everyone think they suck!"

Noone is taking them seriously because noone is taking them seriously because noone is taking them seriously because...
Posted on 19-06-21, 10:33 in Something about cheese!
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Posted by sureanem

Passing calculus is "highly educated" now?


Yes, since it is part of college curriculum, it is per definition "highly educated".

Posted by sureanem

Also, "given the chance" is misleading. They'd have far more resources spent on them than the normal students, so it wouldn't be as if they'd be competing on equal footing. All you'd end up doing is spending inordinate amounts of money to replace conscientiousness.


Wait, the "normal" students would be the ones with an IQ above 130?

Posted by sureanem

...they suck.

The national teams consistently get beaten by teen boys. The reason I can't find any links for normal teams is simple: they don't even bother playing against them.

Utter shitholes with hardly enough money for food manage to scrape together half-decent soccer teams, so I don't think it's about money. And surely, not all teen boys' teams can have better coaches (let alone funding) than the bloody national team, even if they're women?


Yes, they are put to a lower standard. Which is a shame, really. Because there is nothing physically hindering them from doing as good a job as men does.

Sure, men will kick the ball harder and run faster. But strategy? Technique? Player awareness? All those are things that women could do just as well as men. If you would let the women train on the exact same terms as the men teams, they would also perform on a similar level. But we don't and they don't.
Posted on 19-06-23, 23:03 in Ubuntu: x86_32 is dead because WE SAY SO!
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Posted by creaothceann
Well, how much of a solution is VirtualBox (et al)?

Emulation already works for DOS games.


Actually, pretty decent considering sixteen core systems are starting to become affordable. Add to that we now have GPU passthrough, and full virtualisation of a 32 bit windows or Linux environment is extremely possible.

However, the hoops one has to jump through in order to get this to work properly are still pretty ridiculous...
Posted on 19-06-25, 11:38 in (Mis)adventures on Debian ((old)stable|testing|aghmyballs) (revision 2)
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Posted by tomman

In other related news, the clock is running out for Jessie - LTS phase ends in one year and one week from now on (that is, June 30th, 2020). If Saki still survives to that date, maybe it's time to consider something else, maybe learn Arch, or simply ignore it as noone has hacked my shit and will not bother since you can't mine buttcoins on an (slightly overclocked) Pentium-MMX/200, and if you ever breach into its filesystems, feel free to steal all of my porn doujinshis (which are backed up anyway).


Well, I hate to break it to you, but the only sane, guaranteed way to go on i386 going forward is source based. Either GuixSD or Lunar Linux would be my best bets. Or perhaps Gentoo.

Good news is, you can produce a system image in a VM and simply keep Saki rsynced to that after initial boot, if you don't have the system resources to self-compile.
Posted on 19-06-25, 13:22 in Something about cheese! (revision 1)
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Posted by sureanem

Chess is hardly physically intensive, and they're terrible there too, so it's wouldn't be unprecedented if they indeed were.


Not true. Please refrain from spreading this lie further.

Posted by sureanem

They get curb stomped while playing against youth teams with far worse funding and coaches. They could play against even better teams, but they don't because they'd get curb stomped even worse and it would be an utter disgrace to the "sport". 9-0 is not very good optics already, so imagine if they'd get twice that.


... Because women are NOT drilled to the same extent as men, they do not have to meet the same standards, hence they don't. And also statistics e.g. Lower size of women in general are interested in Soccer => lower number of really good players => lower interest and worse games => lower interest in Soccer => ...

People in general will only do as much as is expected of them and not an inch more. If you expect more of them... They will magically give more. Until they reach their threshold, but that's usually very far above what is expected of them.

That is also what is wrong with the current school system. If you tell a kid (s)he's dumb enough times he/she will eventually believe it, and thus no expectations are put on, well, any kid to achieve. This is what feeds the "dumb as bricks" students we see exiting the classroom of today, most are stuck in a negative feedback loop that tells them they are dumb as bricks which makes them dumb as bricks.

