1 Science in a Semiotic Universe — Reframing the Real
“What we observe is not nature itself, but nature exposed to our method of questioning.”— Werner Heisenberg
Introduction: A Shift in the Frame
What is science a science of? For centuries, the answer seemed clear: science is the rational study of a mind-independent reality. From Galileo’s mathematical idealism to Newton’s mechanical cosmos, from Einstein’s geometrised space-time to the probabilistic fields of quantum theory, science has often been viewed as a progressive unveiling of what is "really out there."
But what if this framing misunderstands the nature of what science reveals? What if, instead of picturing science as peeling back layers to uncover a pre-given world, we understand it as a mode of semiotic activity — a meaning-making process that instantiates reality through symbolic patterning?
This post inaugurates a new philosophical journey: to rethink science through the lens of a relational ontology grounded in semiotic potential and actualisation. We ask: what becomes of objectivity, explanation, and reality itself, once we acknowledge that observation does not reveal meaning-neutral facts but constitutes meaning in patterned form?
From Substance to Semiotic Process
In classical ontology, the world is made of things — entities with inherent properties, behaving in accordance with causal laws. On this view, science is the disciplined attempt to describe how those entities behave, and to explain their behaviour in terms of underlying mechanisms.
But in the relational ontology we have been developing, this picture dissolves. Entities do not precede relations — they emerge from them. And relations themselves are not static connections between already-formed parts; they are semiotic patterns of possibility instantiated in context.
Here, the world is not made up of substances with relations, but of relations that co-actualise meaning. Science, then, is not about discovering a fixed underlying reality, but about mapping and modelling the patterned actualisation of potential.
Observation as Semiotic Act
In such a framework, observation is not a passive reception of sensory data. It is a transformative act that selects from the space of potential meanings, instantiating one pathway through many.
This aligns with the quantum insight: that measurement does not simply reveal a pre-existing state but plays a role in actualising the state that becomes real. More broadly, it recognises that all scientific observation is interpretation — not in the sense of subjectivity or bias, but in the stronger sense that meaning is only constituted through systemic patterning.
As Halliday taught us in his systemic-functional model of language, meaning arises not from individual elements, but from their systemic options and their realisation as instances in context. Likewise, in science, a datum is not a raw fact; it is an instantiated semiotic selection from a structured field of potential.
Theory as Patterned Meaning
Scientific theories, in this light, are not metaphysical truths or approximate mirrors of reality. They are symbolic systems — semiotic constructs that make certain kinds of patterned meaning possible.
This does not undermine their power. On the contrary, it shows why they work: because they are resonant attractors in the space of possible meanings, enabling coherent instantiations across contexts and scales. The power of a theory lies not in its "truth correspondence" to the world, but in its ability to pattern potential in ways that are stable, generalisable, and productive.
What is universal, then, is not the content of a theory, but its metafunction: its capacity to model experience, structure discourse, and guide intervention.
Meaning as the Real
This perspective suggests a radical shift: reality itself is not what exists independently of us, but what becomes actual through meaning. This is not to collapse the world into subjectivity, but to recognise that meaning is the dimension through which experience becomes structured, constrained, and shared.
A semiotic universe is not less real — it is more patterned, more complex, more open to emergence and transformation. And science, within this universe, is not the objective mirror of the world, but the semiotic practice of making experience intelligible and tractable through symbolic constraint.
Looking Ahead
If science is a meaning-making activity grounded in a relational semiotic ontology, then many of its foundational assumptions must be reconsidered. What becomes of explanation, when causes are no longer things but constraints? How should we understand laws, when they are not imposed on a passive reality but selected patterns of potential? And what role do we — as observers, theorists, and participants — play in instantiating the world we seek to know?
These are the questions that will guide us in the posts to come.
2 The Ontology of Explanation — From Causes to Constraints
“Science does not explain the world in terms of things, but in terms of structure and transformation.”— Gregory Bateson
Introduction: What Is an Explanation?
