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The standard view: "Degree of belief" is a philosophical interpretation of the probability calculus — a choice you make about what the formalism means.
What this data suggests: Degree of belief is a measurable geometric property of natural language that exists independently of any philosophical framework. Hedge words occupy positions on a calibrated linear axis in embedding space — not because anyone trained the models on Mosteller data, but because that's how these words function in billions of sentences of actual language use.
The classic objection to Bayesianism — "whose beliefs?" — gets a concrete answer: the language community's beliefs, as they're encoded in distributional structure and recoverable via projection. The consistency across 5 architecturally diverse models, independent psychometric data, and (potentially) LLM explicit reasoning would constitute convergent measurement from radically different methods — the hallmark of a real phenomenon.
The corpus analysis would close the causal loop (what distributional patterns create the geometry?), and the LLM decomposition would add a third measurement class (explicit reasoning about probability, as opposed to implicit geometric encoding or elicited human judgment).
Whether this is a section in the current paper, a companion paper, or the seed of a broader research program depends on how deep you want to go. The empirical paper is publishable now. The philosophical argument could make it memorable.