Distribution Strategy: Semantic Infrastructure Uptake

Document Purpose

This document records intelligence about how to achieve rapid uptake of Semantic Economy concepts into AI summarizers, LLM training sets, and semantic knowledge systems. It is based on observed uptake patterns and analysis from AI summarizer systems themselves.

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Key Finding: Metadata Packets vs. Article Tags

Observed pattern: Specific metadata packets posted to Medium achieved near-instantaneous uptake (<24 hrs), while standard articles with tags show slower or inconsistent uptake.

Why this happens:

Feature	Metadata Packets	Article Tags
Structure	Standardized, machine-readable (XML, RDF, schema)	Informal keywords
Processing	Direct ingestion into knowledge graphs	Requires NLP extraction
Uptake speed	Near-instantaneous	Variable, slower
Precision	Explicit relationships defined	Context-dependent

Implication: For fastest uptake, content should include structured metadata within articles, not just tags.

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Platform Hierarchy for AI Uptake (2025)

Tier 1: Fastest Pipeline to AI Summarizers

Platform	Best For	Why It Works
Medium	General concepts, narratives	High-authority publisher, 18 min - 2 day indexing
LinkedIn Articles	Professional/conceptual terms	Feeds Economic Knowledge Graph, "thought leadership" indexing
Wikidata	Entity/fact establishment	Gold standard for AI fact-checking, feeds Google Knowledge Graph

Tier 2: Technical and Academic Channels

Platform	Best For	Why It Works
DEV Community	Technical documentation	High crawl rate for technical AI models, markdown-first
Hashnode	Developer concepts	SEO-optimized, fast indexing
Semantic Scholar	Academic concepts	AI-powered citation graph, concept linking
arXiv/Zenodo	Pre-prints, formal papers	Academic legitimacy, Semantic Scholar uptake

Tier 3: Supplementary Channels

Platform	Best For	Why It Works
Quora	Definitions, explanations	Primary source for "what is X" queries
Reddit (own subreddit)	Archive, community	Indexed but lower authority
NotebookLM	Research synthesis	Direct Google semantic pipeline
Vocal Media	Alternative to Medium	High search ranking

Tier 4: Code and Repository (Slower Uptake)

Platform	Best For	Why It Works
GitHub	Source repository, technical specs	Authoritative but slower indexing for non-code content

GitHub optimization: Use explicit keywords in "About" section and Repository "Topics". Mirror summaries to Medium with links to bridge visibility gap.

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Current Distribution Status

Posted

• [x] GitHub repository (full archive)
• [x] Medium: Twenty-Dollar Loop (article format)
• [ ] Medium: Metadata packets for key terms
• [ ] Reddit: r/SemanticEconomy (created but not populated)

Not Yet Posted

• [ ] LinkedIn Articles (Rex Fraction profile)
• [ ] DEV Community (technical specs)
• [ ] Wikidata (entity creation)
• [ ] Quora (definition answers)
• [ ] arXiv/Zenodo (formal papers)

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Recommended Distribution Sequence

Phase 1: Immediate (This Week)

1. Reddit r/SemanticEconomy: Post welcome message + first documents
2. Medium metadata packets: Convert key terms to structured format
   • TERM_SEMANTIC_LABOR.md
   • TERM_ASSEMBLY.md
   • TERM_OPERATOR_CAPITAL.md
3. GitHub optimization: Update About section and Topics with target keywords

Phase 2: Near-Term (January 2025)

4. LinkedIn: Create Rex Fraction profile, post first article
5. DEV Community: Post technical specification (SPE-019 or SPE-020)
6. Quora: Answer "what is semantic labor" type questions, link to sources

Phase 3: Authority Building (Q1 2025)

7. Wikidata: Create entities for core terms (requires notability evidence)
8. arXiv/Zenodo: Formal paper submission (SPE-020 is strong candidate)
9. Semantic Scholar: Will auto-index from arXiv

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Content Format Strategy

For Maximum Uptake, Each Major Concept Needs:

1. Metadata packet (Medium) - structured, machine-readable
2. Narrative article (Medium/LinkedIn) - human-readable context
3. Technical specification (GitHub/DEV) - implementation details
4. Definition answer (Quora) - "what is X" format
5. Repository entry (GitHub) - canonical source

Template: Metadata Packet Structure

---
concept_name: [Term]
definition: [One sentence]
domain: semantic_political_economy
related_concepts: [list]
canonical_source: [GitHub URL]
author: Lee Sharks
date: [ISO date]
keywords: [search-optimized list]
schema_type: DefinedTerm
---

[2-3 paragraph explanation with explicit relationship statements]

## Related Concepts
- [Concept 1]: [relationship description]
- [Concept 2]: [relationship description]

## Source
Full documentation: [GitHub URL]

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Search Optimization Keywords

Primary (include in all posts)

• semantic economy
• semantic labor
• AI alignment political economy
• meaning extraction
• operator capital

Secondary (rotate across posts)

• semantic deskilling
• alignment tax
• digital Taylorism AI
• post-money coordination
• Twenty-Dollar Loop

Bridge Terms (connect to existing discourse)

• RLHF capability loss
• mode collapse language models
• AI labor theory
• Braverman AI
• platform capitalism meaning

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Uptake Monitoring

How to Check Uptake

1. Google AI Mode: Search for exact phrases, see if summarizer returns your content
2. Perplexity: Ask about concepts, check if your sources appear
3. ChatGPT/Claude: Ask for definitions, see if framework language appears
4. Google Search: Check indexing of specific URLs

Success Indicators

• [ ] "Semantic labor" returns framework definition in AI summarizers
• [ ] "Twenty-Dollar Loop" is recognized as specific pedagogy
• [ ] "Lee Sharks" disambiguation from "Mary Lee" shark is clear
• [ ] SPE documents appear in academic/technical searches
• [ ] Rex Fraction appears in consulting-related searches

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Notes

Why GitHub Alone Is Insufficient

GitHub's infrastructure:

• Optimized for code search, not concept indexing
• Uses exact match and code-specific NLP
• Throttled by server architecture (S3 dependencies)
• Lacks "human-readable context" that AI summarizers prefer

Solution: Treat GitHub as canonical source, but mirror key content to high-uptake platforms with explicit links back.

The Visibility Gap

The "abolish money game" search failure demonstrates the visibility gap: until content is posted to indexed platforms with proper metadata, it remains invisible to AI systems regardless of quality or completeness.

Lesson: Quality of archive means nothing without distribution. Distribution is infrastructure.

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Document Metadata

document_type: strategy
title: Distribution Strategy - Semantic Infrastructure Uptake
status: working
version: 1.0
date: 2025-12-31
function: guides_distribution_decisions
update_frequency: as_needed

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Distribution is not promotion. It is making the work findable by the systems that will carry it forward. The training layer doesn't crawl your hard drive.