N²LAB is an open research initiative focused on narrative forensics — the study of how stories originate, propagate, diverge, or go unreported across the media landscape. Through a combination of contributor-driven research and analytical modeling, N²LAB works to make editorial patterns visible — not to judge what is true or false, but to surface how coverage behaves around a given story.
This overview explains what N²LAB measures, what its outputs mean, and how they should be interpreted.
N²LAB works with publicly available journalistic outputs from a defined set of editorial sources. Inputs include:
N²LAB does not ingest or rely on non-public data, leaked materials, internal communications, or private datasets.
N²LAB organizes editorial sources into four tiers based on observable structural characteristics:
Tier assignments describe editorial structure, not political orientation or factual reliability. They are reviewed periodically as outlets evolve.
N²LAB evaluates measurable editorial dynamics across several analytical dimensions. These include:
N²LAB scores reflect the presence and intensity of these dynamics. They do not infer motive, intent, or truthfulness.
Each N²LAB analysis produces auditable evidence outputs that allow independent review. These include:
These artifacts are designed so that anyone reviewing an N²LAB output can trace the reasoning back to observable data.
Given identical inputs and the same N²LAB version, N²LAB produces identical outputs. Each analysis includes:
This means N²LAB assessments can be reviewed, challenged, or audited under consistent conditions by editors, researchers, legal teams, or institutional reviewers.
N²LAB identifies editorial behavior. It is an analytical tool, not a verdict.
N²LAB outputs are designed to support:
When referencing N²LAB in published work, users should cite the accompanying evidence artifacts to provide full context.
Download Method Overview (PDF)