Not cool, man!
Posted on 19-06-25, 21:36 in Something about cheese!
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Now you are just plain and simple deliberately misreading statistics. I see you never took a proper statistics course - or if you did, you must've never passed it. :)

If you would have bothered to find and read the original paper, you would have found this excerpt in the discussion (emphasis mine):

This argument sounds reasonable but it rests on a controversial assumption. It requires that there should be innate differences between men and women in the intellectual abilities required for success at chess. The topic of gender differences in cognitive abilities is a hotly debated one, which lacks conclusive evidence (for example, Geary 1998; Kimura 1999; Kerkman et al. 2000; Pinker 2002; Spelke 2005; Summers 2005; Lachance & Mazzocco 2006; Ceci & Williams 2007). Even if such differences exist, it is unclear which, if any, intellectual abilities are associated with chess skill (for a recent review, see Bilalić et al. 2007). Whatever the final resolution of these debates, there is little empirical evidence to support the hypothesis of differential drop-out rates between male and females. A recent study of 647 young chess players, matched for initial skill, age and initial activity found that drop-out rates for boys and girls were similar (Chabris & Glickman 2006).


The study is legit and has been successfully defended a number of times, and the formulas used to check out the conclusions hold up (check appendix A). At this point, if you are going to argue with science then go right ahead, but I will laugh at you. :)

As for women soccer, well, there are a ton of improvements happening already and as time move on, things are bound to get better. :)
Posted on 19-06-27, 16:25 in Something about cheese!
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Actually, your reasoning is not correct, and the same can be said for Knapp.

The criticism of Knapp is threefold. The first is that the maximum theoretical player does not have the expected score of the real world player. This may seem relevant, but since the error is *uniform*, it does not matter for the intended purpose, which is to show that simple statistics make up the absolute majority of the explanation.

The second problem is that he questions the assumption that given a large enough dataset, each individual sample will fall within a bell curve. I do not see how this assumption is even questionable, any and everyone who has ever taken a statistics course knows this is the truth. The keyword being large enough dataset.

The third and final problem is the same trap you fall in; This is not a linear model. It is an exponential model. That is what you need all those complex calculations for. Again, you are clearly not a statistician, else you would've understood this. It is taught in the first college-grade statistics course. If you want to do more reading look up the official method in this book:

Order Statistics, Herbert A. David, Haikady N. Nagaraja, ISBN: 0471654019

Regardless, what Belalic did was prove that there is a statistical model that, when crunched with real numbers data, holds up. The model also explains the gender gap to 96% accuracy.

-----

As for women soccer games, why should they be able to beat men in order to provide an entertaining game?
Posted on 19-06-29, 14:20 in Something about cheese!
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Is the study perfect? No, of course not. But both you and Knapp have not understood the argument that is made.

The argument being presented is, given the top 100 men and the top 100 female chess players, there is a skill difference that is proportional to the number of men and number of women playing chess.

This is then backed up with a model that predicts ELO and subsequent skill gap based on the number of people in both pools, in a theoretical scenario.

This is then compared with real world data, and concludes that there was a 96% correlation (on average) for this particular data set. If run on another set, it provides a similar outcome.

Conclusion: The gap is mostly due to few women participating in chess, not due to women being inferior to men.

Note that what was measured and predicted here, is the difference in skill, not ELO as such. This difference is also assumed to follow a bell curve (but ELO in and of itself isn't). The model subsequently calculates what the n:th person of a certain pool should have as skill gap, vs what the n:th person of a different larger pool have.

Or in other words: The n:th player in Pool A of size K has ELO x. The n:th player in Pool B of smaller size L then should have ELO y. Difference in skill should be z. Let's check by plugging in the values for the first 100 in pool A and B. Oh look, real world data matches the predictions by an average of 96% (worst case 80+%)!

At this point you're arguing facts. You don't happen to believe the world is flat and the moon landing is a big hoax either, do you? :)
Posted on 19-06-30, 10:05 in Something about cheese! (revision 1)
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Sigh...

The model explains the skill gap to 96% on this particular dataset. Why can you not accept that? This is fact, proven by the paper.

Now you are just arguing semantics, and trying to argue math to fit your world view. In essence, you are saying 2 + 2 = 5 if you squint look at it from a certain angle. That is not only dishonest and misleading, it is trying to warp the facts to your own world view.

If this were a CO2 study, you'd be arguing there is no global warming because the model predicted this year would have an average temperature of 20 degrees celcius, while in fact it only had 19 degrees celcius.

