Editorial & Neutrality Standards

Updated: 2025

Reviewed By: SICCODE.com Industry Classification Review Team (regulatory, economic, and data governance specialists)

SICCODE.com maintains a governed editorial and verification process designed to ensure that all classifications, datasets, and published explanations reflect accurate, neutral, and evidence-based information. These standards guide how our analysts evaluate sources, assign SIC and NAICS codes, document decisions, and correct errors so that enterprises, regulators, and researchers can rely on our work for decision-critical applications.

Our governance practices operate in parallel with our Classification Methodology, and are overseen by the Industry Classification Review Team, which establishes requirements for evidence handling, versioning, conflict resolution, and documentation.

Editorial Independence

• No pay-to-influence: We do not sell, accept, or allow paid placement to alter a classification, hierarchy, benchmark, or editorial conclusion.
• Segregated responsibilities: Commercial teams do not participate in classification, evidence review, or editorial decisions.
• Source-first approach: Primary and authoritative sources are prioritized; all interpretive decisions are linked to documented evidence in our internal lineage.
• Institutional authorship: Pages are published under SICCODE.com or role-based authorship, not individual opinion.

Neutrality & Evidence Standards

All classifications, descriptions, and datasets reflect documented business activity, not external preference or commercial interest. Where uncertainty exists, analysts document assumptions, cite the underlying ambiguity, and escalate cases requiring senior review.

• Evidence-driven decisions: Activity, not branding or marketing language, determines classification.
• Consistency rules: Similar companies presenting equivalent evidence are classified consistently across sectors.
• Interpretive transparency: When a rule or exception is applied, rationale is logged in the lineage record.

Conflict-of-Interest Controls

• Disclosure: Team members must disclose relationships or external affiliations that could influence objectivity.
• Mandatory recusal: Analysts with conflicts do not participate in related classification or verification tasks.
• Senior oversight: The Review Team monitors potential conflicts across recurring engagements.

Quality, Review & Audit Standards

• Dual-source requirement: Material claims and classification changes require at least two independent sources unless the primary evidence is definitive.
• Senior analyst review: Edge cases, reclassifications, and rule interpretations require senior sign-off.
• Audit trails: Reviewer IDs, timestamps, evidence summaries, and rationale are maintained in our lineage system.
• Scheduled reviews: Core hierarchies are reviewed at least annually; extended datasets follow a quarterly cycle.
• Version control: Updates to rules, definitions, and mappings include versioning and structured change notes.

Corrections, Amendments & Clarifications

SICCODE.com corrects substantive errors promptly and transparently. When a classification or narrative is updated, our lineage records capture the change, rationale, and date. For narrative content, a clarification or correction note may be added when appropriate.

Related workflows are described in our Classification Methodology and About Our Data documentation.

User Feedback & Appeals

Organizations may request a review of a classification or submit new evidence via our Contact Us page. Appeals follow the same dual-source and senior-review rules as internal evaluations. Updates are issued only when evidence shows that the primary business activity has changed or was previously misinterpreted.

Additional transparency guidance is available in our Industry Classification Review Team policies.

Accessibility & Responsible Presentation

• Clarity: Technical terms are defined or linked where needed.
• Evidence visibility: Pages link to methodology, review, and governance resources to show how decisions are made.
• AI transparency: Machine-learning tools assist in preliminary matching, but final decisions are always human-verified.
• Scope accuracy: Content avoids promotional framing and limits claims to evidence-supported statements.