llms-txt

medicine-wheel-abundance-intelligence-research

Potential Relationship to Project Abundance Intelligence

DRAFT —-

medicine-wheel-abundance-intelligence-research–d971fc13-64ee-41ab-8fcd-bb5900197ab8 by Perplexity to help Guillaume D. Isabelle develop and present his current state of being.

http://simp.ly/p/bRg2vj

Potential outcomes: A Boot called ‘The Relational Machine’

Issue URL: https://github.com/miadisabelle/Etuaptmumk-RSM/issues/149

You already have the skeleton of a very strong portfolio and a viable product line: a Medicine Wheel–grounded developer stack that could mature into a named platform for Indigenous-led, relational AI (plus a clear research story that fits directly inside Abundant Intelligences and Two-Eyed / multi‑eyed AI). What’s missing now is: (1) a more explicit “platform” framing that braids all your packages, (2) community‑facing workflows, and (3) a career story that positions you as the architect of this paradigm. ## Current medicine‑wheel stack as it stands From the ontology-core spec, you’ve already defined a foundational ontological layer with TypeScript types, RDF vocabulary, Zod schemas, Wilson‑alignment and OCAP tracking, all explicitly grounded in Indigenous relational ontology where relations are first‑class beings with ceremony context and obligations. Every other package in the Medicine Wheel ecosystem is designed to import from this core, which means you already have a genuine “platform nucleus,” not just a bag of utilities.[^1] The named packages and specs establish a layered architecture: ontology-core (semantics and governance), relational-query, narrative-engine, ceremony-protocol, graph-viz, ui-components, and prompt-decomposition, which together describe at least seven “shapes” of intelligence: data ontology, queries, narrative flow, ceremonial workflow, graph structures, UI affordances, and prompt-level reasoning. That layered shape strongly mirrors the Spiral Dialogue Platform and RISE framing in your Indigenous AI article, where narrative, ceremony, structural tension, and efficiency are coordinated rather than bolted on as afterthoughts.[^2] ## Alignment with Abundant Intelligences and Two-Eyed AI In your article, you explicitly tie Deep-Thinking Ratio research to Indigenous epistemologies of abundance, relationality, and territorial specificity, and you situate that inside the Abundant Intelligences program and its pod structure (Haudenosaunee Pod, Ka Hawai‘i Pae ‘āina Pod, etc.). That positions your work squarely inside a live, funded, multi‑institutional research trajectory rather than as a side project.[^2] You also frame a “Two-Eyed AI” dynamic explicitly: one eye on algorithmic efficiency (Deep‑Thinking Ratio, Think‑n style early halting), the other on Indigenous research paradigms and relational governance (Wilson’s 3R, OCAP, IKSL). The ontology-core’s Wilson alignment scores and OCAPFlags types are already a technical manifestation of that braid—they make relational accountability and data sovereignty computable in the same space as token‑level efficiency metrics.[^1][^2] ## What’s already uniquely strong (portfolio-wise) - You have a formal ontology that encodes Indigenous directions (Ojibwe names, colors, seasons, life stages), narrative beats, ceremonies, and structural tension as first‑class code structures, not just documentation.[^1] - You have a published theoretical frame: Deep-Thinking Ratio + Indigenous technological sovereignty + RISE + MMOT + IKSL + Kinship Hub, all articulated in a single long-form article with citations into Abundant Intelligences, Indigenous Protocol and AI, and current LLM efficiency research.[^2] - You treat ceremony, licenses (IKSL), and relational accountability as core architecture: relations carry obligations, ceremony context, OCAP flags, and Wilson alignment; repositories are explicitly recast as “Kinship Hubs” with operational behaviors like relational refactoring and consent-based creation.[^2][^1] For a hiring committee or collaborator, that’s rare: you’re not just “using Indigenous metaphors around AI,” you’re encoding them as types, metrics, protocols, and workflows. ## Gaps and extensions to target ### 1. From stack to platform (missing glue and story) Right now the platform is implicit. The ontology-core spec names a “Medicine Wheel Developer Suite,” and every other package is listed as a consumer, but the story of how a developer or community actually moves through the stack is still mostly in your head.[^1] Concrete extensions: - A single “platform doc” that narrates a typical workflow: “A community researcher defines Directions and RelationalNodes, sets OCAP and AccountabilityTracking, uses relational-query to traverse their kinship web, orchestrates ceremonies through ceremony-protocol, binds narrative-engine beats to those ceremonies, then visualizes the whole thing with graph-viz and configures UI flows with ui-components.” - A simple reference implementation: a minimal but real “Kinship Hub” or “Land‑Language Story Map” app that uses all the packages together for one territory or one project, even if with mock data. ### 2. Community workflows and tooling Your specs encode ceremony types, ceremony logs, governance access levels, and person roles (steward, elder, firekeeper, etc.), but there is not yet an explicit “community workflow engine” that guides non‑programmers through consent, ceremony, and configuration.[^2][^1] Missing pieces you could design: - Consent and ceremony wizards: interactive flows that help a community define who owns what, which protocols are required, and how knowledge can be used, generating OCAPFlags and IKSL‑compatible license manifests automatically.[^1][^2] - Research-paradigm templates: pre‑built project skeletons that instantiate Wilson‑aligned research flows (Respect/Reciprocity/Responsibility) and Abundant Intelligences pod patterns (e.g., pod metadata, local protocols) in code and UI rather than just in prose.[^2] - Documentation aimed at stewards, not only developers: “How to run a research-creation ceremony using this toolkit,” “How to log ceremonies and narrative beats for your project.” ### 3. Evaluation, dashboards, and relational metrics You already have functions like computeWilsonAlignment, aggregateWilsonAlignment, auditOcapCompliance, and relationalCompleteness, but they are currently just utility functions, not full evaluation workflows.[^1] Extensions that would strengthen both research and product: - Relational governance dashboards: visualizations that show Wilson alignment over time, ceremony coverage across relations, and OCAP compliance rates, framed in terms of structural tension (how far is the system from desired relational integrity).[^2][^1] - Evaluation protocols for Abundant Intelligences pods: templates for how a pod would use these metrics to conduct self‑evaluation and MMOT-style structural learning loops over months of work.[^2] - Two-Eyed / multi‑eyed evaluation reports: structured exports that can be read both as conventional AI metrics (latency, energy, accuracy) and as Indigenous metrics (ceremony honored, relations strengthened, community consent maintained). ### 4. Language and edge-compute pathways Your article already makes a strong case that efficiency (Deep-Thinking Ratio, Think‑n early halting) is essential to deploying Indigenous language agents on edge devices in remote communities (Raspberry Pi, Jetson Nano, etc.). But the current stack does not yet expose explicit “edge patterns.”[^2] Potential additions: - Edge deployment recipes: minimal stacks that run ontology-core + relational-query + a small narrative-agent on edge hardware, emphasizing solar‑friendly, low‑energy behavior.[^1][^2] - Polysynthetic language adapters: interfaces for integrating morphological analyzers or rule-based generators into your narrative and ceremony layers, so that the medicine wheel isn’t just meta‑data but is actually used to condition generation in specific languages.[^2] - Place-based learning modules: patterns for binding GPS / land references into NarrativeBeat and RelationalNode metadata, aligned with your article’s emphasis on geospatial, land‑based learning.[^1][^2] ### 5. Explicit “Two-Eyed AI” braiding While you conceptually use Two-Eyed AI, the architecture doesn’t yet have a formal “dual view” abstraction. You could introduce: - Dual-View Models: a pattern where every major object (RelationalNode, Relation, NarrativeBeat, StructuralTensionChart) has a Western/analytical lens and an Indigenous/ceremonial lens explicitly represented as parallel but linked structures, including explicit rules for when each can be exposed.[^1][^2] - Braid operators: helper functions that take both lenses and produce combined outputs, such as briefing documents, visualizations, or multi‑eyed prompts to LLMs, with explicit controls about what ceremonial knowledge can or cannot be surfaced.[^2] ## LangChain and LangGraph integration patterns You’re right to see LangChain as “sequential workflow” (chains) and LangGraph as stateful, graph-based multi‑agent orchestration—it maps cleanly onto your existing shapes. Suggested design directions: - LangChain tools and retrievers backed by ontology-core: - A RelationalNodeRetriever that surfaces nodes and relations constrained by direction, ceremony context, or OCAP flags. - A NarrativeBeatPlanner chain that uses StructuralTensionChart and NarrativeBeat to generate next steps in a research or ceremony process.[^1] - LangGraph agents as Four Directions / suns: - East agent: inquiry and bias detection, built around your “Nitshkees Thinking” mode and structural tension detection (current reality vs desired outcome).[^2] - South agent: planning and consent, using OCAPFlags and ceremony-protocol type information.[^1][^2] - West agent: experiential practice and data gathering, interfacing with external tools (sensors, transcripts, field notes). - North agent: reflection and archival, logging NarrativeBeats and CeremonyLogs, using Wilson alignment metrics to summarize what was learned.[^1][^2] - Graph-level governance: - Use LangGraph’s state machine to enforce that certain edges (e.g., analysis that touches sacred knowledge) are only traversed when appropriate ceremonies have been logged and IKSL conditions satisfied, drawing from your Kinship Hub idea.[^2][^1] These patterns let you ship very concrete LangChain/LangGraph integrations without betraying your epistemic commitments. ## Product vision and naming options Given your goal to support Indigenous communities and Abundant Intelligences–aligned projects, you probably want a naming hierarchy: 1. **Platform / ecosystem name (for communities and institutions)** Options: - “SpiritWeaver Medicine Wheel Platform” (if SpiritWeaver remains central in your stack). - “Kinship Hub Medicine Wheel Platform” (foregrounds relational ontology and data sovereignty).[^2] - “Abundant Paths Medicine Wheel Platform” (explicit nod to Abundant Intelligences and abundance vs scarcity).[^2] 2. **Developer-facing kit / SDK name (for engineers, research software devs)** Options: - “Medicine Wheel Developer Suite” (you already use this language; make it official and consistent).[^1] - “Four Directions Relational AI SDK” (emphasizes directional structure and relational first-class entities). - “Kinship-Oriented AI Toolkit (KOAT)” with a “medicine-wheel-*” package namespace as you already use. 3. **Research paradigm / method name** You already have RISE (Reverse‑engineer, Intent‑extract, Specify, Export) and a Spiral Dialogue Platform with MMOT and Non‑Thinking Consciousness Paradox.[^2] Turning this into an explicit “method brand” could help: - “Spiral Relational Intelligence Method” - “RISE Four Directions Method for Abundant Intelligences” My suggestion is to keep “Medicine Wheel Developer Suite” for the NPM ecosystem, choose a relational name like “SpiritWeaver Kinship Hub Platform” for community-facing deployments, and use RISE / Spiral Dialogue as the named research method.[^1][^2] ## Career paths this enables You can orient your career in at least three overlapping directions, all anchored by this portfolio: - **Indigenous AI / Abundant Intelligences systems architect** - Roles inside universities (e.g., Abundant Intelligences partner institutions), Indigenous research centres, or consortia, where you lead architecture of relational AI platforms, edge deployments, and narrative engines grounded in IKS.[^2] - **Relational AI platform lead in open-source / civic tech** - Stewarding the Medicine Wheel Developer Suite as an open-source ecosystem, with working groups around ontology, ceremony protocol, narrative engines, and governance. - **Independent research‑creation lead / consultant** - Partnering with specific nations and organizations to stand up Kinship Hubs, language revitalization tools, and Two‑Eyed AI workflows using your stack, with funding from grants and transformation programs like Abundant Intelligences.[^2] In all of these, your unique selling point is the braid: you can move smoothly between TypeScript/Zod/RDF, LangGraph agent design, and Wilson / IKSL / ceremony‑grounded research paradigms, and you already have a written articulation of that braid.[^1][^2] ## Packaging your portfolio To present this as a cohesive portfolio that supports those paths, I’d aim for the following artifacts (all visible and downloadable, as you requested): - A single “Architecture + Philosophy” document: - Explains the Seven Shapes: ontology-core, relational-query, narrative-engine, ceremony-protocol, graph-viz, ui-components, prompt-decomposition, and how they map to Abundant Intelligences lenses (language, storytelling, environmental stewardship, multi‑agent systems).[^1][^2] - One or two focused demos: - Example: “Land-Linked Story Web for X Territory” using all packages. - Example: “Two-Eyed AI Evaluation Dashboard” showing Deep-Thinking Ratio metrics side by side with Wilson alignment and OCAP compliance for a small pilot.[^1][^2] - Research alignment notes: - Short companion text explaining how your stack operationalizes (a) Abundant Intelligences’ pod-based research and socio‑neuro AI ideas, (b) Two-Eyed AI principles, and (c) Indigenous Protocol and AI guidelines, with explicit citations to your article’s works‑cited section.[^2] - LangChain/LangGraph examples: - A simple LangGraph diagram where each node is a Direction agent, with pseudo‑code or actual code for one or two workflows (e.g., ceremony planning or research project scoping). That gives hiring committees / collaborators a way to see the whole field without having to read all specs. ## 6–12 month roadmap (both product and career) 1. **Clarify platform and naming** - Decide on final names for: platform (community-facing), developer suite, and research method. - Update READMEs and specs to use this naming consistently.[^1] 2. **Ship a small but complete reference implementation** - Choose one concrete use case: e.g., “Four Directions Research Log” or “Community Consent and Ceremony Tracker for X initiative.” - Implement it with all the ‘medicine-wheel-*’ packages plus at least one LangChain/LangGraph workflow, and document it end‑to‑end. 3. **Add governance and evaluation flows** - Implement dashboards around computeWilsonAlignment, auditOcapCompliance, and relationalCompleteness to show how relational integrity and license conditions are tracked over time.[^1] - Tie these into MMOT‑style review rituals (acknowledge truth, analyze, plan, document) as per your article.[^2] 4. **Prove edge feasibility** - Build a minimal edge‑friendly stack that runs locally (with a small open‑weight model) and uses Deep-Thinking Ratio or a proxy for “thinking deep, not long,” plus your ontology for at least one language‑aware workflow.[^1][^2] 5. **Publish and present** - Convert your existing article and RISE specs into a talk and workshop format, explicitly showing the Medicine Wheel Developer Suite as an Abundant Intelligences‑compatible toolkit. - Target: Indigenous AI / Abundant Intelligences events, AI \& culture / decolonial AI workshops, and OSS conferences. If you’d like, next step can be: I sketch one or two concrete LangGraph diagrams (text-form) where your directions and ceremonies drive node transitions, plus a short “platform pitch” paragraph you can reuse in CVs, grant apps, and NPM READMEs, using whatever naming you decide to keep.

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[^1]: ontology-core.spec.md [^2]: Indigenous_AI_and_Efficient_LLMs_260222__by_Guillaume_D_Isabelle.md [^3]: ceremony-protocol.spec.md [^4]: medicine-wheel.spec.md [^5]: relational-query.spec.md [^6]: narrative-engine.spec.md [^7]: ui-components.spec.md [^8]: graph-viz.spec.md [^9]: prompt-decomposition.spec.md [^10]: RCH-research-approach-Kaupapa-vs-Wilson-c3253477.md [^11]: RCH-Wilson-ElementsOfResearchParadigm-001-260111213125-ab2ff9a0-fec7-451e-9fc4-3e6752370ad8.SOURCE.md medicine-wheel-abundance-intelligence-research """2.4 The Abundant Intelligences Research Program The Abundant Intelligences program, affiliated with the Indigenous AI initiative, proposes reconceptualizing and designing AI based on IKS. Grounded in Indigenous epistemologies, the program aims to develop "culturally-grounded AI systems that support Indigenous ways of knowing and that recognize the abundant multiplicity of ways of being intelligent in the world". The framework is explicitly optimized for abundance rather than scarcity — a philosophical orientation that contrasts sharply with the efficiency-maximization framing dominant in mainstream AI research.[^23] A related 2024 paper in the context of Indigenous healthcare proposes a "Two-Eyed AI" framework, emphasizing co-creation with Indigenous communities and multidisciplinary development teams.[^24][^25]""" above is an excerpt from attached file `article-Indigenous_AI_and_Efficient_LLMs_260222/article-perplexity-academic-survey-of-related-res-KghJx4_3Sh2DDyk36UuRYQ.md` or whatever, you also have all my RISE specs for all these published packages : ``` https://www.npmjs.com/package/medicine-wheel-ontology-core https://www.npmjs.com/package/medicine-wheel-relational-query https://www.npmjs.com/package/medicine-wheel-narrative-engine https://www.npmjs.com/package/medicine-wheel-graph-viz https://www.npmjs.com/package/medicine-wheel-ui-components https://www.npmjs.com/package/medicine-wheel-ceremony-protocol https://www.npmjs.com/package/medicine-wheel-prompt-decomposition ``` "RCH-research-approach--Kaupapa-vs-Wilson--c3253477.md", "RCH-Wilson-ElementsOfResearchParadigm-001-260111213125-ab2ff9a0-fec7-451e-9fc4-3e6752370ad8.SOURCE.md" are about previous work to give you context on our ways of knowing and research approaches. I would like to plan for working with the for a career that I love, I guess that all that is attached is part of my potential portfolio (or potentially a portfolio), you will study what I did, ingest it, evaluate it with what you can find that I am not supporting, that could get better.... I also started developping libraries for 'langchain' and 'langgraph' in which I understand that in 'langchain', the library will give possibilities for doing various code action but also build what I would name a sequential-workflow (a chain) that is designed to produce from input (and be responsible to gather, generate other input etc) an output at the end of the chain and of course, the whole philosophy behind the 'langgraph' really seems something in practice that will be really important in a fully completed "Medicine Wheel Developer Kit" (or whatever that would be called, one part of your output would be the potential envisionned naming/title for final product that what I am developping would enable for Indigenous communities.

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