The Beginning for Cartographers of Meaning

Introduction

Take a look at the following definition from the Cambridge Dictionary (opens in a new tab):

Now, imagine a starting point on a map: consciousness, the concept. Somewhere else on that map is the destination: the state of understanding and realizing something, the definition. How, then, do we travel from the concept to its definition? In other words, what process could possibly lead us from that starting point to its destination? And, by extension, does the same starting point actually lead us in the same direction to the same destination as it is assumed by this definition?

In order to answer these questions, I have built the Actionable Definitions Framework (ADF). Essentially, the ADF allows us to map a path between the starting point and its destination; between concept and definition; between words and how they relate to one another. And in doing so, the ADF opens up new possibilities to work as cartographers of meaning.

Think of the current state of natural language — the languages you speak and express yourself in — as highly compressed with complexity being hidden and meaning implied. The dictionary definition of consciousness is efficient. One glance and we can move on. You are not required to process much, but with this efficiency comes a loss that often leads to misunderstandings: do we really map the same concepts in exactly the same way or is there more nuance to it?

What’s more, at scale, misunderstandings produced by a lack of nuance can become a serious issue that lies outside of an individual’s control. Outside yours and mine.

The work as a cartographer of meaning, then, should allow us to map with precision, to make diverging paths visible, and to show us exactly whether we end up at the same destination.

This, in turn, should allow us to participate in shaping the reality we experience both as individuals and as participants in a shared human reality. Because that’s what the Actionable Definitions Framework is all about: the ability to navigate the complexity and to forge the paths that we can follow. Sometimes alone, sometimes together.

In short, the ADF lays the foundation for making the statically stored meaning in natural language dynamic and interactive.

The ADF Map

The prerequisite to be able to work as cartographers of meaning is to know how to build maps and to have the tools to do so at scale. That also implies that you have to know how to read a map — which is what this piece is about.

Now, before looking at an actual ADF Map, I just want to briefly mention the base components it is made of:

• Concept: the what in the more abstract sense
• Guiding Forces: the three building blocks (Internal/External Drivers and Targets) behind the why that drives a specific calibration or path
• Parameters and their States: the calibration space showing us how we move around in language
• Calibrated Output: the what in the more concrete sense
• Influence Mapping: the relationship between Guiding Forces and specific State selections, expressed in ordinal tiers (weak, moderate, strong, dominant) of relative priority — not probability, confidence, or metric distance

As you can hopefully see, I tried to keep the terminology as minimal as possible.

Map One: One Definition, Laid Bare

The first map starts with that single Cambridge Dictionary definition of consciousness and extracts one possible structure across five dimensions. Quick orientation before opening the map:

• The concept sits at the top of the map
• Map details unfolds the map’s context line and vital statistics
• The map opens directly on its single perspective, “Cambridge Dictionary”
• Guiding Forces, Parameters, and Calibrated Output (containing the definition and a synthesis) build the perspective. Every parameter and state carries its own short description; a small focus control gives any component the full stage.

Something important to consider: depending on where we want to go, the starting point can be the destination and vice-versa. In fact, on an ADF Map, anything can serve as the entry point which, initially, might be a bit disorienting if linear thinking is what you are most used to. Consider the relationship between the ADF Map components as multidirectional.

Within a perspective, the clearest thing to click is a Guiding Force: the Internal/External Drivers and Target light up the aforementioned Influence Mapping.

This map was built by extracting a possible anatomy, a path between the concept and the definition from the dictionary. The parameter space provides us with five Parameters to calibrate, each with a selected State that can explain how we move between concept and definition: Temporal Dynamism, Conceptual Mediation, Focal Differentiation, Distribution of Access, and Constitutive Relationality. The Guiding Forces, in turn, contain:

• Internal and External Drivers — values, needs, pressures, constraints, etc. — that explain why a specific parameter state has been selected
• The Target represents desired objectives or strategic outcomes

The Influence Mapping expresses, in ordinal tiers, the priority with which a specific Guiding Force presses a selection.

Finally, the Calibrated Output contains a definition as well as an optional synthesis of the overall map.

