Before each retrain of an elections turnout model, the county verifies integrity of precinct feature tables. What lifecycle practice does this institutionalize?

Verifying integrity of precinct feature tables before each retrain of an elections turnout model institutionalizes pre-retrain integrity verification for sensitive civic model features. Election-related models are high-assurance civic systems: tampered precinct features could skew forecasts, resource plans, or public narratives. Checking hashes, signatures, row counts, and schema pins before every retrain turns integrity from a one-time project task into a lifecycle habit that blocks training on swapped spices. Post-deployment performance monitoring for live turnout predictions watches predictions after the model ships; it does not verify precinct feature tables before retraining. Versioned archival of trained turnout models for audit purposes stores finished models; it is not a pre-retrain gate on feature-table integrity. Scheduled recalibration of precinct demographic weights following recent elections updates model inputs after the fact; it does not integrity-check feature tables before each retrain. Distractors fail by offering post-training or audit activities instead of pre-retrain data integrity. Exam caveat: integrity verification detects unauthorized change relative to a trusted baseline; it does not by itself validate that political science features are conceptually correct. Pair checks with access control and dual approval for feature-store writes. Be careful not to imply the model predicts or alters vote tallies—turnout models still deserve strong integrity because misuse or tampering has civic trust impact. Operational check: store canonical digests for precinct feature extracts; require the training job to verify digests and abort on mismatch; alert election IT and security on failure. Log each pre-retrain check with operator identity. Tabletop a silent feature-swap before a scheduled retrain to confirm the gate fails closed.