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Add researcher reputation anomaly guard#413

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shaiananvari8 wants to merge 1 commit into
SCIBASE-AI:mainfrom
shaiananvari8:researcher-reputation-anomaly-11
Open

Add researcher reputation anomaly guard#413
shaiananvari8 wants to merge 1 commit into
SCIBASE-AI:mainfrom
shaiananvari8:researcher-reputation-anomaly-11

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/claim #11

Summary

  • Adds a self-contained researcher-reputation-anomaly-guard/ module for the User & Project Management bounty.
  • Evaluates public researcher reputation badges before they are shown by detecting suspicious download bursts, fork rings, reciprocal endorsement clusters, reproducibility-score inflation, private-profile exposure, anonymous-review linkage risk, and stale metric windows.
  • Emits deterministic reviewer actions, audit digests, JSON and Markdown review packets, an SVG summary, and a short AVI demo artifact from synthetic data only.

Non-overlap

This is a narrow metric-abuse and badge-publication safety guard. It avoids the existing broad RBAC/workspace modules, profile reputation sync and freshness slice, privacy access review, lifecycle/offboarding, institutional recertification, anonymous review escrow, identity merge/export, data-room consent, archive handoff, access-audit anomaly, role delegation, invitation-domain/MFA, funding attribution, automation credential governance, deletion/erasure, break-glass, visibility-transition, project provisioning, and object permission inheritance slices.

Validation

  • npm run check
  • npm test -> 4 tests passed
  • npm run demo -> generated reports/reputation-anomaly-packet.json, reports/reputation-anomaly-report.md, and reports/summary.svg
  • npm run demo:video -> generated reports/demo.avi (2,765,416 bytes)
  • git diff --check
  • git diff --cached --check
  • Sensitive material scan across researcher-reputation-anomaly-guard -> no matches

Synthetic data only. No credentials, private user or project data, identity-provider calls, production profile analytics, payment data, or external APIs are used by the module.

AI-assisted with OpenAI Codex; reviewed and locally verified before submission.

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