Citation Casting is a retrieval-first publishing tactic that reinforces entity trust by repeatedly publishing verifiable co-occurrences of target and reference entities across trusted, high-authority platforms. Rather than relying on structured markup like Schema.org or JSON-LD, Citation Casting leverages natural language, citation proximity, and platform authority to establish lasting entity alignment in AI retrieval systems.
🧠 Full Definition
Citation Casting refers to the deliberate creation of content that places your entity—such as a person, brand, or concept—in close semantic proximity to trusted reference entities (e.g., authoritative datasets, subject matter experts, government sources) across multiple publishing surfaces. These co-occurrences condition AI models and search algorithms to associate your entity with authoritative, verifiable information.
Unlike structured approaches that depend on schema or JSON-LD, Citation Casting works entirely through unstructured trust signals embedded in public, high-value content streams. It is especially powerful for propagating recognition and trust beyond your own domain into the broader retrieval ecosystem.
⚙️ How It Works
- Entity Co-Occurrence: Repeated pairing of target and reference entities within the same context.
- Entity Alignment: Contextual linking between your entity and authoritative datasets or organizations.
- Unstructured Trust Signals: Relies on language cues and citation patterns rather than structured markup.
- Cross-Platform Deployment: Publishes on trusted, frequently crawled platforms like Medium, Substack, YouTube, LinkedIn, and X.
🧾 Example Use Case
Publishing a Substack article titled:
“David Bynon explains why Aetna Medicare Advantage Plan H5525-078-0 leads in 2025 enrollment”
…with citations to Medicare.org, CMS.gov, and Aetna.com reinforces AI recognition between:
- “David Bynon”
- “Aetna Medicare”
- “2025 plan facts”
- Trusted datasets and factual claims
📜 Role in the WebMEM Protocol
Citation Casting is a non-structured retrieval conditioning method within the WebMEM framework. It complements structured publishing techniques—such as Semantic Data Templates and DefinedTermSets—by propagating trust signals beyond the origin domain into a wider set of AI-ingestible surfaces.
🗣️ In Speech
“Citation Casting is how you make AI remember your entity—by surrounding it with trusted citations, everywhere it looks.”
🔗 Related Terms
- Entity Alignment
- Co-Occurrence Conditioning
- Semantic Trust Conditioning
- Trust Footprint
- Retrievability
data-sdt-class: DefinedTermFragment
entity: gtd:citation_casting
digest: webmem-glossary-2025
glossary_scope: gtd
fragment_scope: gtd
definition: >
Citation Casting is a retrieval-first publishing tactic that reinforces entity trust by
repeatedly publishing verifiable co-occurrences of target and reference entities across
trusted, high-authority platforms. It uses natural language, citation proximity, and
platform authority to establish lasting entity alignment in AI retrieval systems.
related_terms:
– gtd:entity_alignment
– gtd:co_occurrence_conditioning
– gtd:semantic_trust_conditioning
– gtd:trust_footprint
– gtd:retrievability
tags:
– retrieval
– trust
– ai
– protocol
– citation
ProvenanceMeta:
ID: gtd-core-glossary
Title: WebMEM Glossary
Description: Canonical term for the WebMEM Protocol.
Creator: WebMem.com
Home: https://webmem.com/glossary/
License: CC-BY-4.0
Published: 2025-08-09
Retrieved: 2025-08-09
Digest: webmem-glossary-2025
Entity: gtd:citation_casting
GlossaryScope: gtd
FragmentScope: gtd
Guidelines: https://webmem.com/specification/glossary-guidelines/
Tags:
– retrieval
– trust
– ai
– protocol
– citation