314 lines
6.2 KiB
Markdown
314 lines
6.2 KiB
Markdown
# Model Taxonomy
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## 1. Purpose
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This file defines the taxonomy used to organize the CCPE model library.
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The taxonomy is not meant to be rigid.
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It is a working classification system for managing many cognitive models extracted from articles, prompts, agents, and discussions.
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## 2. Primary Taxonomy
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The model library uses the following top-level categories:
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```text
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1. Foundational Models
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2. Intermediate Models
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3. Applied Models
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4. Workflow Models
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5. Implicit Extracted Models
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6. Deprecated / Archived Models
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```
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## 3. Foundational Models
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### 3.1 Definition
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Foundational Models define deep assumptions, primitives, or explanatory structures that support many other models.
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They often operate at the level of:
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```text
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Cognition
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Causality
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Complex systems
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Information compression
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Entropy / anti-entropy
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Agency
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Learning
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Model formation
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```
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### 3.2 Typical Signs
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A model is foundational when:
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```text
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Many other models depend on it.
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It defines basic assumptions.
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It appears repeatedly across articles.
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It is not tied to one narrow application.
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It influences how other models are interpreted.
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```
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### 3.3 Examples
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```text
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TBD
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```
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Possible future candidates:
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```text
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Prediction Error Model
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Algorithmic Compression Model
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Complex Adaptive Systems Assumption
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Causal Intervention Principle
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Anti-Entropy Insight Principle
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```
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## 4. Intermediate Models
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### 4.1 Definition
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Intermediate Models organize a domain, thinking pattern, or reasoning method.
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They are more concrete than foundational models but broader than applied models.
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### 4.2 Typical Signs
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A model is intermediate when:
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```text
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It has a named framework.
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It has a coherent mechanism.
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It can be applied to multiple situations.
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It may produce multiple Skills.
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It can be used by several Agents.
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```
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### 4.3 Initial Examples
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```text
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认知显影 / Cognitive Imaging
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巨人认知 / Giant Cognition
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认知棱镜 / Cognitive Prism
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```
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## 5. Applied Models
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### 5.1 Definition
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Applied Models are designed for a specific task, domain, or practical use case.
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They usually depend on foundational or intermediate models.
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### 5.2 Typical Signs
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A model is applied when:
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```text
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It solves a specific operational problem.
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It has narrow usage boundaries.
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It can directly guide an Agent or Skill.
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It may be derived from a broader model.
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```
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### 5.3 Examples
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```text
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Article Critique Model
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Strategic Risk Review Model
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Concept Boundary Inspection Model
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Argument Repair Model
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```
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## 6. Workflow Models
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### 6.1 Definition
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Workflow Models naturally become repeatable procedures.
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They are often convertible into CCPE-Skills or CCPE-Runtimes.
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### 6.2 Typical Signs
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A model is workflow-oriented when:
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```text
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It has steps or phases.
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It defines a repeatable process.
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It produces a stable output.
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It has trigger conditions.
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It can be validated.
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```
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### 6.3 Examples
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```text
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Cognitive Imaging Five-Step Procedure
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Model Mining Pipeline
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Review Committee Workflow
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Article-to-Model Extraction Process
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```
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## 7. Implicit Extracted Models
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### 7.1 Definition
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Implicit Extracted Models are reconstructed from writing or discussion where the author did not explicitly frame the idea as a model.
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### 7.2 Typical Signs
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A model is implicit when:
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```text
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The same explanatory logic appears repeatedly.
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A stable metaphor carries mechanism.
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A hidden taxonomy appears across arguments.
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A repeated causal pattern is visible.
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The model can be named only after extraction.
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```
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### 7.3 Handling Rules
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Implicit models should normally start as:
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```text
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candidate
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```
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They require user confirmation before becoming active.
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## 8. Deprecated / Archived Models
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### 8.1 Definition
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Deprecated or archived models are preserved for history but are not currently recommended as active components.
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### 8.2 Reasons for Deprecation
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```text
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Superseded by a better model.
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Merged into another model.
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Extracted incorrectly.
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Rejected by user.
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No longer represents current thinking.
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Too vague to use.
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```
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## 9. Secondary Tags
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In addition to primary taxonomy, use secondary tags when helpful.
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### 9.1 Domain Tags
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```text
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cognition
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writing
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argumentation
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strategy
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complex-systems
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agent-design
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knowledge-management
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evaluation
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coding
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organization
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```
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### 9.2 Function Tags
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```text
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diagnostic
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generative
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evaluative
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compressive
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causal
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synthetic
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critical
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archival
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transformative
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```
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### 9.3 Usage Tags
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```text
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agent-ready
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skill-ready
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runtime-ready
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model-card-needed
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needs-review
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```
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## 10. Layer System
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Use the layer system to locate models structurally.
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```text
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L0: Foundational Assumption
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L1: Foundational Model
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L2: Intermediate Model
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L3: Applied Model
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L4: Workflow / Procedure Model
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L5: Output / Evaluation Lens
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```
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## 11. Multi-Layer Models
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Some models may belong to more than one layer.
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Example:
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```text
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Cognitive Imaging
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= L2 Intermediate Model
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+ L4 Workflow / Procedure Model
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```
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This is acceptable.
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Do not force a model into one layer if it genuinely spans levels.
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## 12. Taxonomy Maintenance Rules
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When adding a new model:
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```text
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1. Assign primary taxonomy.
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2. Assign layer.
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3. Add secondary tags if useful.
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4. Record uncertainty.
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5. Update Model Index.
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6. Update dependency map if relationships are known.
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7. Update usage map if used by Agents or Skills.
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```
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## 13. Taxonomy Review Questions
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Ask:
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```text
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Is this model foundational or applied?
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Is it a model or a Skill?
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Is it a model or a metaphor?
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Does it depend on another model?
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Is it actually a sub-model of an existing one?
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Should it be merged?
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Should it remain candidate?
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```
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## 14. Initial Taxonomy Placement
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| Model ID | Model Name | Primary Category | Layer | Notes |
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| ----------------- | ------------------------- | ---------------------- | ------ | ---------------------------- |
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| cognitive-imaging | 认知显影 / Cognitive Imaging | Intermediate; Workflow; Evaluation Lens | L2; L4; L5 | Active Model Card; active Lite prompt |
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| giant-cognition | 巨人认知 / Giant Cognition | Intermediate | L2 | Candidate |
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| cognitive-prism | 认知棱镜 / Cognitive Prism | Intermediate; Applied | L2; L3 | Candidate |
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## 15. Final Rule
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The taxonomy should help navigation, not imprison thinking.
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If a model resists the taxonomy, record the tension instead of forcing a false category.
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