Research

Collaborative studies

The framework develops through pilots and independent research; both are invited.

Research collaboration · Standing challenge · Correspondence on the record

Collaborative studies are opportunities to test control strategies in live work systems: what works, what needs improving, and what more needs to be learned. A study can run alongside an AI pilot, inside a deployed workflow, or on the framework itself.

The research opportunity

HAOP is deposited, versioned, and citable, and its measures are candidates awaiting empirical work. Thesis-scale questions are open: whether ARECC finds what conventional assessment misses, whether the diagnostic reads repeat across teams, how Verification Overrun prevalence can be measured in the field, and how the work-as-imagined gap behaves under AI adoption. I share protocols and instruments, and independent results are welcome whether or not they favor the framework. AI governance is a study area of its own: whether governance policies and oversight programs translate into functional controls at the consequential transitions they claim to govern.

Show me where it breaks

Challenge a mechanism or tell me what the tools miss. I read every message. Exchanges that sharpen the framework publish, with permission, in the correspondence record.

What you can work with

What exists to study

The full record, with concept DOIs that always resolve to the latest versions, is on the research page.