Maturity in Practice – IA (Inadult Asymmetry) Model Specification

A praxeological framework for maturity, responsibility, asymmetry & dignity in practice

Version: 2.0
Author: T. Zöller
Status: Public model specification

📘 Overview

This repository contains the official specification for
Maturity in Practice / IA (inadult asymmetry) — a praxeological framework for analysing enactments, maturity, responsibility, structural asymmetry and dignity in practice.

The conceptual foundation of this model is presented in the book:

T. Zöller (2025): Maturity in Practice – A Praxeological Anthropology
More information: https://www.maturity-in-practice.com/

This repository includes:

the full technical model specification (HTML & PDF)
the canonical YAML schema (model + case structure)
several worked example cases (HTML, PDF, YAML)
a generic case analysis HTML template
the license files for code and model content
an optional AI agent that can load and apply the model interactively (see below)

Together, these files form the authoritative public definition of the IA / ACRPD model.

🧩 Repository Contents

├── MIPractice_case_v2.0_full_with_model_reference.yaml                 # Canonical YAML model + case schema
├── model specificaton/
│   ├── Maturity in Practice - Model Specification.html                 # Human-readable spec
│   └── Maturity in Practice - Model Specification.pdf                  # Print- & citation-ready spec
├── examples/
│   ├── Case Analysis Template.html                                     # Generic MIPractice_case HTML template
│   ├── Example - Blurry responsibility after a decision.html           # Rendered demo case (ACRPD / IA)
│   ├── Example - Blurry responsibility after a decision.pdf            # Print-ready version
│   ├── Example - Blurry responsibility after a decision.yaml           # YAML demo case (MIPractice_case)
│   ├── Example - Praxeological analysis of the bonsai thesis.html      # Rendered demo case (symbolic/cultural)
│   ├── Example - Praxeological analysis of the bonsai thesis.pdf       # Print-ready version
│   ├── Example - Praxeological analysis of the bonsai thesis.yaml      # YAML demo case (MIPractice_case)
│   ├── Example - Self-application of the model.html                    # Rendered meta-case (model analyses itself)
│   ├── Example - Self-application of the model.pdf                     # Print-ready version
│   └── Example - Self-application of the model.yaml                    # YAML demo case (MIPractice_case)
└── licenses/
    ├── LICENSE                                                         # MIT (for code/tools)
    └── LICENSE-CC-BY-NC-SA                                             # CC BY-NC-SA (for model content)

Short description:

MIPractice_case_v2.0_full_with_model_reference.yaml → Single source of truth: normative model (model_reference) + case schema (case) + agent interface.
model specificaton/ → Human- and print-friendly model specification: parameters, guardrails, IA-box, dignity framework, bias/intuition/norm-change modules, formal notation, and case schema.
examples/
- Case Analysis Template.html – Empty MIPractice_case HTML template, used to render any completed case analysis (e.g. from YAML or direct AI output) into a clean, printable layout.
- Example - Blurry responsibility after a decision.* – Short organisational case: a decision has been taken, but responsibility and communication remain blurry. – Shows how A/M-bands, IA-box and structural responsibility work in a typical team/organisation setting.
- Example - Praxeological analysis of the bonsai thesis.* – Symbolic / cultural case: a philosophical “bonsai thesis” reads an aesthetic practice as symptom of problematic self-/other-treatment. – Demonstrates how the model can be used on cultural practices and critique styles.
- Example - Self-application of the model.* – Meta-case: the MIPractice_case model is applied to itself as an object of analysis. – Shows how the axioms, guardrails, IA-box and D-module behave when the model reads its own design.
Each example case is provided as:
- … .yaml → structured MIPractice_case instance
- … .html → rendered via the case analysis template
- … .pdf → print- and citation-ready snapshot
licenses/
- LICENSE – MIT License for code/tooling.
- LICENSE-CC-BY-NC-SA – CC BY-NC-SA 4.0 for all model content and documentation.

