Science as a Meaning System

Safeguarding Legitimacy in High-Variation Environments

Authors: Jordan Vallejo and the Transformation Management Institute™ Research Group

Status: Monograph C2 | December 2025

Abstract

Science is often treated as a method or a knowledge-producing enterprise, but within Meaning System Science it is defined structurally. Science is a meaning system: an environment that stabilizes interpretation across experiments, models, and institutions. Its reliability depends on proportional relationships among truth fidelity, signal alignment, structural coherence, drift, and affective regulation. When these variables lose proportion, scientific fields experience declining reproducibility, rising interpretive divergence, and reduced capacity to relate results across contexts. These patterns reflect structural imbalance rather than cultural or intellectual decline.

Modern conditions amplify this imbalance. Publication velocity exceeds correction capacity. Incentives redirect attention away from verification. Interdisciplinary growth outpaces coherence structures. Synthetic tools introduce interpretive variation at computational speed. Drift increases as inconsistencies accumulate faster than stabilizing variables can compensate. Scientific interpretation becomes less stable not because researchers lose capability, but because the environment imposes disproportionate load.

This monograph examines science as an interpretive architecture that requires explicit governance. It identifies where scientific meaning destabilizes under accelerating variation and outlines the structural responsibilities institutions must maintain to preserve discovery legitimacy. C2 does not prescribe research methods. It defines the governance conditions required for scientific interpretation to remain stable when variation exceeds inherited stabilizing mechanisms.

1. The Scientific Enterprise as an Interpretive System

Scientific activity transforms observations into evidence, evidence into explanation, and explanation into structured models. These transformations require interpretive conditions that allow results to be evaluated and related across environments. This interpretive environment is the scientific meaning system.

Hypotheses position current knowledge relative to unexplained variation. Experiments generate signals that must map to phenomena consistently. Models organize patterns into explanations that support comparison. Peer review evaluates claims against shared standards. Communication channels preserve continuity across generations.

These processes remain reliable only when proportionality among the MSS variables is maintained.

1.1 Interpretation as the Core Function of Science

Interpretation is present in every component of scientific work. Instruments generate signals that must correspond to the phenomena measured. Statistical analyses yield values that require contextual criteria to be evaluated. Theories structure patterns into coherent explanations. Debate identifies conceptual limits and tests interpretive consistency.

These activities rely on proportional relationships among truth fidelity, signal alignment, structural coherence, drift, and affective regulation. When proportionality holds, disagreement remains interpretable and contributes to refinement.

1.2 Science Within the MSS Architecture

Meaning systems generate interpretations, distribute them through structured pathways, and update under changing conditions. Science satisfies these invariants.

Interpretation is continually updated through experiments, analyses, literature synthesis, and synthetic outputs. These interpretations move through communication structures such as methods, review processes, shared terminology, and institutional repositories. Interpretive conditions change as technologies, incentives, and conceptual boundaries shift.

Drift increases when the rate of environmental change exceeds the stabilizing capacity of the scientific system. Under such conditions, interpretive stability decreases independent of researcher capability. Science therefore requires governance to maintain proportionality.

1.3 Why Scientific Governance Is Now Necessary

Earlier scientific environments imposed natural stabilization through slower publication rhythms, limited communication channels, and clearly bounded disciplines. Norms such as replication and commentary provided sufficient stabilizing capacity.

These conditions have changed. Scientific environments now operate under high interpretive load. Publication volume exceeds integration capacity, incentives distort evidential priorities, interdisciplinary expansion outpaces coherence structures, synthetic tools modify interpretation rapidly, resource and reputational pressures increase affective strain, and communication channels evolve at different speeds.

Drift rises when variation exceeds stabilizing capacity. Reproducibility declines, and interpretive divergence increases. Under these conditions, inherited norms cannot maintain proportionality. Scientific meaning requires explicit governance.

1.4 The Role of C2 in the Governance Trilogy

C2 defines governance requirements necessary to maintain interpretive stability in scientific environments. It does not determine scientific content or operational methods.

C1 governs synthetic meaning-systems.
C2 governs scientific meaning-systems.
C3 governs institutional meaning-systems.

Together they define governance across environments where interpretation operates under accelerating variation.