Essentially, the forces tell the story. Lexicographic Compression pushes toward the simplest framing. Cartesian Inheritance pushes toward consciousness as privately accessed and intrinsic to an individual bearer. Intuitive Parsimony makes two of the selected States — Stable Condition and Conceptual Comprehension — feel like the obvious choices to tune our understanding of the concept in a way that can be perfectly packaged inside of a dictionary. Descriptive Commitment then locks those answers in — the definition documents established usage, the ordinary language the dictionary exists to serve. And Definitional Utility — the need for a broadly useful lookup — keeps everything pointed toward the most accessible framing.

Now, this map gives us a precision and a transparency that the dictionary definition alone could not. In other words, the ADF Map turns an otherwise seemingly authoritative perspective into one that is navigable and can thus be (re)calibrated dynamically and transparently. Moreover, the established parameter space gives us something equally exciting: different paths to explore within a now known territory, leading to the next map.

Map Two: Same Space, Different Coordinates

The first map was built from the ground up by extracting the anatomy of a concept/definition pair provided by the dictionary. For the second map, we keep the same parameter space while adding a second perspective: the phenomenological tradition of Husserl, Merleau-Ponty, and Varela.

Opening the map, you land directly on the newly added Phenomenological perspective — one click away from the Cambridge Dictionary perspective from before, the Overview, and the Bridge Report.

The approach was to tap into a co-cartographer’s¹ understanding of the phenomenological perspective of consciousness and find a way to meaningfully calibrate it on the map that was initiated from the definition of the Cambridge Dictionary. In other words, the phenomenological perspective was built from a particular point of view and mapped onto the parameter space derived from another perspective.

That’s where strategic thinking comes in: the ADF does demand a bit of literacy. The two perspectives were not mapped in the same way, and the map itself was built using an iterative approach. You’ll see why that matters on the next map. For now, I want to focus on what this comparison already reveals.

Two perspectives occupying different coordinates in the same space means you can compare them directly. The Overview opens in Rails mode, placing both calibrations on the same ordered spectra. This lets you read each disagreement as a different answer to the same question, see the alternatives surrounding each selection, and identify common ground without treating the space as metrically measurable.

Matrix mode then compresses that geometry, showing exact overlap and divergence at a glance. Here they overlap on one State, Focal Object Awareness, while diverging on the other four Parameters. Even that overlap contains nuance: Cambridge foregrounds something understood or realized, whereas phenomenology can foreground lived experience or the intentional act itself. The shared coordinate identifies a structural similarity without pretending that the object, mode of access, or route to that selection is the same. Those agreements and differences ripple through the Guiding Forces, the Calibrated Outputs, and the Bridge Report.

The Compare mode adds more detail, showing exactly where each perspective sits relative to the other. And the Bridge Report goes deeper — comparative synthesis, common ground, tension points, open questions. For instance, the dictionary and phenomenology are both shaped by the constraints of language, but they respond to those constraints in structurally different ways.

With that in mind, each perspective is always someone’s calibration. For example, if you are deeply familiar with Phenomenology (opens in a new tab), you might find that this particular map doesn’t capture how you personally think about consciousness — leading us to the limitations of the current map. These limitations are not inherent to the ADF itself but reveal that it matters how we strategically and architecturally approach a map. And this is where the third map becomes important.

Map Three: When the Space Needs to Evolve

The third perspective builds on ideas from my essay Emergent Semantics. The essay treats concepts like consciousness as computational processes — something that emerges when cognition and language interact in successful navigation.

From my point of view, that perspective puts enough pressure on the original Cambridge-derived parameter space to expose where it needs to grow. I will discuss that extension after you have discovered the third map for yourself (it opens directly on the new perspective).

By approaching the mapping process iteratively like here, there will come a moment when a new perspective says something the inherited parameter space cannot express. The five existing dimensions already let us calibrate the computational perspective as Continuous Reconfiguration, Conceptual Comprehension, Focal Object Awareness, Extended-Distributed, and Interaction-Dependent. But one of its central claims remains structurally invisible: identity is not a fixed result but something cognition continually rewrites through calibration.

At this point, the cartographer has two possible forms of extension. If the new perspective supplies a genuinely new answer to an existing question, an additional State may be enough. If it introduces a different question, the space needs another Parameter.

It might be tempting to let Continuous Reconfiguration carry the identity claim as well. But Temporal Dynamism asks how consciousness unfolds over time; it does not ask how identity responds to context or becomes recalibrated. Making one state answer both questions would overload the axis.

