Research by product.

Why this matters

On its surface, Vela's research is response to the materials that have always taught us what it is to be human — figurative art, the vocabulary of emotion, literature, and the behavioral-science corpus on how people form, feel, and become. Underneath, it is an instrument: how does desire — move-toward — separate from preference (like)? How do compositional features mediate response? How stable are individual differences? The methods generalize. They speak to consumer-behavior research, aesthetic measurement methodology, taste calibration in any high-volume domain, and the design of adaptive measurement instruments well outside HR. The lenses are particular; the questions are general.

Why this matters

Naming is the obvious-seeming domain. The research underneath it is cultural diffusion — how cultural objects spread, what predicts breakouts, and where the predictability ceiling lives. Findings extrapolate beyond names: how marketing campaigns succeed or fail, how misinformation propagates, how innovations diffuse through organizations, why fashion cycles look the way they do, what separates lasting public discourse from brief virality. Names are the testbed because the corpus is dense and the temporal signal is clean. The implications travel.

Why this matters

Forthcoming. Anticipated frame: decisions under uncertainty in fantasy extrapolate to executive compensation modeling, medical-decision support, capital allocation, and public-policy tradeoffs — any domain where Monte Carlo plus structured information design beats single-point estimates. The off-season game design thread is itself a study in how to extend a niche industry's revenue cycle.

Why this matters

The principal-issues thesis is the spine. It says every domain has a load-bearing measurement set, and most domains are stuck because they have not named it. People analytics is the demonstration; the same logic applies to any field where rigorous measurement is unevenly distributed across organizations. The platform is built to make load-bearing-set delivery executable at solo cadence — which is the operating-system claim underneath every other portfolio item.

Why this matters

Most organizations treat performance as a rating problem. Performix treats it as a diagnostic problem: which of four conjunctive conditions is the load-bearing constraint *right now* for *this team*, and what is one intervention that would unblock it? The protected-feedback substrate makes the model legible — employees observe social, managerial, and structural blockers better than any external instrument can measure them. The research program generalizes: binding-constraint diagnosis, adaptive measurement with embedded feedback, and peer-reviewed I/O psychology ingested into a structured evidence substrate — applicable anywhere leaders need to act under uncertainty about team activation, not just in HR analytics.

Why this matters

The productivity claims being made for AI coding tools are largely grounded in agent-side measurements — lines produced, tasks completed, time-to-PR. If the Ironies of Automation (Bainbridge 1983) are operative — operator vigilance falling as agent reliability rises — those measurements systematically overstate net effect. The DevPlane research program tests that prediction with continuous production telemetry on a real operator running real agents on a real, multi-month codebase. The methodology generalizes: any team running heterogeneous tools through a coordination layer (multi-tool ops dashboards, hospital handoff systems, distributed scientific instruments) shares the same shape of problem.

Why this matters

Load-bearing organizational measurement is unevenly distributed across organizations and disciplines. The same construct gets measured five different ways across five different studies; effect-size tables live scattered through chapters of textbooks; high-quality instruments get reinvented in low-quality form because the original is paywalled or buried. Principia exists to give builders, researchers, and operators a single graded, sourced, queryable place to look — and to give the People Analytics Platform a canonical measurement vocabulary it can subscribe to. The methodology generalizes: source grading, statistical-metadata extraction into a shared schema, novelty verification before publication, queryable indexing — the same shape works for clinical psychology, educational measurement, marketing research, or any field where rigorous measurement is unevenly distributed.

Why this matters

Existing AI–human-interaction research clusters in single-session, individual-level, descriptive studies. We know remarkably little about what happens to a person's reasoning, vocabulary, social life, or skill acquisition over months and years of daily interaction with capable AI systems. If the Ironies of Automation generalize — operator vigilance falling as system reliability rises — then the productivity story being told today systematically overstates net effect. The portable contribution: a measurement framework for AI-augmented capability development with explicit failure modes, a pre-registered twelve-paper empirical program, and a theoretical bridge between mainstream HAI and the bodies of theory it has under-engaged (companion-species studies, cognitive apprenticeship, working-alliance theory, distributed cognition, niche construction, transactive memory, ritual studies, indigenous relational ontologies). The methods generalize beyond writing — to coding, design, research, education, and clinical practice.