TheoryTHY-2026-0107v0.1
Binary residual quality measurement (IBQF / BRQM)
A 0–10 rating asks the respondent to perceive, build a reference, calibrate a scale, integrate dimensions and map to a number; the burden is highest exactly when the measured state is heaviest. BRQM instead takes many local, concrete, single-construct, non-numeric binary or pairwise answers b_i ∈ {0,1} and lets the measurement system reconstruct a latent multidimensional quality θ̂_H with Bradley–Terry / Thurstone / IRT-type models, adaptive item selection by information gain per human cost, blind and counterbalanced designs, and an explicit rater-disagreement structure — because binary observation is not binary phenomenon and disagreement is not error.
Definitions
definitions- BRQM: 𝔔_H → {0,1}^N → θ̂_H; primitives b^abs ∈ {0,1} and b^pair ∈ {A, B}; skip = missing metadata, not a third value.
- Good-item conditions C_B = (local, single construct, concrete, temporally bounded, non-numeric).
- Adaptive selection i* = argmax E[IG_i] / C_H(i); stop when U_H < ε.
- Human residual object 𝔔_H^IBQF = (θ̂_H, Σ_H, N_obs, D_R, C_H, B_H, U_H, Grade_H); H-grades E uncontrolled rating … A+ cross-context validated.
Assumptions
assumptions- A latent continuous quality exists behind local judgments (IBQF/FDCS micro-binary → macro-continuous emergence).
Claims
claims- BinaryObservation ≠ BinaryPhenomenon; HumanObservation ≠ HumanScaleConstruction; NumericRating = State + ScaleUse + Context.
- MeasurementBurden ≠ MeasuredQuality; Disagreement ≠ Error; MeanPreference ≠ PreferenceStructure; Reliability ≠ Objectivity.
- HumanResidual ⇏ HumanOverridesFormalTruth — the hard gate is applied first.
Formalisation
formalization- P(A ≻ B) = σ(q_A − q_B) (Bradley–Terry); P(b_rij = 1) = σ(a_i θ_j − d_i + β_r), multidimensional λ_i^T θ_j, context-conditioned θ_j(c).
- C_rating = C_perceive + C_reference + C_scale + C_integrate + C_map; C_binary = C_local perceive + C_choose; C_binary, C_pair < C_rating is the hypothesis.
Predictions
predictions- With well-designed items, binary/pairwise adaptive protocols beat direct numeric rating on response time, consistency, dropout, predictive validity or fatigue in at least some settings (Falsifiable Claim 3).
Falsification / failure conditions
falsification_conditions- If binary/pairwise protocols are worse than direct 0–10 rating on all of response time, consistency, dropout and predictive validity, the low-burden hypothesis must be revised.
Known limitations
known_limitations- Not a clinical scale proposal; builds on EveMissLab's internal IBQF/MTF and FDCS theory (2025); Experiment B has not been run.
Evidence
| Source | Relation | Target | Status | ID |
|---|---|---|---|---|
EXP-2026-0104 Experiment B — binary vs numeric human measurement (declared) | tests | THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | ACTIVE | REL-2026-0493 |
Relations
| Source | Relation | Target | Status | ID |
|---|---|---|---|---|
THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | extends | THY-2026-0106 Structured quality, hard gates and the specification–verification separation | ACTIVE | REL-2026-0372 |
RES-2026-0102 Quality line — measuring outcome quality without asking humans for a score | develops | THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | ACTIVE | REL-2026-0364 |
THY-2026-0108 Typed, versioned quality ontology for high-ambiguity artifacts | extends | THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | ACTIVE | REL-2026-0373 |
PAP-2026-0107 Paper 07 — Do not ask humans to numerically score their own feelings: IBQF binary measurement and low-burden quality evaluation | formalizes | THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | ACTIVE | REL-2026-0416 |
EXP-2026-0104 Experiment B — binary vs numeric human measurement (declared) | tests | THY-2026-0107 Binary residual quality measurement (IBQF / BRQM) | ACTIVE | REL-2026-0493 |
History and provenance
- Canonical URL
- https://evemisslab.com/ai/theory/THY-2026-0107/
- Machine-readable
/ai/theory/THY-2026-0107/index.json- Snapshot
AI-SNAPSHOT-v0.1-fe85b9694a45- Provenance
source- EveMissLab research collection: Intelligence Physical Metrology (真本體論13)
extracted_by- Splice (Claude Code), reading the canonical UTF-8 sources and each package's own reports
extracted_at- 2026-09-11
generator- tools/extract_all.py
claim_boundary- status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports