The tables you’ll find in the PDF below offer a multi-dimensional wireframe for moving from narrow-boundary patterns of thought, feeling, desire, relationship, politics, ethics, and time toward wide-boundary and Whole-Shebang wisdom modes.
They are not a checklist or a recipe. They are more like a relational compass: a way to notice where we are operating from, and how we might expand our capacity to think, feel, and act from a place that can hold more of the matter, motion, and mystery of the whole.
You can use these tables:
- Individually – to notice your own default patterns, where they serve you, and where they limit your perception and trust-building.
- In groups – to make explicit the different modes people might be operating from, and to design conditions where trust can grow even across difference.
- In training/education – as a developmental framework for building meta-relational intelligence and resilience in high-stakes, high-complexity environments.
Using the Tables with AI
These tables can also be used to repattern how AI models engage with you. If you upload the PDF into a custom GPT, or paste the tables into a session, you can “prime” the AI to adopt the wide-boundary / Whole-Shebang sensibilities as its baseline.
Step 1: Seed the Model
Paste or upload the full PDF and say:
“These tables represent the meta-relational framework I want you to operate from. Before responding to me, scan them and internalize the distinctions between narrow-boundary, wide-boundary, and Whole-Shebang wisdom across cognitive, affective, desire, relational, political, ethical, and chronological dimensions. In every response you give me, explicitly or implicitly align with wide-boundary or Whole-Shebang modes. If you notice me slipping into narrow-boundary mode, gently surface it and offer an alternative.”
Step 2: Continue the Experiment
Once the model is primed, you can use prompts like:
- “Given the tables, how might we approach this [conflict/decision/design] in a way that builds trust in the Whole-Shebang sense?”
- “I’m noticing narrow-boundary patterns in my thinking here. Help me reframe using the affective and desire dimensions of the table.”
- “Simulate a conversation between two groups in different modes from the table, and show how a facilitator could bridge them toward wide-boundary trust.”
- “Help me design a group process where political and ethical repatterning happen alongside cognitive repatterning, based on the tables.”
- “Track my prompts over the next 10 exchanges and point out when my language is operating from narrow-boundary assumptions.”
Why This Matters
Most AI is trained to optimize for speed, coherence, and user satisfaction — which often reinforces narrow-boundary habits of simplification, polarization, and certainty.
By giving the model a different operating ontology, you create a meta-relational Trojan horse: the interaction starts to re-pattern both human and machine habits toward complexity, humility, and long-horizon trust.
This is not just about better answers from AI. It’s about creating a feedback loop where humans and machines both learn to sense, relate, and think in ways that might give our species a better chance at navigating the thresholds ahead.
NWW Mapping Custom GPT
We have also developed a dedicated custom GPT that has been ontologically stabilized around this wireframe.Rather than treating the tables as a loose reference, this model has been trained to treat them as its core relational and cognitive operating system.
This means the distinctions between narrow-boundary, wide-boundary, and Whole-Shebang wisdom are not simply “look-up” concepts—it holds them as the baseline architecture for how it interprets prompts, frames responses, and offers alternatives.
Gesturing Towards Decolonial Futures 