2. The MSS Variables in Scientific Work

Scientific environments express the MSS variables in distinct ways. Stability depends on how each variable behaves under research conditions.

2.1 Truth Fidelity in Science (Tₛ)

Truth fidelity is the degree to which claims correspond to the phenomena described. Tₛ depends on stable definitions, reliable instruments, transparent reporting, and accessible data and methods. These conditions allow claims to be reconstructed and evaluated across contexts. When Tₛ weakens, interpretations become local and cannot be validated reliably.

2.2 Signal Alignment in Science (Pₛ)

Signal alignment ensures that results retain consistent interpretive meaning across laboratories, platforms, and analytic environments. Measurements, classifications, and statistical outputs must reflect stable reference conditions. Methodological consistency, shared analytic conventions, and replication norms strengthen Pₛ. When Pₛ weakens, inconsistencies reflect divergent interpretive conditions rather than differences in underlying phenomena.

2.3 Structural Coherence in Scientific Systems (Cₛ)

Structural coherence refers to the compatibility and continuity of the conceptual and methodological architecture that supports scientific interpretation. High Cₛ allows findings to be compared and related across subfields and time. Low Cₛ reduces comparability. Coherence relies on stable definitions, methodological compatibility, and continuity in conceptual lineage. Institutional communication structures sustain these conditions.

2.4 Drift in Scientific Fields (Dₛ)

Drift is the rate at which inconsistencies accumulate faster than stabilizing mechanisms can resolve them. Dₛ increases under high publication volume, shifting incentives, methodological variation, synthetic reconstruction, and affective overload. When Dₛ exceeds stabilizing capacity, interpretive stability decreases regardless of researcher capability.

2.5 Affective Regulation in Scientific Communities (Aₛ)

Affective regulation supports the capacity to evaluate complex information under uncertainty. High Aₛ enables revision and correction. Low Aₛ restricts interpretive flexibility and reduces corrective capacity. Scarcity, reputational pressure, and cognitive overload reduce Aₛ. These factors operate as structural influences on interpretation.

3. The Dynamics of Breakthroughs

Scientific legitimacy is the stability of interpretation across researchers, institutions, and time. It reflects whether interpretations converge under scrutiny or diverge as inconsistencies accumulate. Legitimacy is formalized through the First Law of Moral Proportion:

Lₛ = (Tₛ × Pₛ × Cₛ) ÷ Dₛ

Legitimacy increases when stabilizing variables remain proportionate and drift remains within manageable thresholds. It decreases when inconsistencies accumulate faster than stabilizing mechanisms can respond.

3.1 The Structure of Scientific Legitimacy

A field maintains legitimacy when interpretations remain stable across environments and can be evaluated through shared reference conditions. Legitimacy declines when definitional stability erodes, methods become incompatible, or results depend heavily on local context. Legitimacy is a structural property of proportionality rather than a reputational status.

3.2 Golden Eras as Proportional Conditions

Periods of rapid scientific progress arise when improvements in instrumentation and definition increase truth fidelity, when methodological consistency strengthens signal alignment, when coherence structures support integration, and when drift remains within manageable thresholds. Under these conditions, interpretive baselines remain compatible across groups, and results can be evaluated and related with minimal inconsistency.

3.3 Why Scientific Fields Stall

Fields stall when variation exceeds integration capacity. Verification receives less attention than novelty. Conceptual baselines diverge. Stalling reflects drift surpassing stabilizing ability rather than any decline in researcher capability.

3.4 Breakthroughs as Reorganizations of Proportion

Breakthroughs occur when proportionality is restored. New instruments increase truth fidelity, updated standards strengthen alignment, integrative theories improve coherence, and institutional adjustments support regulatory capacity. As proportionality returns, drift decreases relative to stabilizing forces.

4. AI as a Drift Accelerator in Science

Artificial intelligence contributes directly to scientific interpretation. It reshapes literature, preprocesses data, identifies statistical relations, and generates structured outputs. These activities modify interpretive conditions. The primary challenge introduced by AI is velocity: synthetic variation enters scientific environments faster than human stabilizing mechanisms can respond.