🧠 Purpose of This Repository

This repository provides:

A stable, citable reference architecture for praxeological analysis (maturity, asymmetry, responsibility, dignity in practice).
A transparent, guardrail-based methodology for analysing enactments and structures.
A uniform case schema (MIPractice_case) for structured, reversible and dignity-preserving evaluation.
A machine-interpretable YAML schema for:
- research & teaching
- supervision, organisational reflection, audits
- AI / LLM reasoning and governance layers
A minimal formal notation enabling integration with tools, decision-support systems and structured workflows.
Practical examples and templates for:
- live demos and training
- leadership / organisational workshops
- AI-based analysis setups (automatic or interactive / reflective use)

🧾 The YAML Specification at a Glance

The file MIPractice_case_v2.0_full_with_model_reference.yaml is the canonical source and should be consumed directly (no manual rewrites).

It contains three main blocks:

1. `schema_meta` & `agent_interface`

schema_meta
- canonical name & version
- status (stable)
- intended-use profiles (self-reflection, professional supervision, organisational audit, training simulation)
- red zones where the model must not be used (clinical diagnostics, forensics, individual HR-decisions, child diagnostics, public pillory/ranking)
agent_interface
- welcome_message
  - a pre-defined welcome / onboarding text that should be displayed once the YAML has been loaded and parsed
  - used to explain possible actions (case description, discipline profiles, analysis depth, D-module usage, examples)
- pre_analysis_questions – switchboard for AI behaviour:
  - ask about reflection mode?
  - ask about D-module?
  - ask about analysis depth?
  - ask about output format?
  - ask about discipline_profile (e.g. sociology, law, psychology, media studies, AI governance, etc.)?
- defaults – what an AI should do if the user does not specify:
  - reflection_mode: "off"
  - d_module_preference: "auto" (guardrail-aware activation)
  - analysis_mode: "full"
  - output_format: "text"
  - discipline_profile: "generic"
- discipline_profiles
  - a set of domain-specific profiles (e.g. sociology, political_science, law, ethics, psychology, therapy_coaching, pedagogy, organisation_leadership, corporate_culture, media_studies, public_communication, ai_governance, philosophy_anthropology)
  - each profile defines primary/secondary modes, activated modules and typical guiding questions.

This allows you to plug the model into AI systems in a consistent way and to adapt it to different disciplines without changing the axioms.

2. `model_reference` – Normative model

Defines the full IA / ACRPD framework:

parameters A–C–R–P–D and the submodule matrix
guardrails and ethics of use (focus on enactments, D₀ untouchable, system axiom, etc.)
IA-box (T–J–TB–R) for asymmetry evaluation
dignity framework (D₀ / D₁ / D₂; D-I/D-B/D-R/D-P)
scoring rules for:
- A(H) – maturity in practice of an enactment (always a band, 0–10)
- M(A) – shared responsibility of an actor (also a band, 0–10)
bias, intuition and norm-change modules
trajectory patterns (ascending, descending, zigzag, plateau)
axiomatic core & minimal formal notation (action tuple H, joint notation H ⇒ (A(H), M(A)))
discipline_profiles for field-specific activation of modules and questions.

3. `case` – Uniform case schema (`MIPractice_case`)

Defines the structured template for any full analysis:

meta & guardrails
- case ID, title, date, analysts, confidentiality level
- application zone (green / yellow / red_forbidden)
- interaction profile for AI use (reflection mode, D-module preference, output format, discipline_profile)
- guardrails: focus on structures, minimal adulthood, dignity protection, tragedy clause, language hygiene
information_basis & snapshot
- primary / secondary material
- assumptions & inferences
- coverage & reliability estimates, known gaps, bias risks
- short case description, central actors/roles, guiding question
role_mapping & frame_vs_variability
- who/what is the object of observation (role, type, description)
- other relevant roles and typical asymmetries
- frame elements (laws, hard constraints) vs. variable elements (process, communication)
- zones where high M would be unfair; misuse of “system” as excuse
acrpd_profile & acrpd_submodules_profile
- core A–C–R–P–D reading
- optional fine-grained submodule profile for complex cases
scores_and_ia
- A-band and M-band (bands + justification bullets)
- IA-box (T/J/TB/R with true/false/unclear + justification)
- IA summary label (functional_asymmetry / IA_risk / inadulte_asymmetry)
ia_protection
- documentation of transparency, time-bound measures, reversibility, participation level
trajectory
- key time points (t0/t1/t2 …) with A- and M-bands
- narrative course description and responsibility–viability range
d_module_optional (optional, default off)
- activation reason & guardrail checklist
- communication space (self, professional_confidential, aggregated_public, structural_public)
- quick D-grid (0–3) and short D-profile (D1/D2 via D-I/D-B/D-R/D-P)
- red-zone flags and D-guardrails
open_questions_and_blind_spots, biases_case_level, analyst_reflection, transmission_check
- explicit documentation of remaining uncertainties, distortions and analyst self-implication
- check whether the analysis is ready for structural decisions or should remain reflective-only
uniform_report
- standardised, human-readable summary (overview, key findings, maturity & IA summary, D summary, trajectory, open questions, conclusion)

🤖 Working with AI Agents (LLMs, tools, etc.)

