Foucault, Veyne and Graduate School

This is not so much an apology to the "métier d'historien," to borrow from Marc Bloch, or for that matter, a great insightful essay. Rather, it is a somewhat introspective realization from reading Foucault's Archaeology of Knowledge, and beginning a fun reading book Fooled by Randomness.

This particular point takes me back two years, a book by Elizabeth Clark (History, Theory and Text), and a rather nasty comment I made about the idea that historical facts don't matter. I was a young graduate student, in a quest for certitudes both professionally and personally, so the idea that historical facts a) don't exist, and b) even if they did don't matter was a rather disturbing experience. I could not "just leave it," despite my advisor's warnings (in French) "attention Laurent."

Two years later, a slightly different perspective emerged from my graduate work, and my duty as a historian. First, and I must thank a Professor a German History Professor at PennSt who chastised me me (as much as one can be chastised over a beer in a bar) for being far too destructive. He said, and I can only paraphrase, that we have come a long way since we assumed that we could look at texts without biases. Conclusion: stop being so critical. Methodological conclusion: each book has its value, if only (to be reductive of the books one truly hates) the presence of an argument built on a set of sources that is coherent and cogent.

Second, and this goes back to what I intended to write initially, historical facts do not matter, or rather, and this is Paul Veyne's caveat, they matter in a series. For instance, Louis XIV's death, while, as a student in the French high school system, an important date to be memorized (I can't remember the precise date), has no bearing on all aspects the life of Martin, paysan du Languedoc... that is, unless the question asked requires one to consider the death of Louis XIV.

So, my pursuit is to reorganize data sets to highlight different historical processes. Doing so, I cannot assume that what has been done (or is being done) is wrong, or even lacking. Simply their data set has its coherence, and it is different from my data set and its coherence.