The retraction is that the strong/definitive proof that "the camel has two humps" was fabricated/exaggerated.
The converse - that it has one hump hasn't been proven. From the quote, Richard Bornat doesn't say that they re-crunched the numbers and now there is proof of one hump.
The camel has gone back to having an unknown number of humps
Or “It follows a distribution similar to other skills involving mathematics.”
The paper presented an unusual hypotheses, that there was this sharp distinction between those who can program and those who never will, and that this distinction was somewhat orthogonal to other measures of scholarship. So for example, one might be a good architect but never get good at programming no matter how much one tried.
You’re right we haven’t disproven this hypothesis, but given its novelty, the burden of proof is on "the camel has two humps." With the paper retracted, we do not presume it has an unknown number of humps, we presume that skill writing programs is going to be similar to other skills we observe.
As this article observes, unusual outcomes in attempting to teach programming may just as easily be explained by the fact that in a young field, we may not know how to teach programming.
If we’re going to chase ‘humps,’ perhaps we should look for unusual distributions in the skill of teaching programming. We may be terrible at it, and perhaps the skill we are really observing is the skill of learning to program in spite of didactic and structural obstacles.
It is anecdotal evidence, but there are people with innate ability.
Also - natural distribution makes the one hump camel the default hypothesis.
Still, I'd assume that with programming like I assume for most skills (music, athletics, lawyering, foreign language, whatever), some people start out with more aptitude than others, and find it easier than the others. I wouldn't assume it's a markedly more pronounced or separated distribution in CS/programming than for other things though.
(Also, I have actually been programming since I was 12, and additionally believe I do have some aptitude for it that makes it easier for me than for some others, I'm pretty good at it -- and I still remember how hard it was for me to understand pointers in Pascal when I first learned them, which seem so straightforward to me now I have no idea why it was challenging at first! And I'm glad I had good teachers in formal coursework.)
The concept itself still sounds trivial to me though.
Programmers are far too egalitarian for our own good and our increasing marginalization and stagnant wages are the inevitable consequences.
Although it's always tough to compare fields, I do think that some ways of measuring the skill set and educational requirements for a software developer do place it in one of the most rigorous fields out there.
Majoring in computer science at a reputable university (I don't mean an elite one, I just mean one that requires the standard curriculum) typically requires two years science and engineering track calculus (not the easier one year, slower paced track available to econ or bio majors at some universities). Physics is often a requirement, as is an additional year of upper division mathematics (differential equations, calculus based probability), along with some classes that intersect with mathematics had have a heavy computing component, such as numerical analysis which generally involves computational methods to solve mathematical problems that aren't amenable to closed form solutions, or mathematical algorithms that require so many iterations that they aren't practical to solve by hand, optimization, graph theory. Then you add in compilers, operating systems, and other demanding electives. And keep in mind, CS or other Math/Physics/Eng students typically must take a far more substantial general curriculum in humanities than humanities students must take in quantitive fields. We don't get out of writing papers the way they get out of difficult math.
People often point out that a field like law requires a three year grad degree, but honestly, in many ways, I think is is like comparing people who run at a 8 minute mile pace for 70 minutes vs people who run at a 6 minute mile pace for 40 minutes. You can't just compare the time spent, you have to compare the rigor of that time. Attrition rates for computer science are typically high even in elite schools, including at the graduate level (elite law schools, by contrast, often have attrition rates below one half of one percent, and many of the students come from fields like history or poly sci - nothing wrong with those fields, they are interesting, but they really don't have anywhere near the same attrition rates as CS or related majors). And if you do get a grad degree, then I'd say you've honestly gone through something much more difficult if you did it in Math/CS/Eng/PhysicalScience.
Of course, you don't have to strictly major in CS or a related field to be a programmer, but consider what it takes to get through the google interviews. Try out some of the medium to difficult questions from "cracking the coding interview", and think about the poise, communication skill, analytical ability, and coding ability it takes to do a good job on these question sin 45 minutes at a white board (as an unsuccessful google interviewee myself, I assure you you are expected to make substantial progress on these problems, and medium to difficult is surely fair game!) You could get to this through self-study or formal education, but either way, it most definitely is not a low barrier to entry.
