From Masking to Metrics
In technical systems, cognitive load shapes what we detect — and what we overlook.
From Masking to Metrics introduces the Masking Load Reallocation Model (MLRM), a conceptual framework proposing that sustained autistic masking functions as chronic extraneous cognitive load.
The paper argues that changes in masking intensity may shift detection thresholds in structured analytical systems, influencing how quickly drift, metric distortion, and governance failures are detected.
Inside the paper:
Masking Load Reallocation Model
• detection thresholds
• governance vigilance
• AI as load modulator
• falsifiable predictions
Conceptual framework
• empirical validation proposed
12–15 minute read • February 2026
When KPIs Lie
In a world where AI optimizes what you measure, KPIs can start hallucinating progress.
When KPIs Lie shows how proxy metrics become self-reinforcing loops—often without anyone “cheating”—and introduces Trust Signal Health, a practical integrity layer that flags drift before it turns into rework, escalations, and churn.
Inside the paper: KPI feedback loop • governance signals (DAR/DRL/DOV/POR/TER)
SII integrity constraint • 8–10 minute read • February 2026
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