Overview
What this research program is and why it exists. The frame the rest of the work hangs on.
research / arc / adaptive-measurement
Adaptive measurement & psychometric architecture
What does it take to do longitudinal measurement that compounds across studies without confounding? Item-response accumulation, adaptive sampling, RID/SID architecture, instrument validation, and the canonical-vocabulary discipline that lets sibling studies share evidence rather than silo it.
Why this matters
The portable claim — what this arc lets you understand outside the surface domain.
Most fields with measurement traditions reinvent instruments per study and pool effect sizes after the fact. The adaptive-measurement arc is a bet that the right substrate — questions as first-class objects with stable IDs, response data accumulating across studies, instrument-quality grading enforced at write-time — is what lets a measurement program actually compound. The technical contribution shows up at Vela (Reincarnation engine), Principia (organizational-measurement registry), and Namesake (within-session preference calibration). The methodology travels: anywhere rigorous measurement is unevenly distributed across the working community is a candidate for the same architectural pattern.
Spans
Products this arc cuts through. Each application is sometimes the lead empirical apparatus, sometimes the funding/data-collection platform.
Read first
The general-audience entry point for this arc. Drill down below for the full set.
Principia — overview
From PrincipiaA source-graded survey of organizational measurement, output as a book + queryable database, rendered from one underlying registry. Outside-reader brief on what it codifies, why now, and how it relates to the rest of the portfolio via @measurement/core.
Read →Drill-down — full arc surface
Cross-product. Source application shown on each entry.
Methodology
How the work is done — instruments, protocols, the standards each report inherits.
Methodology
VelaAdaptive measurement, dual-grade corpus ingestion, RID/SID architecture, and the standards reports inherit.
Multi-faceted vs. dual-grade ingestion
VelaMethodological note on the Vela ingestion architecture — when to favor the dual-grade pattern, when multi-faceted extraction wins.
Methodology
NamesakeHow cultural-diffusion research is conducted at Namesake — instruments, sources, and standards.
Methodology
PrincipiaSource selection, source-quality grading rubric (A–D), statistical-metadata extraction protocol, schema discipline against @measurement/core, versioning + snapshots, novelty verification, threats to validity, author position.
Methodology
AI–Human InteractionMulti-LLM deep-research with synthesis discipline; Penwright production-instrument design; the Penwright Measurement Framework (six skill dimensions, six derived indices, three measurement layers, five-step learning loop) with four non-negotiable failure modes; pre-registered hypotheses; genre-aware analysis required.
Methodology
People Analytics ToolboxforthcomingMethodology
PerformixforthcomingForthcoming — adaptive CAMS measurement, protected-feedback substrate, anchor-registry ingest, dual-grade corpus discipline, and evidence-chain standards the diagnostic inherits.
Reports
The actual research findings — phased results, research-question briefs, applied analyses.
RQ4 — Individual differences
VelaWhich dimensions of variance separate participants and how stable they are.
Desire Index — Vol. 1 analysis
VelaFirst-volume analysis of the desire-index instrument.
Paper 4 — A Measurement Framework for AI-Augmented Writing Skill Development
AI–Human InteractionTier-2 foundational paper. Operationalizes the Penwright Measurement Framework that underwrites every subsequent empirical paper. Three measurement layers (output · process · development), six skill dimensions, six derived indices (WQI · II · INTI · OI · MI · DV), five-step learning loop, and four non-negotiable failure modes acting as veto conditions. Measurement-theory positioning (reflective-vs-formative · latent-vs-observed · why standard psychometric scaffolding only partly applies under continuous measurement and model drift). Compares to HCI usability scales, educational skill assessments, and standardized writing rubrics — none fit for purpose for capability-development measurement. Names what's measurable in v1, what awaits Wave-2-through-6 features, what requires longitudinal data, and what requires external validation.
Paper 6 — Learning Loops in AI-Augmented Writing Systems
AI–Human InteractionTier-2 measurement-and-mechanism paper. Companion to Paper 4 — where Paper 4 specifies what gets measured, Paper 6 specifies the loop architecture the measurement is against. Five-step learning loop (write · analyze · reflect · practice · re-measure) and four intervention types (teach moments · constraint challenges · counterposition drills · reconstruction exercises). Theoretical positioning across cognitive apprenticeship (Collins, Brown & Newman), working-alliance theory (Bordin), retrieval practice and desirable difficulties (Roediger & Karpicke; Bjork), and self-regulated learning (Zimmerman). Argues that output-quality optimization is structurally incapable of producing capability development. Four threats to validity, three production-data tests.
RID/SID adaptive measurement
People Analytics ToolboxforthcomingAnticipated thread — cross-study item-response accumulation without confounding.
Preregistrations & protocols
Studies and intervention protocols filed before execution.