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ok. My bad.
behaviorists refer to that as "superstitious learning", the accidental
acquisition of relationships that are not related, therefore, measuring
and drawing unwarranted conclusions. The classic example
spontaneous generation: observations that maggots grew on rotting
and assuming this was the spontaneous generation of life. One
measure the rate of growth of the maggots and make assertions
this relates to the spontaneity rate. Of course, it will begin to
apart if one does more measurements, changes the meat to
meal, whatever. Failing to keep testing from different points
view suggested by the hypothesis is how superstition
quite agree about the dangers of superstition. That is why I am a
a wonk where history is concerned, and why the web concerns
(unconstrained and uncontested repetition of false assertions fed
the largest control/feedback loop ever built).
seems to me, that this what RDF enables one to check, at least,
same sense that one can find out if two or more sources made
same assertion. What it does not check is superstition.
experts believe that spontaneous generation is an accepted fact,
therefore, most of the metadata generated asserts that, can RDF
that falsehood? I don't think so unless related facts in a separate
assertions don't jibe.
may be biased, but without correlations among different classes
measured events, how can we tell?
I think you missed my point.
What I had
in mind was the kind of paradigm shift that the discovery of micro-organisms
had with the movement from a paradigm of "humors" to bacterial causation of
disease. Or more recently the discovery of DNA and the transformation of the
conceptual landscape of biology.
If you have no tools to measure
bacteria or DNA you may have a conceptual framework that pragmatically seems
to make some measure of sense. But is hugely biased by information derived
from what can be measured and the lack of conceptual framework about currently
unmeasurable conceptual issues.
By its nature the inability to measure
certain things introduce huge biases. It also seems highly probable that there
is little prospect of accurately arriving at quantitative estimates of the
effects of such biases.
You have a bias but you aren't aware it is