If this was a doctors prognosis, you'd argue cancer is harmless because your old man whom the doctors gave 6 months to live actually managed to live 18 months instead.

Surely not even you is that ignorant?

[edit]
Aaaand your math still fails you.

Article: The model predicts the difference gap between the n:th ranking male and n:th ranking female skill as a function of skill pool size and rank.

Let X be the number of males available.
Let Y be the number of females available.
Let n be the rank we choose to look at (1 - best male vs best female, 50 - 50th best male vs 50th best female).

gap = model(n, X, Y);


Let's code it:


Pool X = getMalePool()
Pool Y = getFemalePool()
abs_error = 0
sum_predicted = 0
sum_real = 0

for n in 1..100:
predicted_gap = model(n, X.size, Y.size)
real_gap = X.members[n].skill - Y.members[n].skill
abs_error += abs(predicted_gap - real_gap)
sum_predicted += predicted_gap
sum_real += real_gap

print("Absolute error: %f", error)
print("Average accuracy: %f", sum_predicted / (sum_real*1.0)) // This amounts to ~96%


Could you please point out which line of this algorithm is incorrect?

[/edit]
Posted on 19-06-30, 17:17 in Something about cheese! (revision 2)
Post: #72 of 205
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Ok, you have the model, you have the data, go nuts. Prove him wrong. :)

Protip: It's built in to the model. Also, seems you have confused accuracy and causation.
Posted on 19-06-30, 21:20 in Something about cheese! (revision 1)
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Posted by sureanem
I did, two posts ago:

for German players, the median male score is 1560 while the median female score is 1254 (gap = 306)


This could hardly be caused by any outliers, now could it?


Ah, no, sorry. You used a different model, that's not how this works. See:

1. We have a machine which uses a model that claims to do a specific thing.
2. When tested versus real world data (albeit a specific dataset) using a testable and repeatable method, the model is proven to have a 96% accuracy.
3. You now say the results do not count, because that is not how you measure accuracy. Fair enough.
4. Please provide proof. With the same model, using the same method, please display your results, as well as calculating your coefficient of determination. It should take you perhaps half an hour to an hour with the tools at your disposal.

Since you are the one claiming this study is a fraud, the burden of proof is on you. :)
Posted on 19-07-01, 06:14 in Something about cheese! (revision 2)
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So with your own calculations you find that the mean R² is about 0.74.

This means in your own words that the model explains for 0.74 proving, at the very least, that there is a strong correlation between pool size and skill gap.

Thank you! :)
Posted on 19-07-01, 19:19 in Something about cheese! (revision 1)
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Posted by sureanem
Posted by wertigon
So with your own calculations you find that the mean R² is about 0.74.

This means in your own words that the model explains for 0.74 proving, at the very least, that there is a strong correlation between pool size and skill gap.

Thank you! :)

Not the mean R2, just the R2.

What does the second sentence mean? That the high R2 proves the model is accurate, even if it happens to perform worse a model which does take into account the skill gap?


Wait, really? You have no idea at all what R² actually means, just that you need to calculate it? ROFL... It says clear as day the correlation between pool size and skill gap is 86.1%.

Oh, and your "other hypothesis"? Women are worse at chess? Yes, this is already a fact. What is your explanation to this? That women inherently are worse at chess? Nope, not holding up according to multiple studies.

And yeah, there's a reason I prefer MATLAB (actually Octave) or Numpy over shitty, shitty excel. :)
Posted on 19-07-01, 22:36 in Something about cheese! (revision 1)
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You still fail to understand the argument, or, indeed, the scientific method as a whole. Ok, one last time, let's examine.

1. Observation: There is a large skill gap between two groups A and B.

2. Hypothesis: The skill gap between a group A and a group B can be explained by the difference in pool size of the two groups.

3. Method: Create a statistically valid model that predicts the skill gap of group A and group B. Of each pair from the 100 best from each group, test whether or not the gap predicted is close to the actual gap for each pair.

4. Expected outcome: If the model is accurate, it should correlate very well to the real world results.

5. Result: The result shows the model has around 96% accuracy from the dataset, and a very strong correlation coefficient of around 86%.

