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 with relative weights (higher values mean greater priority, not probability or confidence)

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 four dimensions. Quick orientation before opening the map:

• The concept is in the top left corner
• Below the concept is a line of context and three tags with a quick overview for what to expect from the map
• A single perspective, “Cambridge Dictionary”, follows right below the context
• Guiding Forces, Parameters, and Calibrated Output (containing the definition and a synthesis) build the map.

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.

With that in mind, here is what you can click: the Internal/External Drivers and Target (they highlight 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 four Parameters to calibrate, each with a selected State that can explain how we move between concept and definition. 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 (with weights) expresses the importance with which a specific Guiding Force prioritizes the 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 private mental substance. Intuitive Parsimony makes two of the selected States — Static State and Intellectual 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, the first thing you’ll see is the same Cambridge Dictionary perspective as before, but next to it you’ll also find the Phenomenological perspective as well as the Overview and a Bridge Report — all of which are clickable.

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 gives you a Matrix — a visual map of where the two calibrations overlap and diverge. In this case, they don’t overlap at all. Both are mapping consciousness, but they land on different answers for every single Parameter. This, in turn, ripples through the entire map as the Guiding Forces and the Calibrated Output of the phenomenological perspective have to reflect a different calibration altogether.

The Compare view 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 cannot be modeled with enough accuracy on the original parameter space derived from the Cambridge Dictionary definition of consciousness. As a result, I made two structural tweaks that I will discuss after you have discovered the third map for yourself.

By approaching the mapping process in an iterative way like here, there will come a moment when adding new perspectives will be restricted by the initial parameter space. In the case of trying to fit the thinking from my own text, Emergent Semantics, I hit a serious limitation with the fourth parameter, Scope of Access, as none of the available States would allow for the calibration required by the essay.

The solution, then, is to simply extend the Parameter with an additional State, Substrate-Independent. That extension allows for the conceptual approach of my essay to consciousness to be mapped on the parameter space without affecting the two other perspectives which already have their own calibration on the Scope of Access parameter.

That being said, the parameter space itself can also be meaningfully extended: a new fifth Parameter derived from the essay, Identity Structure, was added to provide an additional calibration dimension. This is where the two previously mapped perspectives, Cambridge Dictionary and Phenomenological, need a slight recalibration. How would they fit on the new Parameter? And how can their Guiding Forces plausibly reflect their new State selection without distorting their agreed-upon definition? At the same time, mapping existing perspectives onto the new Parameter opens an opportunity: each perspective gains more nuance.

This flexibility is inherent to natural language itself as opposed to being a convenient feature of the framework. Specifically, the ADF just provides the structure to turn that flexibility into a design or engineering question. For instance, a different approach to building a map with these three perspectives would be to design the parameter space with all three of them from the ground up, leading to a different kind of map: one with a co-designed parameter space. That kind of map requires more upfront architectural work but can reveal structural relationships that the organic approach might not surface. Both 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 Intellectual Comprehension — but the dictionary treats comprehension as a product (a state you reach), while the computational view treats it as a process (something cognition continuously performs). Same label, different ontology. 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. Then a second perspective entered the same space without breaking it. Then a third perspective pushed the space to grow — adding a state, and then an entirely new axis. 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 measure distance: how far apart are two calibrations of the same concept? 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 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.