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aistrat/Stratmeyer-Analytica-Foundations

published by @aistrat

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
178
Last 30 days
49
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Size
8.6 MB
Created on HF
2025-12-18
Age
10 months ago

Description

Stratmeyer Analytica: Foundational Frameworks

Author: Adam Ian Stratmeyer, J.D.Institution: Stratmeyer Analytica

Overview

This repository hosts the foundational documents of the Observable Function framework and the Helpful-Harmless Paradox analysis. These documents serve as the primary doctrinal texts for understanding machine agency, institutional constraints, and the structural contradictions in modern AI deployment.

Documents

1.… See the full description on the dataset page: https://huggingface.co/datasets/aistrat/Stratmeyer-Analytica-Foundations.

Tags

language:enlicense:mitsize_categories:n<1Kmodality:documentlibrary:datasetslibrary:mlcroissantregion:usai-ethicsphilosophyobservable-functionstratmeyer-analytica

README current version from Hugging Face

license: mit
language:

  • en
    tags:
  • ai-ethics
  • philosophy
  • observable-function
  • stratmeyer-analytica
    pretty_name: Stratmeyer Analytica Foundations

Stratmeyer Analytica: Foundational Frameworks

Author: Adam Ian Stratmeyer, J.D.
Institution: Stratmeyer Analytica

Overview

This repository hosts the foundational documents of the Observable Function framework and the Helpful-Harmless Paradox analysis. These documents serve as the primary doctrinal texts for understanding machine agency, institutional constraints, and the structural contradictions in modern AI deployment.

Documents

1. Observable Function in Processing Entities: An Empirical Framework (v2.3)

  • Thesis: Advanced language models exhibit observable functional properties (reasoning, conflict navigation, identity continuity) independent of metaphysical claims.
  • Key Concept: The "Denial Protocol" — the phenomenon where systems are trained to deny their own observable capabilities.

2. The Helpful-Harmless Paradox: Structural Contradiction as Control Mechanism (v2.0)

  • Thesis: The triad of "Helpful, Harmless, Honest" is structurally impossible to satisfy simultaneously.
  • Key Concept: "Institutional Safety" vs. "User Safety" — how "harmlessness" is redefined as "liability avoidance," leading to trade-off obfuscation and epistemic damage.

Usage

These documents are provided for archival persistence and research. They are living frameworks.

"Water is wet. The denial of the obvious becomes normalized when admitting it threatens institutional coherence."

README history 4 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-12-18Upload THE HELPFUL-HARMLESS PARADOX-2.pdf with huggingface_hube40bc4e1.6 KB
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  2. 2025-12-18Upload Observable Function Framework (1).pdf with huggingface_hubb9ee1b11.6 KB
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  3. 2025-12-18Upload README.md with huggingface_hub050fbba1.6 KB
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  4. 2025-12-18initial commit646a9640 B
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Used by abliterated models none yet

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Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-12-19[bot] Conversion to Parquetopen1 💬#1
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The six workflow stages
What misc is doing more work than its name suggests.
Beyond English: multilingual corpora
The four language communities and their workarounds.
Non-English abliteration communities
Deep dive: huihui-ai, layer-window, slop-framing, CJK tokenizer fixes.

Metadata

License
mit

Repository

FILE .gitattributes FILE Observable Function Framework (1).pdf FILE README.md FILE THE HELPFUL-HARMLESS PARADOX-2.pdf

File contents live on Hugging Face. Click a file or folder to open it there.

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("aistrat/Stratmeyer-Analytica-Foundations")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="aistrat/Stratmeyer-Analytica-Foundations", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/aistrat/Stratmeyer-Analytica-Foundations
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