This archive contains the complete deterministic reproducibility package for: Is the S₈ Tension Structural? A Provenance-Based Reanalysis of Cross-Survey Covariance (Paper S). It provides all data, code, correlation-matrix construction logic, residual systematic implementation, and validation procedures required to regenerate every numerical result reported in the paper. The archive implements a provenance-encoded correlation framework across 36 published S₈ measurements spanning CMB, weak lensing, 3×2pt, RSD, cluster, and joint analyses. Residual systematic corrections are applied using literature-supported ranges without parameter fitting. All computations are executed through a deterministic pipeline with golden-output validation (tolerance = 0.0), SHA-256 checksum manifests, and ROOT_HASH chain-of-custody enforcement. All numerical values reported in Paper S correspond exactly to the outputs generated by this archived version 1.0.0 pipeline. This repository contains the full reproducibility package accompanying Paper S. The archive provides a deterministic implementation of the provenance-based correlation framework and residual systematic model used to evaluate cross-survey structural dependencies in published S₈ measurements. Archive contents Data • 36-measurement S₈ dataset• conservative and upper-bound residual-corrected datasets• provenance-encoded 36×36 correlation matrix• synthesis and sensitivity-analysis outputs Code • correlation-matrix construction• residual systematic application• inverse-covariance synthesis• sensitivity sweeps over correlation parameters• consolidated metric export and validation Validation • golden-output files (tolerance = 0.0)• schema and unit validation• SHA-256 checksum manifest• ROOT_HASH integrity file• continuous-integration workflow Documentation • methodology overview• provenance notes• eigenvalue diagnostics• robustness tests• replication protocol and runbook Deterministic regeneration From the archive root: python code/minimal_run.py python code/validate_metrics.py python validation/rebuild_checksums.py Successful execution with no errors confirms full deterministic reproduction of the canonical archived results. Structural context This archive applies the deterministic reproducibility and covariance-governance framework defined in Paper 0 and implements the cross-probe correlation synthesis methodology used in Paper 1 within the late-time structure-growth domain.