Observation Integrity
Expose missing observations, provenance, possible state dependence and data-quality limits. Reporting mechanisms are often not identifiable from reports alone.
BenchEWS · Studio 3.0 / ECHO
Public project plan and preliminary requirements · October 13, 2026
BenchEWS analyses warning signals in observed systems. SCF adds a prior question: are observations and missing feedback suitable for interpreting those signals reliably?
PLANNED / SPECIFICATION — not implemented, verified, or released
The SCF manuscript “When Does Suppressed Feedback Impair Self-Correction?” studies selective reporting, receiver models and early-warning artefacts in synthetic dynamical systems. It is not peer-reviewed and does not study real organisations. Published October 13, 2026 as a Zenodo preprint (v1, DOI: 10.5281/zenodo.23345829). The publicly displayed PDF has 36 pages; its version still needs reconciliation with the previously reviewed 38-page manuscript.
Expose missing observations, provenance, possible state dependence and data-quality limits. Reporting mechanisms are often not identifiable from reports alone.
Compare naive and mechanism-aware decoding under explicit assumptions. Known thresholds and mechanisms are best-case assumptions.
Test variance, AR(1) and other warning indicators against selective missingness, level shifts, truncation and threshold errors; include level-only negative controls.
Separate information loss from decision harm; show assumptions, alternatives, uncertainty, null states and reproducible evidence.
No automated diagnosis of intentional suppression, global risk score, causal claim about organisations, certain tipping-point prediction or unsupported recommendation. Studio 2.0 remains unchanged.