The SIRC™ Classification System
SIRRS: Scientific Information Reliability & Relevance System
SIRRS is the classification framework that defines what types of scientific publications exist, how they map to zones on the SIRC™, and what each zone means for readers, researchers, and institutions.
Why a Taxonomy?
The scientific publishing landscape is not a monolith. It contains thousands of journals with radically different standards, incentive structures, and purposes. Without a shared classification language, it's impossible for stakeholders to communicate clearly about quality.
SIRRS provides that shared language. It maps observable publication characteristics to defined categories, giving everyone — from library acquisitions staff to AI training-set curators — a common framework for discussing what they're working with.
The taxonomy is built on top of SCORR scores, meaning every classification is traceable to specific, auditable evidence at the article level.
Relationship to SCORR
SIRRS classification is derived from SCORR scores. A publication's tier on the reliability axis and its level on the relevance axis are determined by its aggregated SCORR scores — not by editorial judgment alone.
Dynamic, Not Static
Classifications are updated as new article-level data is collected. A journal that improves its editorial standards can move up the chart; one that degrades can move down. The taxonomy reflects current state, not permanent status.
Reliability Classification
Three Reliability Tiers
Each tier reflects a distinct combination of editorial standards, peer-review rigor, and publication practices — observable and measurable through SCORR.
High Reliability
Journals publishing well-designed trials with appropriate claims from qualified authors while providing complete disclosures and document records.
Mixed Reliability
Journals publishing a mix of study designs making a mix of claims from authors who are not always qualified while providing incomplete disclosures and/or document records.
Low Reliability
Journals and outlets publishing many poorly designed studies often making unsupported claims by typically unqualified authors while providing little to no disclosure and/or incomplete document records.
Relevance Classification
Three Relevance Levels
The horizontal axis of the SIRC™ is not just about quality — it's about how much a publication helps its readers. These three levels describe the range of relevance found in scientific publishing.
High Relevance
Publications that consistently deliver original contributions from expert authors, with claims tempered for peers. They are focused on serving their scientific community rather than promoting findings, and they hold themselves accountable to that community.
- Authors generally have clearly related expertise in the subject matter or discipline
- Claims appropriate for an expert audience, findings interpreted in professional context
- Observations introduce new data gathered from direct observations of the natural world
- Little to no promotion beyond the core scientific community
- Accountability to community via publication financial incentives
Mixed Relevance
Publications with variable relevance. Some articles make original, peer-tempered contributions from qualified authors; others offer little that is new or lean toward promotion. Readers should assess individual articles rather than relying on publication-level assumptions.
- Author expertise varies from article to article
- Claims not clearly aimed at a specific expert audience
- A mix of original contributions and incremental or redundant work
- Occasional promotional framing of findings
- Accountability processes present but not systematic
Low Relevance
Publications that add little to the scientific conversation or actively confuse it. Claims undergo little scrutiny, expert contributions are rare, and findings are often promoted beyond the scientific community. Readers must rely entirely on their own background to judge what might matter.
- Author expertise absent or unclear
- Claims presented using unclear or biased language
- Little original contribution to the literature
- Strong promotional focus on findings, products, or reach
- Minimal accountability to the scientific community
Want to Learn More About SIRRS?
Reach out to discuss the taxonomy, explore licensing the classification data, or ask about how SIRRS maps to your specific use case.
Contact Us