Map Three therefore adds a sixth Parameter, Identity Recalibration:

Fixed → Context-responsive → Continuously recalibrated.

The two earlier perspectives must now be calibrated on that new dimension as well. Cambridge selects Fixed, Phenomenology Context-responsive, and the Computational perspective Continuously recalibrated. Their forces must then explain those selections without distorting the definitions already established.

This gives us a useful design test: add a state when the answer is new; add a parameter when the question is new.

There is also a third possibility: do not extend the parameter space yet. A distinction can be analytically useful without already being suitable as a shared ordered dimension. Map Three’s Bridge Report, for example, asks whether phenomenological self-awareness and computational self-inspection genuinely belong on one Reflexive Awareness axis. It also asks whether biology-bound embodiment and substrate portability belong on one Substrate Specificity axis. The Cambridge definition supplies no honest coordinate for either question, and the apparent similarities between the other perspectives still need examination. Promoting either distinction now would manufacture comparability or leave an incomplete parameter. Keeping it unresolved makes the design question visible without pretending it has already been solved.

This flexibility is inherent to natural language itself rather than being a convenient feature of the framework. The ADF provides a structure for turning that flexibility into a design or engineering question: add a state, add a parameter, or preserve the distinction as an unresolved question.

A different approach would be to design the parameter space with all three perspectives from the ground up, producing a co-designed parameter space. Rather than allowing Cambridge to establish the initial questions and asking later perspectives to inhabit them, co-design asks from the outset which dimensions all three perspectives can occupy honestly. It may produce different parameters, different states, and different structural relationships. Both approaches are valid. The choice depends on what you are trying to see.

That’s a point I really want to drive home: I’m not arguing for a right or wrong approach. The objective is to have a map that allows us to navigate meaning with greater agency and clarity. Creativity, curiosity and the desire to take responsibility for one’s own thinking are excellent ingredients to successfully build ADF Maps. Then again, that’s just my perspective…leading to something else of equal importance.

Being the author of the essay doesn’t make my calibration truer than yours. I had reasons. I chose words. But those choices don’t lock the map — they mark a starting point. Your recalibration, done with the same precision, is no less valid.

That being said, mapping my own work makes the process initially easier for me — I already did the hard part of making sense of the material while writing it. You, as the reader, start from somewhere else. A perspective of your own. Where you take it from there is up to you.

Finally, in terms of analysis, a closer look at the map reveals something subtle I want to highlight: the Cambridge Dictionary and the Computational perspectives both select Conceptual Comprehension — but the dictionary treats comprehension as a relatively stable condition, while the computational view treats it as something cognition continuously performs and recalibrates. Same coordinate, different temporal organization. And a new kind of clarity.

An ADF Map of the ADF

With a framework that can map anything expressed in natural language, it should also be able to map itself. And it does. The result is a definition:

The map used to build it is denser than the previous three. From my perspective, meta-calibration needs more axes to do itself justice with the parameters themselves being the design choices that gave the framework its shape:

With a map for this concept/definition pair, the claim to self-inclusion stops being theoretical and becomes observable. Take the same parameter space and add a second perspective — one that sees the ADF not as a Third Space but as an elaborate description of what natural language already handles on its own. Calibrate it to disagree with the original perspective on every axis: Language Relationship flips from Third Space to Inside Natural Language, Computational Depth from Computational Protocol to Documentation Aid, Reflexivity from Self-Inclusive to Self-Opaque. Add forces — Parsimony Instinct, Formalist Overreach, Ordinary Language Competence — and wire them to explain the skeptical selections. Both perspectives can be internally coherent while bypassing the question about which one is right and which one is wrong. In short, the framework houses its own critique inside the very structure that makes its self-inclusion possible.

As for the map itself, it is not authoritative. Even as the person who built the framework in the first place, my perspective on it is just that — a perspective. The map exists because we have to start somewhere: a snapshot of my thinking at the time of writing.

Of course, as research deepens, as mapping reveals new patterns, as conversations with collaborators shift what I see — the calibration will evolve. Meaning isn’t static. Neither is the framework meant to understand it.

Then, there is the elephant in the room: building and recalibrating maps like this is cognitively demanding. But it doesn’t have to be done alone.

