A hard science is one where you can control causes, or have an equivalent operationalizable theory of causal mechanisms, or there is a feasible strategy for the development of either.
Science becomes a half-empirical speculative activity in all other cases. So yes, a lot of genetics (but not all), and so on, is a speculative science. Eg., very rarely some finite number of known genes can be given a known causal mechanism (etc.) -- in this cases, you have a Science.
This seems to be more exposing that “hard science” is an arbitrary label which is not especially useful. Genomics doesn’t magically become non-empirical because you don’t control all of the variables - empiricism is about observation, not control – and all science has some level of speculation because the entire concept is based on collecting evidence to test theories. Just because a biologist or psychologist works with more complex problems doesn’t change that underlying mechanism.
When geneticists are studying eg., single-gene-to-single-disease relationships then this is science. When they're studying possible trajectories of 1000s of genes on down-stream phenomneon with an amazing number of uncontrollable causes.. then i'd be inclined to call this pseduoscience.
The line isn't arbitrary at all, but based on hard facts about the nature of reality and our ability to measure it.
Modelling the weather is a science if it is done based on extremely recent history, and the models are explanatory and accurate to within a relevant time horizon. If you use the same models to predict the weather next year, that's pseduoscience.
Pseudoscience is often deeply apparently scientific -- resuing all the same statsitical formulae, etc. -- this is a charade. It is reality which decides that these techniques are broken, not the techniques themselves.
Sure, and you’re welcome to have personal preferences. My point is just that “hard science” sounds like an objective term but it’s neither well-defined nor objective. Calling it “specialties which mjburgess likes” would be just meaningful.
Correct. But the problem for these areas is that they cannot operationalise "works", because there's no causal theory. There's literally just "measure this, measure that, assume some unknown mechainms with extremely strong properties that we have no evidence for ..."
So we're in a very very bad situation in these cases. It isnt that mere speculation is invovled as an input, its that mere speculation is the output.
Not really my experience when helping on, for example, gene array data analyses: there's often a lot of causal theory (or at least hardened hypotheses) developed going into the experiments. People don't just randomly measure things.
Sorry, but what is an "operationalizable theory of causal mechanisms" other than the ability to control causes?
I guess, there is rather little hard science done in the world then/a lot of science is speculative in your view (maybe only most of mathematics would survive if it were a science).
There's a lot of science: chemistry, physics, some biology -- etc.
Mathematics isnt a science at all, since its a study of number not of cause: there is no inferential gap between measures and their causes in mathematics, because there are no measures.
> operationalizable theory of causal mechanisms
A theory whose terms refer, at some point, to measurable variables that can in some contexts be controlled. So, eg., you might have a theory of light which tells you necessarily how to adjust for some lighting effect in some experiment, even if you cannot control it in that experiment.
The key relation that Science has is Necessity, since it is a study of cause. If your "science" has no means of obtaining necessary relata between physical objects, then it's not a science.
> there is no inferential gap between measures and their causes in mathematics, because there are no measures
That's not true. For example in researching prime numbers, mathematicians carry out simulations and produce observational results from those simulations, without having a fully-proven formal theory to support the results.
So not being able to control a cause in an experiment is fine then - how is that not speculative science in that experiment if the theory isn't a theory of everything (and it still could be wrong!)?
I'm not trying to equate science with "perfect knowledge". I'm simply saying that if you open a physics textbook you'll find causal theories. These cannot, often, be tested in a wide variety of experimental setups. Instead they are used to design the experiments, and sometimes, post-facto, adjust the measures based on these theories.
This is perfectly fine. And it does make science in some minimal literal sense "speculative".
My issue is with an extreme depature from this method. Where you open a psychology textbook and there are no causal theories, no quantification of the number and nature of causes, and hence no way of designing experiments with controls, and so on.
This kinds of activity may, at some great distance, resemble each other -- but one is capable of reliably producing knowldge (even if in small quantity), and the other is not. That latter mechanism produces, imv, at least as much wholesale nonesense.
To get to causal theories from hypotheses needs a lot experiments that were somewhat speculative then. And, yes, there are well and poorly designed experiments (and analyses) but I don't think the speculative nature prior to having a theory is what necessarily separates them.