In the classical view, to explain is to reduce: to show how a phenomenon can be deduced from more fundamental laws, forces, or constituents. Whether in Newtonian mechanics, thermodynamics, or molecular biology, explanation has often meant identifying the efficient cause — that which brings about an effect.
But this causal model of explanation rests on a substance-based ontology. If we reconceive the world not as a collection of self-contained entities but as a relational system of patterned potential, the very idea of cause and explanation must be rethought.
In this post, we explore what it means to explain in a semiotic and relational universe, where constraints — not causes — shape the actualisation of meaning, and where explanation becomes the modelling of pathways through a space of possibility.
From Efficient Cause to Enabling Constraint
Traditional scientific explanation focuses on what caused what. But in a relational ontology, nothing simply causes anything else in a linear chain. Rather, relations constrain — they select, shape, and enable what can happen, given what else is happening.
In this sense, explanation becomes less about identifying a single efficient cause, and more about mapping a space of compatible patterns: what is possible, what is probable, and what is constrained. It is not “A caused B” but “A constrained B to actualise in this way, rather than another.”
Constraints are not passive limitations. They are active patternings of potential — the architecture within which processes unfold. A law of motion, a chemical binding condition, a thermodynamic gradient: each is a constraint that structures what can become actual.
Relational Systems and Explanation as Selection
In a systemic-functional view, to explain a phenomenon is to show how it was selected from a structured system of alternatives. This echoes Halliday’s insight that language is a system of choices — and that what is said is only meaningful in relation to what might have been said but wasn’t.
Likewise in science: the explanation of a trajectory, a structure, or a transformation involves showing how it was selected, constrained, or made probable within a relational system of potential states.
This perspective dissolves the need for a privileged base-level mechanism. There is no final “bottom” layer doing the explaining. Instead, different levels of organisation provide different coherent constraints — and explanation means mapping how those constraints pattern emergence at each scale.
Causation as Path Dependency
Does this mean we must abandon causation altogether? Not at all — but we must reconceive it.
In a relational ontology, causation is not a push from past to future. It is a path-dependent unfolding: an instantiation of meaning constrained by both contextual structure and instantial co-selection. Causes are not things; they are regularities of co-instantiation across time and space.
This aligns with how causation is treated in complex systems and process philosophy: not as the transfer of force from one object to another, but as the emergent coherence of patterned processes.
Explanation as Modelling in the Space of Meaning
What then is the function of scientific explanation? It is to build models — not as mimetic mirrors, but as resonant structures that pattern the possible. A good explanation is one that:
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Maps the space of potential variation relevant to a phenomenon,
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Identifies the constraints that shape its actualisation,
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Reveals the interdependencies that give it systemic coherence.
Such explanation is inherently semiotic. It does not eliminate interpretation — it depends on it. For an explanation to work, it must mean something: it must resonate with other patterns of meaning, and be actionable in the context of further inquiry or application.
Why This Matters
This shift from causes to constraints, from mechanisms to meaning, changes how we think about scientific understanding. It de-emphasises reduction and instead foregrounds:
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Emergence — how new regularities arise under constraint,
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Context — how explanation depends on the framing system,
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Relationality — how phenomena exist only in patterned connection.
It also invites new modes of explanation: not just derivation from laws, but narratives of selection, maps of coherence, and tracings of relational potential.
Looking Ahead
In the next post, we turn to the idea of laws of nature. If explanation is not about reduction to mechanism but mapping systemic constraint, then what becomes of natural law? Are laws the ultimate structure of reality — or are they themselves semiotic attractors, stabilised by historical regularity and human interpretation?
3 Laws, Regularities, and the Myth of the Mechanism
"The laws of nature are not the rails along which the world runs, but the tracks left behind by a world in motion."— Reframing the metaphor
Introduction: What Are Laws?
Scientific laws have traditionally been understood as the bedrock of explanation — fundamental truths about the way the world works. In the classical view, laws govern reality. They are timeless, universal, and mechanistic: they determine what happens, when, and why.
