I downloaded the RTM through Dreamspark, because Dreamspark students get a free Windows Store account (I have some asp.net and WP7 apps I wanted to port over to Win 8). Turns out, you still have to submit a credit card even if you have a Dreamspark account, which instantly turned me off.
Many people think that science is too closed, and too slow. We are trying to change that. There are 4 things we are trying to achieve with Academia.edu - ways in which we are trying to re-shape and accelerate science:
- Instant distribution. Right now there is a 12 month time-lag between submitting a paper to a journal, and the paper being published. We need to remove that time-lag and introduce instant distribution of scientific ideas.
- Multi-media. Right now, scientists only share papers in PDF form. We need to bring about a science where scientists are incentivized to share data-sets, code, videos, blog posts, and comments on all these media. Right now 50% or more of the world’s scientific output does not get shared, because the system of credibility metrics only rewards one kind of format, the paper. We need to change this.
- Open access. We need to bring about a world where a villager in India has the same access to the world’s scientific output as a professor in Harvard. When you open up access to the world’s scientific literature to 2 billion people, magical things may happen.
- Better peer review. Right now the peer review process takes 12 months to complete, and only surfaces the opinions of two academics - academics who may be biased, uninformed about the subject area, or just in a bad mood when writing the review. 2 people is too small a sample size. We need a faster and more robust peer review system, one that surfaces the opinions of the entire scientific community, across a variety of dimensions, and in real-time.
There are a couple of themes that connect these four goals. One theme is that increasingly scientists are wanting to have a direct relationship with their audience, as well as analytics that reflect how that relationship is developing. Previously the scientific journal would mediate the relationship between the scientist and his/her audience. This is starting to change, and it's reflective of broader changes on the web, as sites like Twitter and Facebook allow people to have more direct, unmediated, connections with their audience.
The second theme that connects these points is credibility metrics. The behavior of scientists is driven by the need to build credibility metrics, ones that reflect well on the scientist and his/her work when they are applying for a grant or a job. New reputation and credibility metrics are starting to emerge in science that incentivize things like instant distribution, or the sharing of data-sets, or open access sharing. I think the emergence of these new credibility metrics (citation counts from Google Scholar, usage metrics such as download counts from Academia.edu) are going to have a big impact on the rate at which science evolves.
It's an exciting time for science. Science is transitioning from a 17th century way of sharing ideas (sending papers around the world with 12 month time-lags in every iteration) to a much faster system of sharing ideas on the web.
If this mission sounds exciting to you, I would love to hear from you at richard [at] academia.edu. We are looking to hire engineers to join our team, and more generally I love chatting to people who are excited about trying to make science faster, and more open.
I hate that my first reaction to this was a jaded groan. In actuality, whatever your profit motive, your stated goal is quite noble and valuable. It is always astonishing to me how litte researchers and academics care about the metrics of dissemination, as if printing something for their peers to read and having inset charts hidden away in the body of the text was all that's needed to make their work known. But not much effort is taken to improve communication and transparency because hey, academics are smart enough to figure it out themselves, right? Good luck with your work.
> how litte researchers and academics care about the metrics of dissemination
I think if anything, the pendulum has shifted rapidly to the other direction, where many (and especially many in administration) care for little besides metrics. Impact factors, acceptance rates, citation counts, h-indices, media "hits", etc. are the currencies that rule academic promotion.
IMHO, you should start by offering to host the paper native source (whether latex or doc - I'm not a huge fan of PDF, especially when A4 printing issues arise)
Then, putting the matching data online would be a worthy 2nd step.
I have a "long term project" to put that on my own website but never did. Maybe one day when I have more time.
For some simulations I did, uploading the sourcecode and the various part so that my results can be replicated on the same page as the paper would be great - but it can get big (like in GB) and I'm not sure it would be cost effective for you to do that.
I'd even like to upload a VM with all the tools setup and ready (free software only so I'm fine) and the queries making my results as batch files along with other premade queries, but that would be even worse - the overhead of a distribution is big.
Yet making results more reproductible would be a great goal.
How do you see yourself in relation to ResearchGate, a startup in Berlin that apparently is quite 'respectable' in the Berlin startup scene? Surely they cannot have escaped your attention...
(and I know for a fact that you have not escaped theirs)
GP was bizarrely tone-deaf in his comment, but BA, QA, PM are a could vantage point form which to attack gender inequality.
They are jobs expect less technical prowess, and thus leave open an opportunity for a woman to show crossover talent (a BA knows how to write SQL? Wow!) and chisel away stereotypes.
Now, it's a ridiculous rigamarole to have to go through, but it may be strategically effective in wearing down barriers.
Here's an example from my own experience: There's a significant number of trans women who work in tech. And almost all of them started transitioning as adults, in their 20s and 30s. So, they had the "benefit" of male socialization growing up, and being encouraged more to dabble in computers/tech/engineering. Much fewer trans men (and non trans women) in tech, it seems.
Also, the number of women historically majoring in CS dropped off significantly in the 1980s, when 1. Video gaming culture started to take off and 2. Hardware (something seen as more masculine) started to be emphasized more often. Like, programming historically was something women with math and CS degrees did, then I think there was a greater influence of hardware and tinkering with your hands that was part of being in tech. Stuff that is traditionally seen as more masculine.
The DSM-IV (1994) quotes a prevalence of roughly 1 in 30,000 assigned males and 1 in 100,000 assigned females seek sex reassignment surgery in the USA.
Those numbers are based on statistics from the 1960s, when transitioning was next to impossible, and are probably a couple orders of magnitude too low. More to the point, that threefold difference seems to be much smaller, if it exists at all.
I agree totally with what you say. She really doesn't have the experience in age or diversity of tech career fields to base her opinion.
Women are underrepresented in tech, and that is a big problem. Just look at college CS classes, hackerspaces, meetup groups for various programming languages, etc. I think that in a lot of bigger companies, there are institutional pressures to keep this stuff on the downlow, but are more prevalent in startups.
Yeah, but they are really important to know for interviews. Which of course may or may not have anything to do with the actual life of a developer, but that's life.
whenever prospective employer has this type of CS trivia questions I lose interest to work in that environment regardless if I know or don't know answers. It shows lack of maturity and experience to understand what are actually characteristics to look for to determine good developer. This type of questions might be useful for non experienced candidates but it is fairly counterproductive for experienced candidates
Last year I interviewed a BigCorp which was all about these interviews. The first guy clearly told me that the project was sort of stuck and they needed to sprint to make it happen.
The interview was 99% math. The people who asked me question looked all smart alecs and their questions all seemed to indicate that they wanted to show me how intelligent they are, and how stupid I am.
By the end of the interview it was clear to me why they were stuck. Those people had absolutely no interest in building things. It was all about individual One-upmanship, and showing their little math and puzzle tricks they would have gathered by combined reading on the internet over the years.
Why is the software development interview so radically different from the kinds of conditions a developer actually works under? I mean, I can't imagine someone on the job struggling with code, but only being able to look at a printout of it rather than in an ide/debugger, unable to look at a book, the internet, or ask someone for advice, and having a strict 30 minute or so time limit to figure out their problem.
