This morning I sat through my first biology lecture in 6 years. It's been a while, but it's all coming back to me - especially the phospho-lipid bilayer. Today, however, we laid a little groundwork for our future discussions of cell biology. Part of that was Del Ratzsch's definition of science:
"Science is a theoretical explanatory discipline that objectively addresses natural phenomena with the general constraints that theories must be rationally connectable to generally specifiable empirical phenomena and that it normally does not leave the natural realm for the concepts employed in its explanations."
Besides being a mouthful, this brings up some interesting questions for me, primarily with my professor's view of objectivity. He claimed that in the scientific realm objectivity meant examining and addressing all data available, not just data that supports your claims. For him objectivity was not necessarily about approaching a question without prejudice or bias, except when they prevent you from examining certain data. The more I think about this way of looking at objectivity, the more I like it. We obviously have our prejudices, but objectivity is lost when they blind us to information out there.
It's got me thinking about objectivity as a form of humility. In the scientific sense, we lose it when we start to think we don't need data from certain sources. An unbiased person has the humility to acknowledge and evaluate information from all sources. Any thoughts from the peanut gallery?
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"Humility, that low, sweet root, From which all heavenly virtues shoot" - Thomas More
2 comments:
Do you think this model of objectivity is helpful in other arenas? Say, doctrine of God, for example? I think the idea of trying to account for all the data that exists is wise. I suppose the trouble with humanities is that not everyone interprets things the same way, and we aren't always sure what information is credible.
there's some morning thoughts from the peanut gallery.
I do think it's helpful in other arenas. You're right that this thinking says nothing about how to evaluate data, which puts us back in the same palce we started - full of bias. I guess it is just a myth.
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