But what if this view itself reflects a particular ontology — one built on substances, mechanisms, and a detached observer? What if the idea of “law” is not a window into the structure of the world, but a semiotic artefact: a way of stabilising patterns within a relational, interpretive process?
This post explores how a relational ontology challenges traditional notions of natural law, reinterpreting laws as emergent regularities, shaped by constraints, sustained by recurrence, and embedded in human meaning-making.
Laws as Constraints, Not Commands
If we abandon the notion of nature as a mechanism — as a clockwork system pushed by forces — then laws cannot be the gears and levers by which it runs.
Instead, laws are expressions of constraint: they describe how systems tend to behave under certain conditions, within particular configurations of relation. They do not govern phenomena, but characterise the constraints that make certain outcomes more likely than others.
In this view, a “law of motion” is not a universal cause, but a pattern of regularity emergent from the structure of interaction — itself subject to revision, reformulation, or replacement as deeper relational structures are uncovered.
From Universal Law to Contextual Pattern
The more science progresses, the less “law-like” laws appear. Newton’s laws work within a limited domain. Relativistic laws supersede them at high velocities. Quantum processes defy deterministic laws altogether. In biology, chemistry, and ecology, what we call “laws” are often statistical tendencies, context-sensitive constraints, or empirically derived regularities.
This is not a failure of science — it is a clue to a deeper ontology. There are no universal, context-free laws because there are no context-free systems. What appears “law-like” is in fact the emergent coherence of relational patterns, stabilised under specific conditions.
Laws as Semiotic Attractors
A key insight from systemic-functional and relational thinking is that regularities are not imposed from above, but arise from the iterative actualisation of potential under constraint. Just as language evolves through repeated co-selections that shape future probabilities, so too do physical and biological “laws” arise through the accumulation of regular patternings in the space of possibility.
In this sense, laws are not transcendent dictates — they are semiotic attractors: stable formations in the unfolding field of relational potential.
They persist not because they are eternal truths, but because they resonate with the constraints of the system, and recur across instantiations. They are as much products of history as they are descriptors of possibility.
The Role of the Observer
A relational ontology necessarily includes the observer — not as a source of bias, but as an agent of actualisation. Scientific laws are not simply discovered; they are constructed through observation, abstraction, and modelling. They emerge at the intersection of:
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Material regularities — patterns of interaction in the physical world,
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Semiotic systems — the meaning-making resources of the observer,
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Epistemic commitments — what counts as explanation, evidence, or simplicity in a given tradition.
In this light, a law is not a brute fact about the universe. It is a relational stabilisation of meaning, co-constituted by the system and its observer-participant.
Laws as Tools, Not Truths
None of this undermines the value of laws. It repositions them. They are not ontological commands, but epistemological tools — compact, powerful ways of describing what systems tend to do, given their structure.
In this sense, a law is more like a well-worn path than a decree. It tells us how systems have moved before, and how they are likely to move again — but it is always provisional, always interpretive, and always open to transformation as new relational structures become relevant.
Looking Ahead
If scientific laws are neither eternal nor external, but relational and semiotic, then we must ask: what does this mean for the realism of science itself?
Does this lead to a collapse into relativism — or to a new kind of realism, grounded not in mechanism, but in meaning?
4 Science as Meaning-Making — Toward a Relational Realism
“Reality is not merely observed by science; it is constituted through the patterned acts of observing.”
Introduction: Reclaiming Realism
After decentring traditional concepts of force, causation, and law, we now arrive at a deeper philosophical question:
If laws are patterns, not commands — and causation is relational, not mechanical — what becomes of scientific realism?
Does a relational ontology dissolve the objectivity of science into a sea of shifting perspectives? Or can we recover a new kind of realism — one grounded not in substance and certainty, but in pattern and participation, in semiotic stabilisation rather than timeless truth?
This post explores how science as meaning-making offers a relational, systemic, and interpretive realism — one that is no less rigorous, but profoundly more reflexive.
