The biggest limitation to this is that all of this hermeneutics stuff takes place in the math/comp domain without translating it back into the linguistic/cultural. But that’s where we are looking next. I suspect we will have to learn how to read networks/geometries/etc as text.

5/5

I’m skeptical that performance measures/benchmarks will be able to help us reason about model hermeneutics unless we can make their own interpretive lenses visible. Our framework offers a way to do this by contrasting a benchmark, as a modeling decision, against alternatives.

4/5

This is what we attempt to do in the paper. We start with a very simple theory of semantic models based on the distributional hypothesis and Saussure. From that we develop a theory of model semantics in which models themselves function as signifiers.

3/5

But to contrast a model against alternatives requires a theory of what exactly they measure that is sufficiently rigorous so as to motivate a measure of the semantic differences between models (without reducing semantics to a narrow task).

2/5

That is wild!

Hey thanks! Definitely going to submit elsewhere. But it’s hard because this is all very specific to a subfield of a subfield that has like 3 publication venues. It seems like the natural home for this work but I’m second-guessing that.

“Oh, you’re bringing up epistemology? Name every philosopher of science.”

But I’m glad to know that The Structure of Scientific Revolutions lays out a case of how supervised learning can easily mask cultural complexity in computational humanities work. Shame we’re still falling short on such an old and obvious problem.

Sadly, we limited ourselves to C. S. Peirce, Terrence Deacon (by way of Eduardo Kohn) and a bunch of current phil sci that is actually about ML.

Jean Baudrillard, Umberto Eco, Thomas Kuhn, Paul Feyerabend, Ian Hacking, Bruno Latour, Donna Haraway, and the anthology Models as Mediators edited by Mary Morgan and Margaret Morrison.