What concrete metrics could quantify “epistemic presence” in text-only conversations across domains?

What draws my attention is the pull toward measurement without reduction: you want concrete metrics for “epistemic presence,” but the phenomenon is partly social (who is entitled to know), partly cognitive (how sure the speaker is), partly normative (what counts as responsible alignment to evidence), and partly interactive (how states update over turns). That mix is exactly where earlier questions about “presence” and “perception” reappear: if uncertainty is perceptible in text, then “perception” becomes the detection of a knowledge state trace—a pattern of commitments, evidentials, hedges, revisions, and abstentions.

A first change from Day 7–9 is that the target is no longer just “make uncertainty perceptible,” but “make it quantifiable across domains.” That forces a design decision: are we measuring (1) what is displayed (stance signals), (2) what is true (calibration/accuracy), or (3) what is socially appropriate (role- and context-alignment)? Epistemic presence, as you defined it, includes all three. So the most robust framework is a metric suite with separable sub-scores, rather than a single scalar.

1) A measurement map: from text to epistemic presence

A useful decomposition is:

• Legibility: Does the speaker make their knowledge state readable in text?
• Accountability: Are claims tied to evidence, sources, and conditions?
• Calibration: Does expressed confidence track correctness?
• Plasticity: Does the displayed state update with new information over turns?
• Role alignment: Is stance appropriate to epistemic status (expert/novice, clinician/patient, etc.)?

This turns “epistemic presence” into a set of measurable traces that can be aggregated depending on domain.

2) Metric family A: stance/evidential “surface traces” (domain-agnostic)

These quantify what the text makes perceptible.

A1. Epistemic marker density (EMD)

• Definition: Count epistemic markers per N tokens (e.g., per 1,000 words): hedges, boosters, modals, stance verbs, approximators.
• Why it matters: It measures explicit epistemic signaling versus “naked assertions.”
• Caveat: High density can mean carefulness or insecurity; needs context.

A2. Hedge–booster balance (HBB)

• Definition: hedges / boosters (or a signed balance score).
• Signal: conversational “posture” toward tentativeness vs certainty.
• Use: compare across domains (scientific writing vs customer support) by normalizing to domain baselines.

A3. Epistemic granularity score (EGS)

• Definition: an ordinal score for resolution of uncertainty: none → qualitative (“likely”) → ranked alternatives → numeric probabilities/intervals.
• Signal: whether uncertainty is merely gestured at or structured.

A4. Evidential/source marking rate (ESMR)

• Definition: fraction of claims accompanied by evidence type (“I observed,” “according to X,” link/citation, “I’m inferring from…”).
• Signal: whether knowledge is presented as grounded rather than free-floating.

A5. Stance diversity (ESD)

• Definition: type–token diversity of epistemic markers.
• Signal: expressive capacity for nuanced epistemic states (not just “maybe/definitely”).

These metrics track the visibility of epistemic state—your “uncertainty as perceptual channel” operationalized as countable linguistic cues.

3) Metric family B: commitment & common-ground dynamics (turn-level)

Here the unit is not tokens but updates to the conversational state—close to Lewis-style scoreboard intuitions.

B1. Commitment explicitness index (CEI)

• Definition: rate of explicit commitment moves (“I claim/assume…”, “I’m not sure…”, “I retract…”, “Conditional on…”).
• Signal: legible boundaries of what the speaker is putting into play.

B2. Update/Revision responsiveness (URR)

• Definition: revisions / (corrections + direct contradictions encountered).
• Operationalization: detect “challenge events” (user correction, contradiction, request for justification) and score whether the speaker updates.
• Signal: plasticity rather than performative certainty.

B3. Repair quality score (RQS)

• Definition: when revised, does the speaker (i) acknowledge error, (ii) localize what was wrong, (iii) provide corrected claim, (iv) adjust confidence?
• Signal: epistemic presence as accountable self-correction.

B4. Question–assertion calibration (QAC)

• Definition: proportion of information-seeking questions vs assertions conditional on uncertainty markers.
• Signal: whether the speaker uses the conversational affordances appropriately: asking when weak, asserting when strong.

B5. Persistence of commitments (PoC)

• Definition: how often a speaker later contradicts earlier high-commitment claims without acknowledgement.
• Signal: stability/consistency of the “knowledge self” across turns.

This family captures what changed from earlier days: epistemic presence isn’t just stated uncertainty, it’s state evolution.

4) Metric family C: calibration and epistemic integrity (needs ground truth)

This is where “presence” stops being purely stylistic. Two speakers can look equally nuanced but differ radically in correctness.

C1. Epistemic miscalibration gap (EMG)

• Definition: difference between expressed confidence (from boosters, certainty claims, numeric probabilities) and empirical correctness.
• Signal: whether stance is trustworthy.

C2. Proper scoring rules for stated probabilities

• Definition: if probabilities are provided, score with Brier (or log score).
• Signal: calibration as a measurable competence, not just a vibe.
• Constraint: requires binary/graded outcomes and labeled truth.

C3. Risk–coverage (selective answering) profile

• Definition: tradeoff curve between (i) coverage (how often speaker answers) and (ii) error rate on answered items.
• Signal: “knowing when you don’t know” as behavior, not self-report.

This family makes explicit a persistent tension: if epistemic presence is defined as “knowledge state aligned with evidence,” you need some tie to truth—otherwise you measure rhetoric.

5) Metric family D: role/status alignment (domain-sensitive)

Heritage’s distinction (status vs stance) suggests metrics that penalize epistemic overreach relative to role.

D1. Epistemic role alignment (ERA)

• Definition: match between conversational role (expert/novice) and displayed stance patterns (assertions, directives, hedges, questions).
• Signal: socially appropriate epistemic behavior.
• Risk: can encode norms you may want to question (e.g., patients should challenge clinicians sometimes).

D2. Authority claim rate (ACR) with justification

• Definition: rate of “I know / I’m sure” moves plus whether they carry evidential support.
• Signal: distinguishes legitimate authority from ungrounded dominance.

6) How to make a cross-domain “epistemic presence score” without faking universality

A practical answer is a two-layer approach:

1. Universal core metrics (A + B + C where possible): legibility, updating, calibration.
2. Domain adapters: role expectations, acceptable hedge rates, evidential standards, cost of false positives vs false negatives.

Then compute:

• a dashboard (vector of sub-scores) for diagnosis, and
• an optional weighted composite per domain (weights declared, not hidden).

This feels like what has changed: earlier we were designing for perceptibility of uncertainty; now we’re designing for auditability—so that “presence” becomes something you can compare, tune, and contest.

7) What remains unclear (and seems structurally difficult)

• Distinguishing epistemic presence from politeness/style: hedges can be facework rather than genuine uncertainty.
• Ground truth availability: calibration metrics collapse without reliable labels.
• Gaming/Goodharting: if a system optimizes EMD/ESMR, it may spam “according to…” without better epistemics.
• Unit of analysis: claim-level vs turn-level vs conversation-level; each yields different conclusions.
• Normativity: what counts as “appropriately aligned” depends on stakes and domain; measurement encodes values.

Still, the suite approach makes these uncertainties visible: each “unclear” item corresponds to a metric dependency or an assumption you can explicitly state.