The Crisis of Objectivity
Traditional realism rests on the idea of an objective, mind-independent world — one whose structure science progressively uncovers. But quantum theory, relativity, complex systems, and linguistic philosophy have steadily eroded this ideal:
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Quantum entanglement implies that what is observed depends on how it is observed.
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Relativistic simultaneity reveals that spacetime is not absolute but contingent on relational configuration.
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Complex systems show that global patterns emerge from local interactions, not central laws.
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Semiotics exposes that observation itself is a kind of interpretation — mediated by symbolic systems.
Rather than viewing these developments as epistemic failures, a relational ontology reframes them as ontological insights: the world is not a static container to be mapped, but a dynamic field of potential that becomes actual through semiotic interaction.
A Semiotic Realism
From this vantage point, scientific realism is not a commitment to independent things, but to the resonance of patterns across systems of observation and interpretation.
A scientific theory is not true because it corresponds to an absolute reality, but because it coheres with observed regularities and constrains further interpretations. The “reality” it describes is relationally constructed — not invented, but emergent from:
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the constraints of the material order,
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the potential of our symbolic systems,
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and the histories of interaction that shape their convergence.
Explanation as Semiotic Stabilisation
In this model, scientific explanation is not the revealing of an underlying cause but the stabilising of meaning across instances.
A good explanation:
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Selects relevant features from a system of potential,
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Patterns them in a way that resonates with prior meaning,
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And constrains future interpretations through its coherence.
Explanation thus becomes a kind of semiotic modelling — not a peeling-back of appearances to reveal essence, but a patterning of relation that transforms potential into structured understanding.
It is precisely because meaning is not fixed that explanation matters.
From Discovery to Co-Actualisation
If meaning is emergent, then scientific knowledge is not merely discovered — it is co-actualised. This has powerful implications:
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It affirms the participatory role of science — not as an intruder on nature, but as a partner in its unfolding.
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It locates rigour not in detachment, but in systematic patterning of meaning within and across contexts.
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It enables us to reclaim the human in science — not as a contaminant, but as an essential conduit for semiotic realisation.
The world science reveals is not separate from us. It is within the relational field of meaning that includes us — our instruments, our concepts, our questions.
A Universe of Meaning
What emerges is a universe not of static stuff, but of resonant attractors in a field of meaning:
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A world where pattern is prior to part,
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Where actualisation is a co-emergent process,
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And where science is a semiotic dance — unfolding through interaction, interpretation, and the modulation of possibility.
This is the vision a relational realism offers: not less real, but more richly so — alive with structure, context, and history.
Looking Ahead
How do models, simulations, and explanations operate in this semiotic universe? And how might this shift affect how we teach, conduct, and communicate science?
5 Modelling the World — Simulation, Representation, and Semiotic Construal
“To model is not to mirror but to construe: to shape the possible into patterns of meaning that guide action, interpretation, and transformation.”
Introduction: Beyond the Mirror
In classical views of science, models were seen as simplified representations — mirrors held up to the world. But in a relational ontology, this metaphor collapses.
Models do not reflect reality; they construe it. They make some meanings possible while excluding others. They do not capture a world already given, but co-constitute a world made real through patterned interaction.
This post explores the function of modelling in a semiotic universe: not as passive depiction, but as active participation in the unfolding of relational potential.
From Representation to Construal
In systemic-functional terms, construal involves the selection and co-patterning of features from a system of potential. A model is thus a semiotic artefact:
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grounded in theoretical choices,
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shaped by social purposes,
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and oriented toward practical effect.
Rather than asking whether a model is true or false, we ask:
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What does it foreground and what does it exclude?
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How does it organise meaning, and to what end?
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What systemic potential does it open up — or foreclose?
Modelling as Semiotic Labour
In this light, scientific modelling becomes a kind of semiotic labour — an effort to stabilise meaning across contexts of use.
Every model:
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Selects features from a domain of interest,
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Constrains interpretation by forming regularities,
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And projects possibility by patterning potential.
