{
  "schema_version": "monitoring-change-register/1",
  "edition_date": "2026-10-16",
  "publication_versions": [
    {
      "version": "1.0.0",
      "date": "2026-10-16",
      "scope": "Initial edition of this educational publication, its synthetic report contract, guides, cases and downloadable examples.",
      "reason": "Make data quality, descriptive drift, complete-window outcome measurement and evidence-based review decisions inspectable.",
      "validation": "Calculation reconciliation, source review, static content checks and desktop/mobile interactions are recorded with this edition; release status is established by the retained deployment and public-readback receipts.",
      "decision": "Record the initial publication edition only. This entry does not claim an earlier release history or a production model deployment.",
      "evidence": "The methodology, source review, fixture and test records, build manifest and cloud-release receipts associated with this edition are the relevant evidence categories. No fictional workflow item below is deployment evidence."
    }
  ],
  "fictional_workflow_records": [
    {
      "id": "EXAMPLE-QUALITY-01",
      "fictional": true,
      "example_revision": 1,
      "scenario": "Fictional exercise: a candidate monthly batch contains a missing date and a duplicated date.",
      "scope": "The synthetic quality preset and its downstream descriptive report; no live model, portfolio or operational incident.",
      "owner_role": "Illustrative data steward \u2014 a role in the exercise, not an appointed employee.",
      "reason": "Twenty supplied rows do not prove twenty unique expected periods. A feature null, a model-return null and stale delivery metadata add separate findings.",
      "validation": "Reconcile expected, observed and unique counts; inspect missing and duplicate identities; retain null denominators; verify drift and complete-window performance are withheld where required.",
      "decision": "Fictional decision: escalate the incomplete candidate for investigation and withhold the affected metrics. Do not silently delete the duplicate, impute the missing return or label the previous report as newly refreshed.",
      "rollback": "Keep the prior accepted input and report available with their original timestamps. A correction creates a new candidate and evidence record; it does not overwrite the failed input.",
      "evidence": "Synthetic quality input, identity/count differences, explicit withholding reasons and a proposed correction comparison. These are teaching artifacts, not a real incident ticket."
    },
    {
      "id": "EXAMPLE-BASELINE-02",
      "fictional": true,
      "example_revision": 1,
      "scenario": "Fictional exercise: replacing the reference distribution after a drift review.",
      "scope": "The fixed-bin PSI reference sample and baseline version; the model and policy are unchanged.",
      "owner_role": "Illustrative model owner and separate reviewer \u2014 hypothetical responsibilities only.",
      "reason": "The drift fixture produces PSI about 0.202499 against the frozen baseline. Substituting the current sample as its own reference would mechanically erase that comparison.",
      "validation": "Preserve old and proposed baselines, unchanged bin edges and smoothing. Recompute both comparisons and document why a new reference period is appropriate before adoption.",
      "decision": "Fictional decision: defer the replacement until an independent rationale and review evidence exist. A smaller PSI alone is not approval evidence.",
      "rollback": "Retain the original baseline and versioned report so the prior comparison can be reproduced. A prospective replacement must have an effective date and cannot rewrite earlier findings.",
      "evidence": "Both baseline definitions, fixed-bin count tables, unchanged-policy comparison and a proposed effective-date record. No real model baseline was changed."
    },
    {
      "id": "EXAMPLE-POLICY-03",
      "fictional": true,
      "example_revision": 1,
      "scenario": "Fictional exercise: a request to raise the PSI review threshold solely to clear an alert.",
      "scope": "Example policy psi_review; no change to data, model, metric or observed result.",
      "owner_role": "Illustrative policy approver \u2014 a fictional decision role, not a regulator or certification body.",
      "reason": "Changing the review threshold from 0.10 to 0.21, while retaining escalation at 0.25, would alter the treatment of a 0.202499 PSI even though the underlying comparison is unchanged. Setting both thresholds to 0.25 would instead fail input validation.",
      "validation": "Replay the same input under both policies, retain equality conventions and identify every changed status. Require a prospective rationale and evaluation beyond the single case that motivated the request.",
      "decision": "Fictional decision: reject the unsupported threshold relaxation. Retain the original review finding and investigate its cause.",
      "rollback": "Restore the recorded policy version if an unapproved edit is found. Preserve the comparison showing its effect rather than removing the attempted change from the record.",
      "evidence": "Old/new policy values, identical input identity, status comparison and fictional decision rationale. This is a training example, not a claim of actual committee action."
    }
  ]
}