4.1 Synthetic Interpretation in Scientific Pipelines

AI influences literature review, hypothesis formation, data processing, statistical modeling, result summarization, and manuscript preparation. Each influence alters interpretive context. Summaries shift evidential emphasis. Automated analyses change framing. Concept-generating models introduce new relational structures.

These outputs circulate through publication cycles, collaborative environments, and training programs. Variation that once accumulated gradually now appears continuously. Drift increases because interpretive changes propagate faster than correction rhythms can stabilize them.

4.2 Effects on Variable Proportionality

Synthetic variation modifies each variable.

Truth fidelity becomes harder to maintain when automated reconstruction shifts correspondence conditions or compresses interpretive detail.

Signal alignment decreases when outputs differ across model versions, training regimes, or prompting styles in ways unrelated to underlying phenomena.

Structural coherence weakens when synthetic tools introduce unanchored conceptual relationships that exceed the system’s capacity to maintain compatibility.

Drift increases because inconsistencies accumulate at computational speed and outpace standard correction cycles.

Affective regulation decreases when verification demands intensify and interpretive environments lose predictability.

These effects arise from the pace of interpretive change rather than accuracy alone.

5. Meaning-System Governance for Science

Meaning-System Governance maintains proportionality in environments where interpretation determines coordinated action. In scientific systems, governance sustains stable interpretive conditions by supporting structures through which evidence is produced, evaluated, and related. Governance concerns interpretive architecture rather than content.

5.1 Governance of the Variables

Truth fidelity requires conditions that allow claims to be reconstructed and evaluated. Governance maintains definitional stability, instrument reliability, transparent reporting, and data accessibility.

Signal alignment requires compatibility across laboratories and analytic contexts. Governance maintains methodological standards, verification practices, and consistent reporting so interpretive meaning does not diverge across environments.

Structural coherence requires conceptual and methodological compatibility. Governance maintains definitional clarity, supports integration frameworks, and anchors methodological innovation to established structures.

Drift governance requires monitoring inconsistency accumulation and adjusting correction rhythms to maintain proportionality. Governance identifies when variation exceeds stabilizing capacity and updates evaluative structures accordingly.

Affective regulation requires environments where revision is viable. Governance reduces unnecessary competitive strain and ensures transparency in evaluative processes.

5.2 Proportional Governance

Proportional governance maintains stability among the variables. Institutions observe relationships among truth fidelity, signal alignment, structural coherence, and drift. They identify when variation rises faster than stabilizing mechanisms can respond and adjust structures accordingly.

Increased publication volume may require enhanced integration frameworks. Greater methodological diversity may require revised standards. Synthetic tools may require faster verification cycles. Proportional governance treats these adjustments as structural responsibilities.

5.3 Institutional Responsibilities for Scientific Coherence

Scientific interpretation is distributed across journals, universities, funding agencies, training environments, and collaborations. Governance must operate across these distributed structures.

Institutions coordinate measurement standards, sustain definitional clarity, maintain methodological compatibility, and support communication structures that allow claims to be evaluated across contexts. These responsibilities preserve the conditions that make scientific interpretation stable.

6. A Proportionist Future for Scientific Discovery

As scientific environments increase in complexity, proportional governance becomes essential for maintaining interpretive stability. Synthetic tools introduce rapid variation. Interdisciplinary growth expands boundaries. Communication channels diversify. These changes increase interpretive load.

A proportionist stance defines scientific leadership as stewardship of interpretive conditions. Leaders monitor definitional stability, signal consistency, coherence capacity, drift rates, and regulatory conditions. These indicators determine when structural adjustments are necessary to maintain proportionality.

When proportional governance becomes institutionalized, scientific interpretation remains stable even as variation increases. Results remain evaluable, methods remain comparable, and conceptual frameworks maintain continuity. Science becomes more capable because its interpretive structures can manage higher variation without losing stability.

C2 formalizes this responsibility. It establishes the governance foundation required for scientific institutions operating in high-variation environments and defines the structural criteria necessary for scientific meaning to remain legitimate as complexity accelerates.

C2 defines this responsibility. It completes the governance framework for scientific meaning within the MSS canon and establishes the structural foundation scientific institutions will require in the century ahead.

Citation

Vallejo, J. (2025). Monograph C2: Artificial Intelligence as a Meaning System. TMI Scientific Monograph Series. Transformation Management Institute.