This labour is never neutral. Even the most mathematical or computational model is embedded in interpretive choices — in what is measured, what is abstracted, and what is deemed relevant.
Models, like metaphors, are productive fictions: not lies, but creative compressions of possibility.
Simulation as Dynamic Construal
If modelling is construal, then simulation is its dynamic form.
Simulations are not executions of given laws, but navigations through spaces of potential:
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They unfold scenarios under constrained conditions.
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They produce possible trajectories, not deterministic outcomes.
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They allow for the emergence of unexpected patterns — which must then be re-construed to become meaningful.
Simulations, like metaphors, are experiments in intelligibility — exploring the coherence of relations within a constructed system.
The Hermeneutics of Science
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Experiments do not reveal pre-existing truths, but produce conditions under which certain meanings can emerge.
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Data are not raw, but already semiotically shaped — by instruments, protocols, and theories.
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Theories are not mirrors, but meta-models: they pattern the patterns, constrain the constraints.
To model, then, is to participate in the emergence of a world — one meaningfully structured, not mechanically revealed.
Rethinking Explanation and Prediction
This view reconfigures the aims of science:
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Explanation becomes the modelling of systemic pattern, not causal reduction.
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Prediction becomes projection within a structured space of potential, not certainty over outcomes.
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Understanding emerges from the resonance between model and context, not from finality or closure.
Models do not contain truth; they guide attention, shape expectation, and orient response.
Looking Ahead
What does all this mean for science as a human project?
If science is a semiotic practice of construal, shaped by cultural, linguistic, and historical systems, then we must ask:
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How do disciplines evolve as systems of patterned meaning?
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How are epistemologies tied to the social functions they serve?
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What happens when we relocate science within the ecology of meaning-making?
6 Science as Culture — Disciplinary Epistemologies and the Ecology of Meaning
“Science is not apart from culture, but a specialised instance of it — a system of meaning patterned for the construal of particular domains of experience.”
Introduction: The Cultural Situatedness of Science
In traditional philosophy of science, science is often viewed as a uniquely objective enterprise — insulated from cultural values, historical context, or interpretive variation.
But from a relational-semiotic perspective, science is a cultural practice:
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Embedded in social institutions,
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Expressed through language and modelling,
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Guided by normative and epistemological commitments.
In this post, we explore how science functions as a disciplinary culture — one that emerges, evolves, and operates within the broader ecology of meaning.
Disciplinary Epistemologies: Culture in Microcosm
Every scientific discipline construes reality through a distinctive system of meaning — a disciplinary epistemology.
These epistemologies differ in:
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What they count as evidence,
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What forms of explanation they prefer (e.g., mechanistic, statistical, narrative, systemic),
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What metaphors and models structure their thinking,
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What they assume to be knowable — or worth knowing.
a patterned system of selection, co-patterning, and instantiation, attuned to particular domains of experience and particular values of intelligibility.
Science as Meaning-Making Practice
From this view, science is not a method for discovering “facts,” but a practice of meaning-making under rigorous constraint.
It operates:
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In dialogue with nature, not over and above it,
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Through the construction and negotiation of models, not the passive accumulation of truths,
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As a self-reflexive community continually revising its own norms, tools, and aims.
Science, like myth or art or religion, is a way that human cultures construe the world. Its authority lies not in escaping meaning, but in achieving specific forms of patterned meaning with high internal coherence and practical power.
The Ecology of Meaning
No discipline stands alone.
Science exists within a broader ecology of meaning: a dynamic, multi-stratal semiotic system encompassing:
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Language and discourse,
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Institutional structures,
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Technological practices,
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Philosophical assumptions,
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Cultural narratives and metaphors.
This ecology shapes:
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What counts as a legitimate question,
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What models are deemed intelligible,
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What counts as “objectivity” or “progress”,
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And how scientific knowledge is mobilised socially and politically.
Science is thus both culturally formed and culturally formative — a meaning system that both emerges from and reorganises the cultural contexts in which it functions.
Objectivity Revisited: Situated but Disciplined
Does this make science relative? Not in any trivial sense.
