Yes I misunderstood your post. I appreciate your taking some responsibility as the communicator (e.g., probabilities and likelihoods are pretty quant!), but your post could have also been reasonably read as referring to inexplicit models, and that is on me. Communication breakdowns are rarely on one party alone.
I agree that cliodynamics has been a dicey application of quant modeling to history—the valuable parts of it are generally in the inexplicit modeling rather than the real quant model per se. Inexplicit forecasting is more common, but it’s also less testable (anything but the most extreme falsification fits!) and then again not really all that different from what historians already do. The status quo in history is inexplicit modeling in expert judgment, so I’m not sure that relabeling it or asking historians to think less-inexplicitly-but-not-quite-explicitly will do much to move the field.
Qualitative work is not fated to fall into “just-so” stories, and neither is quantitative work destined to be “scientism.” The key is understanding the internal and external validity of your research.
Yes I misunderstood your post. I appreciate your taking some responsibility as the communicator (e.g., probabilities and likelihoods are pretty quant!), but your post could have also been reasonably read as referring to inexplicit models, and that is on me. Communication breakdowns are rarely on one party alone.
I agree that cliodynamics has been a dicey application of quant modeling to history—the valuable parts of it are generally in the inexplicit modeling rather than the real quant model per se. Inexplicit forecasting is more common, but it’s also less testable (anything but the most extreme falsification fits!) and then again not really all that different from what historians already do. The status quo in history is inexplicit modeling in expert judgment, so I’m not sure that relabeling it or asking historians to think less-inexplicitly-but-not-quite-explicitly will do much to move the field.
Qualitative work is not fated to fall into “just-so” stories, and neither is quantitative work destined to be “scientism.” The key is understanding the internal and external validity of your research.