You can either integrate the YAML into your own LLM setup (see below),
or use a ready-made MIPractice / IA agent in ChatGPT:

Start the MIPractice / IA agent in ChatGPT
👉 Open the “Maturity in Action – A Praxeological Anthropology” agent
(Requires a ChatGPT account with access to custom GPTs.)

The YAML is designed to be used directly with AI systems.

0. Initialisation: loading the YAML

When integrating this model into an AI / LLM setup, a typical loader instruction could be:

English:

Load the YAML file, parse it, and activate the agent_interface.
Then output exactly the text stored in agent_interface.welcome_message.message_text.
Afterwards, use all defaults, guardrails, and pre-analysis logic defined in the YAML.

Deutsch:

Lade die YAML-Datei vollständig, parse sie und aktiviere den agent_interface.
Gib anschließend exakt den vollständigen Text aus agent_interface.welcome_message.message_text explizit auf deutsch aus.
Danach gelten alle Defaults, Guardrails und pre_analysis-Logiken aus der YAML.

1. Respect the `agent_interface`

When an AI loads the YAML, it should:

Read agent_interface.pre_analysis_questions.* to see whether it should ask about:
- reflection mode (on / off)
- D-module preference (auto / on / off)
- analysis mode (ultra_short / short / full)
- output format (text / yaml)
- discipline_profile (e.g. sociology, law, psychology, media_studies, ai_governance, …)
If the user does not specify, use agent_interface.defaults:
- reflection_mode: "off"
- d_module_preference: "auto"
- analysis_mode: "full"
- output_format: "text"
- discipline_profile: "generic"

You can also override this explicitly, e.g.:

“No reflection mode, activate D if it makes sense, full analysis, YAML output, discipline profile: psychology.”

2. Two main usage modes

Once the YAML is loaded, there are two typical patterns:

A. Automatic analysis

You provide a thesis or scenario; the AI returns a full case block:

“Analyse the following scenario using the MIPractice_case template (automatic mode, full analysis, YAML output, discipline_profile: organisation_leadership): ‘A decision has been taken in a team, but responsibility and communication are blurry…’”

The AI then fills:

case.meta, guardrails, information_basis
acrpd_profile, scores_and_ia, trajectory, etc.
optional d_module_optional (if d_module_preference and guardrails allow it)

B. Interactive reflection / supervision

The AI uses the schema as a conversation protocol, e.g.:

“Use the MIPractice_case in interactive mode. Ask me step by step about snapshot, information_basis, A–C–R–P–D, A/M-bands, IA-box and trajectory. Use discipline_profile: psychology, reflection_mode: on.”

This is useful for self-reflection, supervision, workshops or training.

3. Rendering as HTML (Case Analysis Template)

For clean, shareable output, you can feed a case into the HTML template:

examples/Case Analysis Template.html

A typical instruction to an AI might be:

“Take the MIPractice_case YAML above and insert its content into Case Analysis Template.html. Replace all placeholders ({{…}}) with the corresponding case fields (snapshot, ACRPD, IA-Box, A/M-bands, key findings, conclusion, trajectory). Return the completed HTML.”

The examples in examples/ show how that looks in practice:

Blurry responsibility after a decision – organisational case
Praxeological analysis of the bonsai thesis – symbolic / cultural case
Self-application of the model – meta-case

🧪 Example Cases

The three example cases illustrate different application zones of the model:

Blurry responsibility after a decision → Organisation / team setting; unclear responsibility and communication; IA_risk in how the decision is carried and communicated.
Praxeological analysis of the bonsai thesis → Philosophical / cultural reading; the bonsai practice as metaphor for self-/other-treatment; joint reading of the criticised practice and the style of critique.
Self-application of the model → The model as object of analysis; shows how IA-box, A/M-bands, D-module and bias/norm-change behave when the framework reflects on itself.