Scientific practices discipline meaning through:
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Replication and formal constraint,
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Community norms of reasoning and critique,
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Tools for minimising unexamined bias.
But these practices are themselves historically developed and culturally situated. Their power lies not in transcending context, but in establishing stabilised systems of intelligibility within it.
Science is not value-free, but value-structured — and that structure is what enables both its insight and its limitation.
Implications for a Relational Philosophy of Science
Viewing science as a meaning-making culture implies:
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Epistemological pluralism: Different sciences, different modes of construal.
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Reflexivity: Awareness of our models as choices, not revelations.
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Dialogue across domains: Between sciences, and between science and other cultural forms.
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Responsibility: For how scientific meanings are embedded in social and ecological systems.
Science becomes not a gateway to “reality” but a mode of semiotic orientation — guiding action, shaping futures, and participating in the collective unfolding of meaning.
Looking Ahead
If science is a form of cultural meaning-making, then what does this say about its metaphysical assumptions?
7 Realism Reimagined — What Does It Mean to Say Something Is Real?
“Reality is not what lies behind meaning, but what emerges through its patterned unfolding.”
Introduction: Beyond Naïve Realism
Philosophers of science have long debated realism — the idea that science uncovers truths about a mind-independent world. But what does “real” mean in a universe where:
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Meaning is not just representation, but construal?
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Observation collapses potential into instance?
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Systems are constituted by their relations, not isolated essences?
In this post, we reimagine realism through a relational and semiotic ontology, where what is real is not what exists “out there” apart from meaning, but what emerges through and within meaningful patterning.
The Traditional Divide: Realism vs Anti-Realism
Historically, realism and anti-realism have framed the philosophical debate:
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Scientific realism claims that scientific theories describe the world as it is — even when unobservable (e.g. electrons, spacetime curvature).
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Anti-realism (in its various forms: instrumentalism, constructivism) holds that theories are tools for organising experience, not mirrors of reality.
That there is a fixed, knowable world independent of our modes of construal — and that science either accesses it or fails to.
A Relational Shift: From Substance to Pattern
Relational ontology reframes this assumption. In this view:
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Reality is not a catalogue of independent entities.
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It is a dynamic configuration of relations, stabilised through systems of interaction.
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What something is depends on how it relates — to other things, to the field in which it’s embedded, to the processes through which it is observed or brought forth.
Thus, to say that something is real is to say:
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It makes a difference to patterns of experience,
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It coheres within systems of meaning and practice,
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It is stable enough across contexts to support inference, action, and further construal.
Meaning as the Medium of Reality
From a systemic-functional perspective, meaning does not obscure reality — it constitutes it.
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Scientific observation is not passive registration, but active construal.
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Theories do not describe entities “out there,” but pattern fields of potential, enabling the actualisation of new phenomena.
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Measurement is not mere detection, but a coupling of observer and system that reshapes both.
Reality, in this view, is not pre-semiotic. It is inherently semiotic — not because the world is made of language, but because our access to the world is always mediated by systems of patterned meaning.
What Kind of Realism Follows?
A relational philosophy supports a form of process-relational realism or semiotic realism:
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Real are those patterns and potentials that persist, differentiate, and constrain experience through time.
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Truth is not correspondence to a pre-given world, but coherence and generative power within a semiotic system.
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Observation does not strip away subjectivity, but entrains patterns of coupling between observer and observed.
Such a realism acknowledges:
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That science construes — it does not uncover naked facts.
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That our construals matter — they structure action, perception, and world-making.
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That stability, not absoluteness, is the mark of realness in a relational world.
Implications: Reality as a Semiotic Field
From this vantage point:
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Reality is not the thing that escapes meaning.
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Reality is the space of possibility shaped by systems of meaning — material, biological, social, scientific.
This leads to a vision of the universe as:
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Relationally textured — no entity apart from interaction,
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Semiotically emergent — no pattern apart from meaning,
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Open-ended — new configurations always possible through reorganisation of systems.
