Django Girls one year later

http://www.nsf.gov/statistics/nsf13327/pdf/tab33.pdf

The first thing to note, is that both the number of men and women getting CS degrees dropped: the entire field went from ~42k BS degrees to ~24k degrees. There were about 10k less men, and about 8k less women graduating in 1996 compared to 1986. The number eventually rebounded for men, but didn't recover for women until 2003. So something drove men and women out of the field, and women stayed out of it longer.

The next interesting thing is the number of masters and PhDs per gender. Neither of them dropped (so the percentage of BS graduates getting MS and PhD actually increased!). So it was still desirable for men and women in the field to get their masters and doctorates.

So the question isn't why the number of women plunged, it's what drove both men and women out of CS, and what caused it to grow for men? I would probably hypothesis that CS was seen as a risky degree, so while men are generally less adverse to risk (see all the dangerous jobs they do) and got a degree, women choose more stable degrees (though those interested remained, as I think the number of PhD and Master degrees show). Now that CS is now seen as a stable career, we can see there is more interest to join. Of course, that is only looking at the data cited by what you linked, there could definitely be other circumstances.

You're reading things that I'm not saying. It is an ordinal concept, but we don't have the ability to measure it well, only notice large differences. You can think of it as a quantity for which we have very inaccurate measurement devices.

> I'm also confused as to how you can even determine that such a "constant shift" isn't happening - in terms of edges in your influence graph, they shift regularly. Yesterday I met a new person at work and we now interact and influence each other. The power digraph has gained 2 edges. Could you explain how you quantify this?

Because in this discussion we are talking about groups (social mobility in individuals is an interesting, yet somewhat different discussion), and when we look at a large number of people, it's easier to approximate power through proxies such as income, percentage of powerful roles such as CEOs, leading journalists etc. (it's harder to compare power in individuals, because it's hard to tell which is more influential, a media personality or the CEO of a large company, especially as the latter's influence might sometimes be hidden).

Shifts in power from nobility to the bourgeois class, or in the relative cultural and technological power of Europe, were sudden changes of relative homeostases. Similarly, you can track power distribution between men and women, or between whites and blacks in the US. They point at very stable states that are violently changed, or (usually in the case of women), a faster rate of smaller revolutions (or a more steady rate of change). The difference between male/female and race hegemonies is also well understood (and seen in many other cultures), because the classification of women as "strangers" is always very different from that of actual foreign ethnicities.

Why onboard women? Why give them silver spoons? Why is this a thing?

Also, I fail to see how your formula is meaningful at all, as it has nothing to do with power dynamics. At best, it describes the distribution of the number of powerful people in a random sample. Well, OK, how is that helpful? It cannot describe any shifts in power as it assumes independence, where our understanding of power is anything but (you are more likely to be powerful if you're close to powerful people). The actual dynamics is a collection of non-linear processes in a complex system. A very, very crude example of a somewhat similar process is that of segregation[1] with the addition of random populations during the run. It is processes with strong correlations between neighboring agents that tend to show stability pockmarked with rare bifurcations, which is exactly what we see in human society.

When I went to study history in graduate school, I dreamt of applying math to historical events. Playing with cellular automata to recreate certain shifts in history is fun, but eventually teaches us little, as we cannot say much more about the model than the same qualitative descriptions historians give anyway. It is sometimes cool to figure out the values of certain variables in the model and assign them meaning (like the "despair level prior to the French Revolution", but even these numbers don't tell us much because the models are so sensitive we can't be sure we got them right, and even if we did, it's virtually impossible to measure the values directly rather than figuring them out retrospectively. Therefore, speaking about events in broad qualitative terms is often the best we can do (compound cellular automata's Turing completeness with the non-linear ODEs defining the automaton's rules and the difficulty measuring variables directly and you get very little predictive ability).

[1]: http://ccl.northwestern.edu/netlogo/models/Segregation

This is clearly wrong - I demonstrated a stupidly simple alternate theory which disagrees with the consensus, but still lacks constant power shifts.

Rapid shifts in the composition of various fields would debunk basically every theory that presupposes human nature doesn't rapidly shift. As such, your theory proves very little, and other theories (e.g. mine) make the same predictions. So why cling to that theory, rather than, say, my theory of p=A - B x math_content + second_order_corrections?

My theory predicts more things (e.g., it predicts % women in academia, % women in finance, % women in tech vs non-tech roles) and is vastly simpler. How is that not a win?