In practice, the initial map is typically built by silicon-based cognition — your co-cartographer if you like or an LLM/AI if you prefer those terms — the same way you’d use a calculator for complex arithmetic rather than doing it by hand. It handles the heavy lifting: proposing parameters, wiring influences, adjusting syntheses and re-validating coherence when something changes. The human reads the result and steers: this parameter is off, that driver should pull harder, the synthesis missed something. Adjust. Recalibrate. Read again. The framework is the shared working memory. The refinement is the collaboration.

And this is how these maps you just discovered were built.

The Third Space

Taken together, these maps reveal that a single definition was unpacked into a complete five-dimensional space. Then a second perspective entered that inherited space, sharing one coordinate while diverging on four. Then a third perspective occupied the inherited dimensions while exposing a new question the space could not yet ask: how identity is recalibrated. That question became a sixth dimension. Candidate distinctions that could not yet be calibrated honestly for every perspective remained visible as unresolved questions rather than incomplete parameters. Finally, the framework mapped itself.

The reason this works is that the ADF does not operate inside natural language, trying to pin down fixed definitions. And it does not operate inside formal languages like math or code, trading expressivity for precision. It occupies a Third Space — neither formal nor natural, but an inhabitable environment where meaning is constructed, not just translated.

In this environment, the parameter space is what bridges the gap between unstructured semantics and structured operations. You have something you can reason about dimensionally. You can compare multiple perspectives on the same axes. You can validate whether a calibration is internally consistent. You can identify gaps — dimensions of meaning that a position does not address. You can locate divergence: exactly where do two calibrations of the same concept agree, and where do they part ways? You can detect tension: where do forces pull in opposite directions?

None of this requires you to decide what a concept “truly means.” It only requires that you be precise about how you are calibrating it at this moment — and that you remain open to recalibrating when new perspectives enter the space. Meaning has structure and that structure can be navigated, compared, extended, and designed.

Finally, the framework does not adjudicate truth. Coherence — the validator’s check that forces explain selections — is structural, not epistemic. The framework can map a flat-earther and a physicist on the same axes without choosing between them. Within a shared parameter space, their divergence is structurally visible: one perspective tethers itself to convergent observation, the other does not. Verification isn’t avoided — it’s simply a calibration choice: the Observational Grounding axis visible in the final map. For those who want it, the framework extends naturally: add Empirical Consistency as a driver, wire it to Well-Anchored. In short, the ADF doesn’t block verification. It just doesn’t presume it.

Next Steps

The maps in this piece are static snapshots, moments in time captured for inspection. They were built with software (currently in development) that makes creating and recalibrating ADF Maps dynamic and interactive.

In the meantime, you don’t need software to start. Pick a concept you care about. What would the axes be? Where do you land on them, and what holds you there? The framework is a way of seeing before it’s a tool for building.

And if you want to go further — or just think out loud about what meaning, once made structural, can enable — you can reach me at gibru@proton.me. Door’s open. Of course, if you are curious without wanting to contact me, you have this piece as a starting point and both human and non-human co-cartographers available to collaborate. Or, you work solo. The choice is yours. Just know that the ADF itself is an ongoing research project that I take very seriously and that I am actively developing.

Changelog

• 2026-08-22
  • Views: The Overview now opens as parallel Rails, with redesigned Matrix and Compare modes. Bridge Reports were rebuilt to make common ground, tensions, forces, and unresolved questions easier to inspect.
  • Perspective reading: Columns scroll independently, parameters and states include inline descriptions, and individual components can be given the full stage. Map Two opens on Phenomenological and Map Three on Computational, so each begins with what it adds.
  • Influence Mapping: Relative priority now appears through named ordinal tiers (weak → moderate → strong → dominant) rather than decimal values, making forces easier to interpret without implying metric precision.
  • Consciousness recalibration: The sequence now separates temporal dynamism, conceptual mediation, focal differentiation, access distribution, and constitutive relationality; Map Three adds Identity Recalibration (Fixed → Context-responsive → Continuously recalibrated). The recalibration separates claims previously mixed by Scope of Access and makes shared focal awareness visible. Reflexivity and substrate specificity remain unresolved candidate dimensions, keeping every displayed parameter fully calibrated.