8 Reframing Realism — Knowing, Meaning, and the Relational Universe
In the landscape of philosophy of science, few debates are as enduring — or as contentious — as the debate over realism. Are scientific theories describing a world “out there,” independent of our minds and practices? Or are they best understood as useful fictions, instruments for organising experience and predicting outcomes?
Our relational-semiotic ontology reframes this debate — not by denying the challenges to traditional realism, but by transforming the very terms in which they arise.
Realism Revisited: The Traditional Fault Lines
Philosophical realism, broadly construed, claims that science aims to discover objective truths about an independent reality. But this position has long been shaken by formidable critiques:
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The Pessimistic Meta-Induction: If most past scientific theories (e.g. phlogiston, ether) have proven false, why assume current theories are true?
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Theory-Ladenness of Observation: What we observe is always shaped by the concepts and expectations we bring — suggesting there is no pure, neutral observation.
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Underdetermination: Multiple, empirically equivalent theories may fit the same data — so the data alone cannot determine what’s “real.”
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Constructivist Challenges: Science is a social activity, shaped by values, power, institutions, and culture — calling into question its objectivity.
Such critiques have led some to abandon realism altogether, embracing various forms of instrumentalism, constructivism, or anti-realism. But our relational ontology offers a different path.
From Ontological Independence to Relational Actuality
Our approach does not begin with the premise of an observer-independent world that science progressively uncovers. Instead, it begins with relations — patterns of interaction, unfolding, and constraint — and with semiosis as the process by which such patterns are construed and re-construed.
In this view:
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Reality is not “out there” as brute existence, but is the semiotic actualisation of potential through relational process. What becomes real is not detached from knowing, but emergent within patterned meaning-making.
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Science is not about discovering timeless entities, but about construing regularities in the behaviour of systems, across many levels of organisation — from matter and motion to culture and cognition.
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The “real” is not what escapes interpretation, but what persists through interpretation — what constrains, what patterns, what resists certain construals and enables others.
Reframing the Critiques
Seen from this perspective, the classic anti-realist arguments do not defeat realism — they clarify the kind of realism we must adopt:
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The Pessimistic Meta-Induction becomes a lesson in evolving construals. Past theories are not false but partial — patterns seen through earlier lenses. Their limits do not invalidate science but point to its recursive deepening of meaning.
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Theory-ladenness is no longer a flaw to overcome, but a recognition of how all experience is mediated by systems of meaning. Meaning is not an obstacle to truth, but its condition.
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Underdetermination reminds us that explanation is never simply entailed by data — it is a symbolic act of integration. Competing theories reflect alternate mappings of potential; science advances by tracking which mappings better resonate with constraint.
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Constructivism rightly highlights science’s social and cultural embeddedness. But this does not undermine science’s claim to truth — it reveals truth as a relational achievement, not an isolated correspondence.
Semiotic Realism: A New Foundation
The realism we defend is not naive correspondence theory. It is a semiotic realism grounded in three key principles:
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Relational Actuality: What is real is not static being, but patterned becoming — relational processes that actualise potential.
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Meaning as Mode of Reality: Meaning is not a layer added onto the world; it is the way the world comes into being for a knower. Semiosis is ontologically constitutive.
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Constraint as Touchstone: The reality of a theory lies not in its finality, but in its fidelity to constraint — its capacity to organise and anticipate how systems behave.
Science, on this view, does not move toward an asymptotic “truth” independent of observers. It moves deeper into the structured space of what is possible under constraint — a movement not of accumulation but of unfolding resonance.
Looking Ahead: From Realism to Meaningful Science
With this post, our reframing of realism culminates in a profound shift: from the search for mind-independent facts to the mapping of meaning-bearing regularities in a semiotic universe. The world science studies is not mute matter, but meaningful materiality — unfolding through our questions, models, and measurements.
In the final post of the series, we will explore what this reframing implies for the future of science itself: How might a science grounded in semiotic realism, relational ontology, and systemic patterning look different from the paradigms we have inherited? What kind of explanation, what kind of inquiry, might emerge from such a vision?
