Testing the Instrument:
Six-Part Battery for CAMS v1.0.1

Step 1 Is the data sound?

The project's full archive — 49 datasets covering 25 entities across up to two centuries — was checked file by file. Every number was recomputed from scratch against the canonical v1.0-Final formulas. Forty-seven of forty-nine files passed perfectly.

Separately, four countries scored at five-year intervals rather than yearly were removed from the comparative corpus so that all cross-society analysis rests on consistent annual data.

Step 2 If we measure twice, do we get the same answer?

A fresh, independent five-scorer run of Australia 2020–2026 was compared against the existing series. The two runs broadly agreed on direction but disagreed on magnitude by about twice the published error bars implied.

Step 3 Does the mathematics actually work? — The JUNO formalism audit

The v1.0-Final formalism was tested against all 2,532 society-years. The graph-theoretic core survived. The classifier layer did not.

Error 1 — Fatal: the σ_i floor clip kills three regime triggers

The published formula uses σi = (A·C/100) · max(K−S, 0.1). Because max(K−S, 0.1) is always positive, σ_i is also always positive — making negative σ_i thresholds unreachable:

Error 2 — Germany 2024 claim fails on the verified corpus

v1.0-Final claimed "Germany 2024 now correctly classifies as Local Node Failure." Observed in the verified germany_cams5 series: V̄ = 7.2, V_min = 4.4, B̄ = 0.221, unclipped σ_min = −0.77 → Strained, not LNF. V_min misses the 4.0 trigger by 0.4; σ_min misses −0.85 by 0.08. The claim rested on a series that is not in the corpus.

Error 3 — USA figures unverifiable: USA not in the corpus at the time

v1.0-Final's "USA 2026: V̄ = 3.5, V_min = −0.1, B̄ = 0.131 — Confirmed" could not be verified; no USA file existed in the corpus at the time of the claim. The USA five-scorer ensemble has since been migrated (see Step 6 below). The observed figures differ materially from the claimed ones: V̄ = 1.17, V_min = −0.70, B̄ = 0.120.

Claimed bounds: mostly hold

Step 4 Repair and recalibrate — CAMS v1.0.1

All three errors are attributable to a single root cause: thresholds calibrated on the unclipped σ_i, then a floor was added without re-deriving them. The repair is minimal and preserves the original intent.

The formerly impossible Phantom Type II category came alive: 41 historical cases, all historically exact — Weimar Germany 1931, late Qing dynasty, partition-era Poland, post-Soviet Ukraine. The category was never wrong; it was unreachable.

Classifier precedence was specified explicitly: Freeze → Systemic Crisis → Phantom Type II → Local Node Failure → Stable Adaptive → Strained. The repaired classifier was validated against twelve historical anchor cases — all twelve passed.

Fig 1. Node Value verification — computed V_i vs pre-computed values across the full 2,532 society-year corpus. Points lie on the 1:1 line; max |Δ| = 0.00.

Fig 2. JUNO Bond Strength verification — pre-computed vs recomputed B_ij. Max absolute deviation = 0.00e+00: the canonical bond-strength formula is verified exact across the full corpus.

Fig 3. Phase-space trajectories (V̄, B̄, λ₂) under the v1.0.1 classifier. Stable Adaptive societies cluster top-right; Systemic Crisis cases cluster bottom-left. Strained societies form a diffuse mid-band.

Fig 4. Regime timeline, selected societies 1800–2026, under the repaired v1.0.1 classifier. Phantom Type II cases (hollow institutions) appear throughout the historical record once the dead code is removed.

Spot checks

Fig 5. Ukraine 2014–2026 detail. Local Node Failure in 2022 is driven by V_min = 0.6 (Shield absorbing full wartime stress while other nodes degrade). The trajectory is coherent with the corpus anchor cases.

Step 5 Testing the measurer, not just the measured — vantage-point bias control

The deepest worry with historical scoring is hindsight. Do recent years look worse simply because their troubles are vivid, while older crises have been smoothed by time? To test this, the years 2000–2010 in America were scored twice: once from today's vantage (a 2026-scorer) and once as if by an analyst working in early 2012.

The sign pattern matches the pre-stated hypothesis exactly: each vantage scores its own recent past more harshly and the distant past more benignly. The blinding was simulated (the scorer knew post-2012 history), which means the measured divergence is a lower bound on the true vantage effect.

Step 6 Predictions on the record — pre-registered 2027–2028

A diagnostic tool earns trust by predicting, not just explaining. Six predictions for 2027 and 2028 were frozen on 12 June 2026, with explicit falsification criteria. No post-hoc adjustment is permitted.

Method: per-node, per-dimension OLS trend over the last five scored years, clamped to [0,10]; F_G and regime computed by v1.0.1. Bands set at ±2.0 V̄ to reflect honest inter-run reliability.

What it all adds up to

The archive is now verified end to end. The mathematics genuinely works under v1.0.1. The diagnostic categories have earned their places against real history. But the week's most important product is humility made precise.

The instrument measures two things at once: the state of a society, and the standpoint of whoever is doing the measuring. That second component is real, runs in a consistent direction, and is now quantified — roughly 0.5–2.0 V̄ of exaggeration in the most recent few years of any series.

Long-run trajectories can be trusted in shape. Dramatic claims about the most recent three years of any country deserve a discount that is now known rather than guessed. The framework no longer merely asserts that it is honest about uncertainty. It has receipts.

Source files: CAMS_Validation_Report.md · JUNO_Formalism_Test_Report_2026-06-12.md · Blind_Scoring_Control_Report_2026-06-12.md · JUNO_Prospective_Predictions_2027_2028.md · CAMS_Testing_Summary_Plain_Language.md · JUNO_v1.0.1_Recalibration_Patch.md — all in HariSeldon/cam5 working directory. Datasets: JUNO_FG_corpus_v101.csv · JUNO_predictions_2027_2028.csv · USA_blind2012_vs_2026_comparison.csv.