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CONTEXT.md
Overview
This repository reflects a body of work situated at the intersection of:
- bibliometrics and research systems
- knowledge infrastructure analysis
- AI-assisted research workflows
- emerging work on AI evaluation, verification, and structured reasoning
The work is not a collection of isolated analyses, but a set of structured methods, frameworks, and tools designed to:
- represent research and knowledge systems
- analyse their structure and dynamics
- evaluate outputs, including AI-generated outputs
Core conceptual model
The work operates across three primary layers:
Methods (conceptual)
- define how systems are represented and analysed
- abstract, transferable, not tied to a specific implementation
Frameworks (applied structures)
- structured applications of methods
- introduce dimensions, workflows, and constraints
- partially operational but not fully executable
Tools (operational)
- implementations supporting specific tasks
- interface-driven or programmatic
- do not expose full internal logic
Additionally, there is a supporting layer:
- Prompt artifacts / protocols
- structured prompts used within tools and workflows
- encode behaviour, evaluation logic, or style
- not exposed as standalone methods
Implemented baseline
The following components are now defined and publicly structured:
Method
- AI metascientist
Framework
- GRID+
Tool
- AVA
These establish a minimal working system across all three layers: method → framework → tool
Key distinction: method vs prompt artifact
This distinction must be preserved:
Methods / frameworks
- describe how work is done
- conceptual or structured processes
- reusable independently of tools
Prompt artifacts (e.g. style.md)
- encode instructions for AI systems
- operational components
- belong to tooling or execution layers
Example:
- writing research publications → method / framework
- style.md → prompt artifact
Structural constraint
Each layer must remain distinct in both content and level of detail:
- methods define conceptual logic
- frameworks define structured application
- tools implement constrained operations
Tool pages must not exceed frameworks in detail.
Frameworks must not expose full operational logic.
Latent / implicit project categories
Based on working patterns, the following are likely to exist as implicit assets:
- prompt protocol library
- evaluation prompts (AI verification / validation)
- classification systems (e.g. funder classification, journal categorisation)
- data construction pipelines
- mobility / researcher flow analysis frameworks
- citation behaviour analysis methods
- metric frameworks (novelty, interdisciplinarity, disruption)
These are candidates for formalisation, but not all necessarily public.
Intellectual property strategy
The work follows a layered disclosure model.
Public
- conceptual methods
- high-level frameworks
- selected examples
- partial workflows
Partial / gated
- structured frameworks with limited detail
- worked examples
- simplified logic
Private
- full prompt systems
- optimisation heuristics
- evaluation logic and scoring
- orchestration / chaining
- internal datasets or mappings
Key principle: Do not expose anything that enables replication of most of the value.
Positioning
The work should be framed as:
- methodological, not product-driven
- structural, not descriptive
- rigorous, not promotional
Avoid:
- startup language
- solution framing
- feature-driven descriptions
Preferred framing:
- methods
- systems
- frameworks
- evaluation
Long-term direction
The work is expected to evolve from:
- bibliometrics and research systems
toward:
- evaluation of knowledge systems
- AI verification and safety
- structured reasoning and validation
This requires maintaining:
- domain flexibility
- conceptual consistency
- non-commercial tone