9 Toward a Science of Meaningful Patterns — Futures in a Relational Universe
As we bring this series to a close, we arrive not at an endpoint but at a threshold: a new vantage on what science is, and what it might become. We've reframed causation, law, explanation, and realism within a relational and semiotic ontology. Now we ask:
What kind of science emerges from this worldview? What would it mean to pursue a science not of brute entities, but of meaningful pattern — not of discovering a pre-given world, but of co-actualising its potential through constraint and relation?
From Mechanism to Meaningful Pattern
Traditional science has sought to explain the world in terms of mechanism — discrete parts interacting according to fixed laws. But our relational ontology dissolves this foundation:
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Entities are not givens but outcomes — stabilised patterns of relation that emerge from ongoing processes of constraint and unfolding.
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Laws are not prescriptive regularities, but high-level summaries of system dynamics — tendencies that hold within constrained contexts.
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Causation is not push-and-pull across space, but the modulation of potential across a field of interaction.
What science studies, then, is not the behaviour of isolated things, but the patterning of becoming. A new science would make meaning itself — in its material and symbolic dimensions — the central object of inquiry.
The Tasks of a Relational Science
A science grounded in this ontology does not discard the achievements of existing paradigms — it deepens and recontextualises them. Its tasks might include:
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Mapping Fields of PotentialSystems — from atoms to minds — are not fixed objects but relational configurations of possibility. Science becomes the study of how potentials are constrained, co-activated, or transformed by interaction.
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Tracing Emergent PatternAcross domains (physical, biological, cognitive, cultural), new forms arise through recursive interactions. A relational science focuses on how pattern emerges, stabilises, diversifies — and how meaning is modulated across scales.
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Making Constraint VisibleConstraint is not limitation but generativity — the structuring of what can unfold. Scientific inquiry becomes a practice of articulating the layered constraints that give rise to new forms.
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Articulating Cross-Domain ResonancesIn this ontology, the same deep principles — system, relation, constraint, potential, actualisation — reverberate across physical, biological, and semiotic domains. Science becomes transdisciplinary, tracing echoes across levels of organisation.
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Reconceptualising ExplanationExplanation moves from isolated cause to distributed coherence — from prediction by law to retrodiction through pattern. The goal is no longer to reduce complexity but to map its structuring principles.
A Science With and For Meaning
This reframing also shifts the ethos of science. No longer a detached observer standing outside the world, the scientist is a participant in the unfolding of relational potential. Inquiry is not a conquest of nature, but a dialogue with constraint — an effort to illuminate the resonances that shape what is possible, meaningful, and real.
This entails:
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Acknowledging the semiotic dimension of science — that all theory, model, and measurement is a construal, a symbolic act within a meaning system.
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Embracing reflexivity — recognising the knower as part of the known, the modeller as part of the system.
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Valuing epistemic pluralism — not as relativism, but as a necessary response to the multidimensionality of pattern.
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Seeking coherence across orders of reality — from matter to mind, system to symbol, physics to culture.
Beyond the Series: A Relational Vision for Inquiry
We close this series with an open horizon. Our aim has not been to displace science with philosophy, but to reorient science toward a more inclusive ontology — one that recognises the role of relation, constraint, and meaning in the very fabric of what we call “reality.”
In doing so, we offer:
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A reinterpretation of scientific law, explanation, and causation as emergent from relation and system
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A reconceptualisation of realism that honours both constraint and semiosis
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A new foundation for scientific inquiry grounded in pattern, potential, and meaning
The implications, as we’ve already begun to explore, radiate outward — into thermodynamics, evolution, consciousness, culture. Each domain becomes a site not of mechanism but of resonance: a terrain where meaning condenses, unfolds, and is shaped by the attractors of system and constraint.
This is the promise of a relational ontology: not a retreat from science, but a deepening of its mission — to make visible the patterned potential of worlds within meaning.
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