6. Conclusion: The data suggests that the skill gap can mostly be explained by the difference in size, though the model should be tested on other, similar data for conclusive evidence.

This is a scientifically sound method. The model is also scientifically sound and reasonable (it might not have been, but it does check out and is legit). Ergo, scientifically, the gap may be explained by the size of the pools rather than any single skill gap.

Evidence currently suggest the model is correct. How do we disprove the model? By running it on other known factors, such as color of skin, country of origin, or preferred language. It is highly probable these will show similar results given the almost unanimous results found in this study, but yes, it could simply be a fluke.

See... The scientific model assumes humans are flawed beings that make mistakes all the time. Therefore a good scientist asks themselves, did I make an error somewhere? This is of course exploitable by those who wish to drive an agenda. See, for instance, the smoking correlation with lung cancer.

I have no agenda to push, but I am interested in the truth. And the truth says, all sweeping statements about gender, genetics or race are best left in the past, where they belong.

Posted by sureanem

Oh, and your "other hypothesis"? Women are worse at chess? Yes, this is already a fact. What is your explanation to this? That women inherently are worse at chess? Nope, not holding up according to multiple studies.


Which "multiple studies"?


The linked study I showed you lists them in section 4 paragraph 4. It's 9 studies listed there.

Posted by sureanem

And yeah, there's a reason I prefer MATLAB (actually Octave) or Numpy over shitty, shitty excel. :)


Then perhaps you could use your expensive software then and calculate that expression I posted?


Why? You can use octave and numpy yourself, they are free to use. If anything, excel is the expensive tool here.

Thank you for proving you have no clue how the scientific model works or what it is about, nor that you have ever written an accepted academic paper. Good day to you sir, I'm finished here. :)
Posted on 19-07-02, 08:00 in Something about cheese! (revision 1)
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So far you've managed to prove that women are bad at chess because women are bad at chess.

The model I have presented instead gives strong indications that women are bad at chess because it is a smaller pool size than men.

So, the interesting question here is whether or not this model holds up to scrutiny. How do we test this? Well one obvious way would be to run the model on other groupings, such as:

- Males vs Females in a different data set
- Different nationalities/cultures

et cetera. You do have the model as described, as well as the tools. Get it to work properly and then test it on different data sets. If it holds up, we should see similar results (~90+% accuracy and ~80+% correlation coefficient). If it does not, well, I'll eat my shoe. Deal? :)
Posted on 19-07-03, 20:31 in Something about cheese!
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Seems like you still do not understand how the math works. It is pretty simple, really:

https://en.wikipedia.org/wiki/Order_statistic#Probability_distributions_of_order_statistics

This is, in other words, a standard function. However, it cannot be used directly due to the extreme calculations that must be made, it must be sped up. That's why the final approximate form is:

[math]E_{n,k} \approx (\my + c_1 \sigma) + c_2 \sigma \frac{n!}{(n-k)!n^k}(\ln{n} - H(k - 1))[/math]

(If that isn't showing up properly, find a latex editor)

Sympy could help with the bigger calculations, but more specifically try out this:

https://www.sympygamma.com/input/

Of course, it is a bit of manual repetitive labor to input 100 different equations, but maybe you could simplify the equation if you plug in some values.
Posted on 19-07-03, 21:23 in Something about cheese! (revision 1)
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My guess is they do not wish to use that in order to create an independent prediction from the dataset.

When creating a model you wish to make it as generic as possible. Ever heard of the term overfitting before? Quite common in the realm of A.I.

As for inputting, well, something is very wrong in your calculations, you should get a value between 0 and 500 roughly.
Posted on 19-07-04, 20:50 in Something about cheese!
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Not sure how you get your formula for the model so messed up. To be clear, the formula should be:

µ + sigma (c1 + (c2 n! (ln n - H(k-1))) / ((n-k)!nk))

Where H(k-1) = 1 + 1/2 + 1/3 + ... + 1/(k-1).

Incidentally, H(n) can be written as follows:

H(n) = log10 n + 33841/58628 + 1/2n - 1/12n2

With this it should be possible to reach a good enough accuracy with a symbolic calculator (e.g. a calculator that understands the notion of 100! / 101!), at least for the purpose of determining the difference in ELO.
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