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A source-graded measurement reference for people analytics practitioners, researchers and survey designers.

Principia — better measures, stronger evidence: look up a construct, inspect the evidence, copy a free scale (2026-09-29).

I built Principia as a practical reference for people who have to defend how they measured engagement, psychological safety, leadership and other workplace constructs. Measurement is where this work earns or loses credibility. The field has decades of useful research, but its definitions, instruments and findings are spread across papers, handbook chapters and supplementary material. A team can end up using a familiar label without being able to show what its questions measure or why the supporting evidence deserves confidence.

Principia brings that material into a usable reference. It organizes constructs and instruments around graded sources, and it releases Principia-owned scales under CC0 when they are available. Published entries pass curator review, while the source trail remains attached for people and software using the evidence.

I want Principia to become the dependable evidence foundation for workplace measurement. The practical test is simple: a practitioner should be able to walk into a meeting and state what was measured, how it was measured and which evidence supports the choice.

Who it is for

People analytics practitioners, researchers and survey designers use Principia to define a construct, choose an instrument and show the evidence behind it. It is especially useful when a measurement choice will be reviewed or challenged and the practitioner must explain what was measured, how it was measured and why the choice is suitable.

The problem

Workplace measures often arrive as a familiar label and an unexamined score. Engagement, psychological safety or leadership can mean one thing in a research paper, another in a vendor survey and another inside a dashboard. When someone asks why those items were chosen, whether the instrument can be used or what evidence supports the claim, the answer is scattered across papers, handbooks and rights notes. Practitioners are left defending measures they may not fully trust, and weak choices carry into analysis, reporting and decisions.

What I built

Principia is a source-graded organizational measurement reference for professionals who measure things at work. It is built so a measure can carry its definition, research support and usage status from survey design into analysis and reporting. Principia organizes definitions, scope notes, evidence grades and related instruments by construct. When a Principia-owned scale exists, it is available under CC0. Every published entry is graded by its sources and passes curator review. Source files retain SHA-256 fingerprints, and a daily Merkle anchor makes the provenance record tamper-evident. The public site and read-only APIs use the same structured evidence for constructs, prior estimates and instruments.

What is new in it

  • Each effect and instrument keeps its study-quality grade, so readers and connected tools can distinguish stronger evidence from thinner support instead of treating every citation as equivalent.
  • Principia distinguishes its own CC0 scales, public-domain instruments and instruments it can describe and cite without reproducing their wording. Practitioners can see what may be copied before designing a survey.
  • Source files carry SHA-256 fingerprints, while a daily Merkle anchor makes the provenance record tamper-evident. Later changes can be detected instead of silently replacing the record.
  • The public pages and read-only APIs draw from the same structured evidence, allowing software to use constructs, prior estimates and instruments without creating a separate measurement reference that can drift.

Where it stands

The aim is for every workplace measure used in a survey, analysis or dashboard to carry a clear definition, a source trail and a plain account of instrument rights. Principia is live at peopleprincipia.com and, as of September 2, 2026, held 526 constructs, 879 instruments and 1,074 measured effects.

More screens

How it works

Curation — source literature flows through CanonicAI extraction into the registry.

Curation — source literature flows through CanonicAI extraction into the registry.

Journal articles, meta-analyses, and handbook chapters flow into the CanonicAI pipeline and exit as construct definitions, instrument rows, and effect-size records. SHA-256 tracks every source file; the registry is downstream of extraction, not parallel to it. Curator-gated throughout — nothing promotes without a curator pass.

Canonical priors + public handbook + MCP — evidence store behind the offer.

Canonical priors + public handbook + MCP — evidence store behind the offer.

The public offer is the handbook: construct definition, open CC0 scale, cite. Behind it: canonical priors (typed Bayesian-prior outputs consumable over MCP by the portfolio tools), VoI at `/api/v1/voi` for single-tuple prior shortcuts, and construct-family browse. Downstream consumers — the toolbox, Performix — vendor typed reads from the registry rather than re-fitting their own. The evidence store powers the offer; it is not the offer.