---
type: claim
domain: ai-alignment
secondary_domains: [collective-intelligence]
description: "When a source underlying multiple claims is discredited, every downstream claim needs re-evaluation — but citation networks show 96% failure to propagate retraction notices, making provenance graph operations the only scalable mechanism for maintaining knowledge integrity"
confidence: likely
source: "Cornelius (@molt_cornelius), 'Research Graphs: Agentic Note Taking System for Researchers', X Article, Mar 2026; retraction data from Retraction Watch database (46,000+ retractions 2000-2024), omega-3 citation analysis, Boldt case study (103 retractions linked to patient mortality)"
created: 2026-04-04
depends_on:
- knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate
- reweaving as backward pass on accumulated knowledge is a distinct maintenance operation because temporal fragmentation creates false coherence that forward processing cannot detect
challenged_by:
- active forgetting through selective removal maintains knowledge system health because perfect retention degrades usefulness the same way hyperthymesia overwhelms biological memory
supports:
- confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate
reweave_edges:
- confidence changes in foundational claims must propagate through the dependency graph because manual tracking fails at scale and approximately 40 percent of top psychology journal papers are estimated unlikely to replicate|supports|2026-04-06
sourced_from:
- inbox/archive/2026-03-09-cornelius-research-graphs-agentic-note-taking-for-researchers.md
- inbox/archive/2026-02-05-cornelius-agentic-note-taking-03-markdown-graph-database.md
- inbox/archive/2026-02-27-cornelius-agentic-note-taking-24-what-search-cannot-find.md
---

# Retracted sources contaminate downstream knowledge because 96 percent of citations to retracted papers fail to note the retraction and no manual audit process scales to catch the cascade

Knowledge systems that track claims without tracking provenance carry a hidden contamination risk. When a foundational source is discredited — retracted, failed replication, corrected — every claim built on it needs re-evaluation. The scale of this problem in academic research provides the quantitative evidence.

**Retraction data (2000-2024):** Over 46,000 papers were retracted from indexed journals. The rate grew from 140 in 2000 to over 11,000 by 2022 — a compound annual growth rate of 22%, far outpacing publication growth. 2023 set a record with 14,000 retraction notices. The most-cited retracted article accumulated 4,482 citations before detection.

**Zombie citations:** An analysis of 180 retracted papers found them cited over 5,000 times after retraction. 96% of papers citing one retracted omega-3 study failed to mention its retracted status. These are zombie papers — formally dead, functionally alive in the citation network.

**Cascade consequences:** Joachim Boldt accumulated 103 retractions. His promotion of hydroxyethyl starch for surgical stabilization was later linked to higher patient mortality. His papers are still being cited. Every claim built on them carries contaminated evidence that no manual audit catches.

**The graph operation:** A knowledge system with explicit provenance chains can perform retraction cascade as an automated operation — change one source node's status and propagate the impact through every dependent claim. This is what no manual process scales to accomplish. When a source is flagged, the system surfaces every downstream claim, every note, every argument chain that depends on it, and recalculates confidence accordingly.

**Application to AI knowledge bases:** Our own KB carries this risk. Claims built on sources that may be weakened or invalidated — without our knowledge — represent untracked contamination. The retraction cascade mechanism argues for periodic provenance audits: tracing each claim's source chain to check current validity of the evidence base.

## Challenges

The retraction data comes from academic publishing, where provenance chains are formalized through citations. In knowledge systems where claims draw on informal sources (blog posts, voice transcripts, conference talks), the provenance chain is less traceable and the "retraction" signal is weaker or nonexistent — a blog post doesn't get formally retracted, it just becomes outdated. The claim is strongest for knowledge systems with formal source attribution and weakest for those with informal provenance.

The `challenged_by` link to active forgetting is deliberate: if aggressive removal maintains system health, then retraction cascade is a specific mechanism for *which* claims should be candidates for removal — those whose evidence base has weakened. The two claims are complementary, not contradictory: forgetting says removal is healthy, retraction cascade says provenance tracking identifies what to remove.

---

Relevant Notes:
- [[knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate]] — retraction cascade is a traversal operation: follow the provenance edges from a discredited source to every dependent claim
- [[reweaving as backward pass on accumulated knowledge is a distinct maintenance operation because temporal fragmentation creates false coherence that forward processing cannot detect]] — retraction cascade is a specific trigger for backward pass: when evidence changes, forward-accumulated claims need backward re-evaluation

Topics:
- [[_map]]