Rotolux

Physics, Python programming, Miscellaneous geekness

Chickens and eggs of physics

Whilst listening today to the ABC science show, I mused on the following (poorly thought-out) inequities of science communication.

Astronomers and paleontoligists have an advantage over the rest of us trying to communicate our science in that they can assume a greater level of knowledge amongst the general public. Thus they get to explain more of what's new to the general public within a given amount of time. Is this why Catalyst seem to have an overrepresentation of stories on astronomy and dinosours? Or is it because two of their prominent reporters have backgrounds in these areas? Probably the latter. However, experience of dealing with Monash media and marketing people, would tend to support me, so I'll stand by my suspicion that there is a bias.

Astronomers like to talk about how big things are, how old things are, how far away things are ... in short how far from everyday experience the things they study are. I also do this when talking to people about my research, because this is what "bends their minds". Luckily I at least work in an are where I can explain how small things are, which gives me a "hook". However, I often think that this distorts the intrinsic value of all the other things that I (and other scientists) encounter in research - many of the beautiful parts of my research are simply too hard to explain to non-specialists without also relating a significant number of background concepts.

By the way, Paleontologists like to talk about how big dinosaurs were, how long ago they lived, how easily they could crush a human skull etc.

Some interesting articles

The latest Edge has an interesting article. I like
this one arguing against Gardner's theory of Multiple Intelligences in favour of a single useful measure "g", which is presumably IQ. The argument is that the curriculum in schools is pandering to Gardner's labels, which aren't orthogonal. I tend to agree that this is a problem, but I wonder whether a roughly-orthogonal set could be found using proper research and data mining or Principle Component Analysis techniques.