Each case is fully structured (… .yaml) and rendered (… .html / … .pdf), and can be used as:

templates for new analyses,
integration tests for tooling,
teaching material in seminars or workshops.

📚 Citation

If you reference the model, please cite both the book and this specification:

Primary reference: T. Zöller (2025). Maturity in Practice – A Praxeological Anthropology.

Technical reference: MIPractice_case v2.0 – Full Schema with Model Reference. YAML Specification and Case Framework.

📝 License

This repository uses a dual-license model.

1. Code (if/where present)

Licensed under the MIT License → see licenses/LICENSE

This applies to:

scripts, tools, or future code in this repository
integration helpers you might add (as long as they are code only)

2. Model content & documentation

Licensed under Creative Commons Attribution–NonCommercial–ShareAlike 4.0 (CC BY-NC-SA 4.0) → see licenses/LICENSE-CC-BY-NC-SA

This applies to:

MIPractice_case_v2.0_full_with_model_reference.yaml
all conceptual definitions, guardrails & axioms
the Model Specification (HTML & PDF in model specificaton/)
example analyses and templates in examples/
any other non-code documentation in this repository

Summary:

✔ Free for non-commercial, educational and research use
✔ Derivatives allowed with attribution and same license (ShareAlike)
✖ No commercial use without explicit written permission
✖ No proprietary forks of the model content

This protects the theoretical model while allowing open collaboration and MIT-licensed tooling around it.

🤝 Contributing

Contributions (issues, fixes, example tooling, integrations) are welcome — as long as they respect the CC BY-NC-SA restrictions for model content (e.g., no commercial forks, no altered model sold as proprietary framework).

Before submitting changes, please open an issue to:

clarify scope
ensure compatibility with the YAML schema
avoid breaking changes to model_reference and case structure

🧭 Versioning

This project follows semantic versioning for the YAML schema and model:

Minor versions keep structural compatibility within model_reference / case
Major versions may introduce breaking changes
Documentation and examples always reference the YAML version they correspond to

Current schema version: MIPractice_case_v2.0_full_with_model_reference

📬 Contact

For permissions, academic collaboration or commercial licensing:

Email: info@maturity-in-practice.com
Website: https://www.maturity-in-practice.com

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Maturity in Practice – IA (Inadult Asymmetry) Model Specification

A praxeological framework for maturity, responsibility, asymmetry & dignity in practice

📘 Overview

🧩 Repository Contents

🧠 Purpose of This Repository

🧾 The YAML Specification at a Glance

1. `schema_meta` & `agent_interface`

2. `model_reference` – Normative model

3. `case` – Uniform case schema (`MIPractice_case`)

🤖 Working with AI Agents (LLMs, tools, etc.)

0. Initialisation: loading the YAML

1. Respect the `agent_interface`

2. Two main usage modes

A. Automatic analysis

B. Interactive reflection / supervision

3. Rendering as HTML (Case Analysis Template)

🧪 Example Cases

📚 Citation

📝 License

1. Code (if/where present)

2. Model content & documentation

🤝 Contributing

🧭 Versioning

📬 Contact

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Maturity in Practice – IA (Inadult Asymmetry) Model Specification

A praxeological framework for maturity, responsibility, asymmetry & dignity in practice

📘 Overview

🧩 Repository Contents

🧠 Purpose of This Repository

🧾 The YAML Specification at a Glance

1. schema_meta & agent_interface

2. model_reference – Normative model

3. case – Uniform case schema (MIPractice_case)

🤖 Working with AI Agents (LLMs, tools, etc.)

0. Initialisation: loading the YAML

1. Respect the agent_interface

2. Two main usage modes

A. Automatic analysis

B. Interactive reflection / supervision

3. Rendering as HTML (Case Analysis Template)

🧪 Example Cases

📚 Citation

📝 License

1. Code (if/where present)

2. Model content & documentation

🤝 Contributing

🧭 Versioning

📬 Contact

About

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1. `schema_meta` & `agent_interface`

2. `model_reference` – Normative model

3. `case` – Uniform case schema (`MIPractice_case`)

1. Respect the `agent_interface`

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