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Intimacy
Science and art naturally overlap. Both are a means of investigation.
Both involve ideas, theories, and hypotheses that are tested ... In ancient Greece, the word for art was techne, from which technique and technology
are derived - terms that are aptly applied to both scientific and artistic practices (Robert Eskridge, The Art Institute of Chicago).
Doing science means analyzing problems, searching for their
causes, and developing solutions. Science is an emergent process
which takes shape as understanding increases and converges towards
a better understanding of what happens. It is a cyclic process
of action, critical reflection, and continuous refining of hypotheses
and methods in the light of the understanding developed in the
earlier cycles.
A science that makes sense is for everyone and can pursue
action (change) and research (understanding) at the same time.
If it is used properly, science is a tool for enacting societal
change for the better.
In practice, science is made for those that provide most of
the funding, for powerful corporations and organizations, for
companies, governments, and political parties. Scientists produce
findings that only serve the interests of those bodies that they
are employed by or receive research funds from [Martin, B. (1992).
Scientific
fraud and the power structure of science. Prometheus,
10, 83-98]. Diet and weight recommendations exemplify this
problem. "The thing to keep in mind about the USDA Pyramid
is that it comes from the Department of Agriculture, the agency
responsible for promoting American agriculture, not from the
agencies established to monitor and protect our health"
[Willet, W. C. (2002).
Eat, Drink, and Be Healthy: The Harvard Medical School Guide
to Healthy Eating].
The BMI (Body Mass Index) tables which are used to
determine whether a person should lose weight exemplify how harmful
science can be when it is used improperly for corporate profit.
Insurance companies developed these tables in the 20th century,
claiming that large insurance studies show that those that comply
to these standards live the longest, that obesity is a health
hazard that has an adverse effect on longevity, and that the
risk for morbidity and mortality accompanying obesity is proportional
to the degree of overweight. However, studies that were not done
by insurance companies have not found that weight and longevity / morbidity
are related to each other in this way.
The BMI is a simple means to define obesity, a height / weight
ratio that is computed by dividing your weight in kilograms by
the square of your height in meters. Calculating risks provides
the justification for insurance rates. After the BMI was
included, many people that would have been eligible for standard
insurance rates before had to pay higher rates because they were
categorized as overweight. By BMI standards, most people
are overweight or obese. The authors of these tables, scientists
that were employed by insurance companies, did what they were
expected to do: They produced a risk which is common, easy to
calculate, justified higher insurance rates, and increased their
companies´ profit significantly.
To this day, there is no scientific justification for the
assumption behind these tables. Nonetheless, they continue to
be used by an enormous industry in a way that is thought to have
caused much of the sudden "explosion" in obesity [Campos,
P. (2004). The
Obesity Myth]. Insurance companies claim that they have evidence
that the BMI predicts morbidity / longevity, emphasizing
that their evidence is based on large studies. They discount
other studies that did not confirm their model by claiming that
these studies were irrelevant because they had general methodological
problems, for example, the sample size was too small or not representative.
However, testing simple models such as "BMI predicts morbidity"
and using large samples is methodologically problematic as well.
Statistical tests of significance depend on sample size and model
complexity. If the sample size is large enough or the model is
very simple, the probability that the variables under study will
be significantly related is very high. This means that the probability
of finding a significant relationship between variables that
are not really related increases with sample size, and testing
more complex models - analyzing relationships between more than
two variables simultaneously - could reveal that another variable
such as personal income is what truly predicts a person´s
BMI and their morbidity/longevity.
A science that serves the interests of everybody needs to be
funded by everyone!
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For
Some Girls,
the Problem With Math
Is That They're Good at It
The New York Times
Tue Feb 01 2005
01:47:00 GMT+0100
Stories
of Mr. Keuner
The Milgram Experiment
Studies on the
Obedience to Authority
WANTED:
HITLER'S SCIENTISTS
The New York Times
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