Ok, not everyone works at google, and many of us work on crud apps. However, I have yet to see the mythical "simple crud app" that people refer to when they talk about how most programmers don't have hard jobs. These apps usually don't have deep algorithmic complexity, but they are hard to work with. You often inherit a difficult and poorly documented code base that you need to follow through, logically, with a fine toothed comb. You often have to get up to speed with a new framework, or an older one that uses deprecated methods during an era of substantial churn. You need to tease out often elaborate business logic and calculation pipelines from analysts or other non-technical workers that they have trouble communicating to you. And you often have to do this under intense pressure to provide estimates and meet deadlines, often from people who work in much more predictable fields and see your hesitance to commit as a sign of unreliability or a lack of professionalism. These, in my experience, are the "easy" crud projects. Honestly, I think researching and applying a novel machine learning algorithm to a data set can be a lot easier in many ways.
That turned into a rant, but in short: this is a really hard job (and JoBrad, I hope it's clear to you that none of this is in disagreement to what you wrote, more riffing on the theme here…). Programming it isn't easy at all. I do think the reason that there is an alleged "shortage" is largely due to the fact that people who can handle this kind of work (it requires a huge about of logical reasoning, quantitative reasoning, consulting and working with vague requirements and end users, and presentation skills under pressure) have a lot of options out there. The market is actually working, pay and working conditions need to improve quite a bit to draw people away form those other things they're doing and into programming in the numbers silicon valley employers would like to see. And it won't happen overnight, ramp up time takes a while, this will be (if it happens) a pretty painful transition that will force the tech industry to dig pretty deep.
First, one can dispute “programming ability is distributed in two humps” without implying that “everyone can program.”
Second, your proposition isn’t even a consistent proposition. If programming talent really is rare, then being egalitarian won’t have any effect on wages: Some people can, some cannot, and the free market will quickly sort out that you cannot build pyramids with thousands of unskilled programmers.
If wages are stagnant, one possible explanation is that more people can program than you would care to admit. Which explains why you are ranting about spreading the myth that programming ability makes you a special, delicate flower. If you can socially engineer people into not becoming programmers, you believe there will be more money for you.
What you fail to realize is that as an industry, “a rising tide lifts all boats.” The more talented people there are, the larger the pie we get to share. More programmers equals more software, equals more tools, equals more companies, equals more hardware, equals more demand for software, and so it goes until software has finished eating the world and it becomes a mature, stagnant industry.
If you feel your wages are not rising as quickly as you like, perhaps you should look into other possible causes, such as a lack of skill in negotiation, or choosing to be an employee instead of an entrepreneur, or wage-fixing by companies with no-hire policies, or the practice of handing out paper stock options in lieu of cash for startup employees.
Yes, but people are disputing “programming ability is distributed in two humps” despite there being clear evidence of that being the case, and no evidence to contradict it. The distribution has not changed, or been retracted. Only the unsupported notion that the distribution is innate and unchangable.
Please cite this "clear evidence" you speak of.
But that’s not all. It’s not enough to summarise the scientific result, because I wrote and web-circulated “The camel has two humps” in 2006. That document was very misleading and, because web documents persist, it continues to mislead to this day. I need to make an explicit retraction of much of what it claimed. Dehnadi didn’t discover a programming aptitude test. He didn’t find a way of dividing programming sheep from non-programming goats. We hadn’t shown that nature trumps nurture. He had, however, found a predictive phenomenon, though he had no explanation of it.
How do you read these paragraphs, or what part of the retraction supports the "two humps" claim and how do you understand that claim?
The guild that is infamous for exalting the "leet", nurturing cliques, and projecting disdain towards n00bs, laypeople and "idiots" is too egalitarian?
> our increasing marginalization and stagnant wages are the inevitable consequences.
IMO, our increasing marginalization and stagnant wages are the consequences of programming being not that hard (as far as the general demands of the industry). Programming is not exceptional or arcane, it's just new and with tooling and pl advancements making it easier and easier to meet industry demands, it's only natural that wages will stagnate.
