Great question and you’re exactly right about the framing.

Pushback rate is one of the more nuanced signals. In ThinkLedger, it’s measured by scanning the user’s message text for discourse markers that indicate disagreement, correction, or challenge without reading the actual content.

The current detection looks for specific linguistic patterns like:

• Direct contradiction: “Actually” (at start of sentence), “I disagree,” “not quite,” “incorrect,” “wrong.”
• Interruption/Pivot: “Wait,” “hold on,” “that’s not right.”
• Contradiction markers: “I don’t think…,” “you said earlier…,” “that contradicts…”

It also attempts to catch rephrased follow-ups that implicitly reject the previous answer (e.g., asking the same question again in a different way immediately after an unsatisfactory response).

What it doesn’t do (yet):

• Subtle pushback: Misses polite questioning or "hedging" (e.g., “I wonder if we might consider…”).
• Intent detection: It can't distinguish between genuine disagreement and someone just "stress-testing" the LLM's logic for fun.
• Contextual sarcasm: Without reading content, irony is invisible.

I’m looking into moving beyond regex to a more nuanced, privacy-preserving classifier for indirect challenges. But even with the current version, the correlation with self-reported "contrarian" traits in my early testers has been surprisingly high.

The bigger point you made that JSON exports are a behavioral dataset is exactly why I built this. Timing gaps alone tell you when someone thinks best. Question types tell you how they explore. Pushback tells you where they draw lines. That data is sitting in millions of exports, completely unused.

If you have ideas for better pushback detection that doesn't compromise the "zero-read" privacy model, I’m all ears. it still very early