Our Approach to Integrity
Infolinx applies a three‑pillar framework to detect, assess, and manage integrity risk in scholarly publishing, combining author‑focused analysis, advanced statistical modeling, and a purpose‑built data foundation.
The 3 Pillars
Pillar 1
Focus on Authors, Not Artifacts
Pillar 2
Multivariate Statistical Modeling
Pillar 3
Fit‑for‑Purpose Database
We evaluate integrity at the author level to uncover patterns that individual articles alone can’t reveal.
We use multivariate, statistically significant predictive models to quantify and forecast integrity risk.
Our proprietary scholarly publishing database is structured specifically for integrity analytics and risk tracking.
How the Pillars Work in Practice
Focus on Authors
Multivariate, Statistically Significant Predictive Modeling
‘Fit‑for‑Purpose’ Scholarly Publishing Database
Aligning the Database to the Time of Publication
Database Enables Tracking of Industry‑Level Integrity Risk Performance
Focus on Authors
Multivariate, Statistically Significant Predictive Modeling
By shifting the focus from individual artifacts to the complex networks of authorship, we identify persistent patterns of manipulation that traditional paper-level checks often miss. Our analysis spans career trajectories, collaboration clusters, and output velocity to build a comprehensive risk profile for the modern research landscape.