I am pretty sure everyone could be a lawyer if they wanted to be. The couple of lawyers I have talked to about it say that it is all about hard work not smarts or any special skill.
Also, let's not forget that sometimes the truthfulness of the assertion takes a back-seat the the desired effect it will have. Very often we tell children "You can be a doctor" when at certain points we know the likelihood of that is very, very small (for any number of reasons, such as aptitude, circumstance or history). The urging may not make them become doctors, but if it makes them expand their ambition and try for a hard goal, the end result may well result in a better outcome for them, regardless of whether they indeed become doctors.
I know several elderly and accomplished medical professors who claim that they would not have been accepted into medical school under today's conditions. They got in during a time when it was very much easier academically (but harder financially).
The hypothesis there was that time spent on assignments and grade obtained are two essentially independent variables.
Some sources even go as far as saying there is no camel involved at all, and might have never been.
Camel has gone back to be assumed to be typical gaussian camel as for many camels we don't know much about.
And right now the zeitgeist in tech is to increase diversity.
You don't want to be the subject of the next Two Minutes Hate?
And of course, I can't ask you for a source that studies are getting stomped out by political pseudoscience, because those sources will also have been stomped out. So I should instead ask you: how did you get this impression?
In fact, all studies should do so to avoid endless blathering on the Internet.
I'm also curious what you mean by 'all studies should do so'. Do what, exactly? Analyze the demographic of white heterosexual upper middle class differently? I'm sure that might be interesting, but it's already a very specific demographic that fits class, sexuality, race, and gender identity. What other categories are being neglected that are skewing the view of demographic majorities in the technology industry?
And that was all there was to my post. But since you are playing Tipper Gore to my Dee Snider:
Why I get that impression - because science is also political. As are the people that do it, write for it, fund it and so on. We people are stupid, biased, and conformist. We fall in line. We do what will bring us money or social capital. We twist our reality to fit the desired outcome. We shred to pieces papers we disagree with, but only skim the one that confirm out initial assumption. We love to offer post facts justification how we were right all along.
I am from former communist country - I know how whipping science and arts to fit the party line is done. I have seen it.
In the west it is not different just subtler. People are more afraid of being on the wrong side of society, than being wrong.
That is how the world works.
And with tech powerhouses like Japan, China, Taiwan and South Korea and India that both produce, utilize domestically and export a lot of tech talent - I am not sure how white is tech.
You're correct there. I was under the impression one would be focusing mostly in the oh so legendary Silicon valley.
Other than that, thanks for your generosity with your perspective. If I could have another question, what do you consider the warning signs of a psesudoscience or bad science being used for political gain? Also, how do you tell when a methodology is found flawed because of political gain vs a methodology is found flawed because it is flawed and in bad science?
If science was apolitical they both would be laughed away.
My opinion is that in the real world - both will gain acceptance at the fringes, but while one of the papers will just be burried and ignored, the other will be career ending move and will be shredded, and careers will be launched disproving it.
Same will be with any paper that perpetuates the white dominance narative and tries to disprove it.
There are bigger penalties in going in one of the possible directions of the accepted truth.
So we have built in conformation and confirmation bias in system.
And the antivaxer paper is stellar example of using pseudoscience for political gain.
With diversity in tech - you lose nothing by spinning any fact into pro diversity argument.
That isn't even the current demographic. Indian and Chinese people are more disproportionately represented than white people are.
I'd be really surprised if that was the case and I'd like to see your data, please.
Almost certainly the former. Notice the dismissal of scientific evidence because they dislike the implications. Every study on the subject has shown clear racial IQ differences. That is not "pseudo-science". Arguing about why that observation exists is fine, but pretending the observation itself must be wrong because it hurts ones feelings is not reasonable.
>The retraction is that the strong/definitive proof that "the camel has two humps" was fabricated/exaggerated.
It isn't even that. The bimodal distribution is very clearly there, the "retraction" is just to the claim that this is an innate characteristic. Something which was widely repeated, but which the actual paper never even claimed.
>The camel has gone back to having an unknown number of humps
It still has two humps, we just don't know why. But we never knew that in the first place.