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Ah,
ok. My bad.
The
behaviorists refer to that as "superstitious learning", the accidental
acquisition of relationships that are not related, therefore, measuring
items
and drawing unwarranted conclusions. The classic example
is
spontaneous generation: observations that maggots grew on rotting
meat
and assuming this was the spontaneous generation of life. One
can
measure the rate of growth of the maggots and make assertions
that
this relates to the spontaneity rate. Of course, it will begin to
fall
apart if one does more measurements, changes the meat to
corn
meal, whatever. Failing to keep testing from different points
of
view suggested by the hypothesis is how superstition
proliferates.
I
quite agree about the dangers of superstition. That is why I am a
bit of
a wonk where history is concerned, and why the web concerns
me
(unconstrained and uncontested repetition of false assertions fed
into
the largest control/feedback loop ever built).
It
seems to me, that this what RDF enables one to check, at least,
in the
same sense that one can find out if two or more sources made
the
same assertion. What it does not check is superstition.
If many
the
experts believe that spontaneous generation is an accepted fact,
and
therefore, most of the metadata generated asserts that, can RDF
find
that falsehood? I don't think so unless related facts in a separate
set of
assertions don't jibe.
So we
may be biased, but without correlations among different classes
of
measured events, how can we tell?
len
Len,
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
there.
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