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   RE: [xml-dev] The perils of P18S (was Re: [xml-dev] Why RDF is ha rd )

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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?
-----Original Message-----
From: AndrewWatt2000@aol.com [mailto:AndrewWatt2000@aol.com]


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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