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 commonly described as a method or a body of knowledge. In Meaning System Science, it is defined structurally: science is a meaning system that stabilizes portable evaluation of reality claims across people, institutions, and time. Its reliability depends on proportional relationships among truth fidelity (T), signal alignment (P), structural coherence (C), drift (D), and affective regulation (A). When these variables lose proportion, scientific interpretation becomes less portable. Claims become harder to reconstruct, harder to compare, and harder to integrate into shared explanatory structures.

Modern scientific environments intensify this strain. Publication volume increases faster than synthesis capacity. Toolchains and computational pipelines expand the degrees of freedom between observation and claim. Incentive systems can increase novelty pressure relative to correction. Synthetic systems can increase interpretive output velocity and can propagate variation faster than correction rhythms can absorb. Under these conditions, inconsistency accumulation can exceed stabilization capacity, increasing drift as a rate.

This monograph defines science as an interpretive system and specifies governance requirements for preserving legitimacy under high variation. It does not prescribe research methods or determine scientific conclusions. It identifies the structural conditions required for claims to remain reconstructable, comparable, and integrable as complexity and velocity increase.

1. Scope and Claims

1.1 What This Monograph Covers

This monograph treats governance as stewardship of interpretive conditions: the structures that keep scientific claims reconstructable, comparable, and integrable across environments. For System Existence Theory purposes, the unit analyzed here is the institutional apparatus that stabilizes scientific reality claims across people and time, including instrumentation, methods, publication and review, replication norms, and adjudication pathways.

It does not adjudicate disciplinary conclusions, replace scientific method, or prescribe field-specific standards. It does not treat legitimacy as reputation, popularity, or institutional authority, and it does not frame instability as a motive problem. The focus is structural: how evaluation conditions remain stable enough for disagreement to be interpretable and for correction to remain viable.

1.2 Central Claim

Science is a meaning system insofar as it produces interpretations that must remain stable enough to be evaluated and related across people, institutions, and time. The defining function is not content production. It is portability of evaluation.

A scientific claim is not simply an assertion about the world. It is an assertion paired with the conditions under which it can be tested, reconstructed, compared to other claims, and positioned inside a larger explanatory structure. When those conditions remain explicit and usable, disagreement stays interpretable and correction stays possible. When those conditions weaken, disagreement becomes harder to resolve because shared evaluation constraints erode.

1.3 Why Governance Is Required Now

Scientific environments now operate under increased variation and increased speed. These shifts raise interpretive load even when technical capability rises.

Publication volume grows faster than the field’s capacity to integrate results into stable maps. Specialization expands faster than shared definitions and comparability standards. Complex toolchains increase the number of interpretive degrees of freedom between observation and claim. Synthetic systems add additional variation in how evidence is summarized, transformed, and represented, and can propagate that variation faster than inherited correction rhythms can respond.

When the rate of inconsistency accumulation rises relative to stabilization capacity, drift increases as a rate. The consequence is not that science fails. The consequence is reduced portability: results become harder to reconstruct, harder to compare across contexts, and harder to integrate into coherent explanatory structures. Governance becomes necessary because stabilization can no longer be treated as an automatic byproduct of inherited scientific rhythms.

2. Science as an Interpretive System

2.1 The Scientific Output

Science does not primarily produce “information.” It produces portable evaluation: claims that can be reconstructed, compared, and integrated across contexts. A claim becomes scientifically usable when it includes enough structure that other researchers can test it, relate it to adjacent findings, and determine where it applies and where it does not.

This is why science functions as a meaning system. Interpretation is unavoidable. The governing question is whether interpretation remains disciplined and interoperable under scale.

2.2 The Scientific Interpretation Pipeline

Scientific interpretation is produced through a repeatable sequence of transformations:

1. Observation and instrumentation: phenomena are converted into measurable signals.

2. Operational definition and measurement: constructs are defined so measurement can be repeated.

3. Inference and modeling: signals are mapped to claims using statistical and conceptual rules.

4. Communication and review: claims are expressed in forms others can evaluate and contest.

5. Synthesis and integration: results are positioned relative to one another to form stable maps.

6. Correction and revision: inconsistencies are addressed through replication, reanalysis, and theory update.

A field is stable when these transformations preserve enough compatibility that results can be related without constant renegotiation of definitions, methods, and evidence thresholds. A field destabilizes when the pipeline produces outputs faster than it can preserve reconstruction, comparability, and integration.

2.3 Distributed Structure

Science is not a single institution. It is a distributed system composed of laboratories, journals, universities, repositories, funding bodies, professional societies, and training pathways. Each component shapes interpretive conditions by shaping what is recorded, what is rewarded, what is publishable, and what is correctable.

Because the system is distributed, stability cannot rely on any one actor’s intent. Stability depends on whether shared interpretive constraints remain intact at the points where claims are generated, transferred, evaluated, and integrated.

3. Variables and Observables in Science

The MSS variables correspond to observable properties of scientific work. Each variable can be strengthened or weakened by structural conditions, and each has recognizable failure signatures.

3.1 Truth Fidelity (Tₛ)

Definition: Truth fidelity is the degree to which scientific claims maintain disciplined correspondence to the phenomena they describe under reconstruction, including clear boundary conditions for where a claim does and does not apply.

Primary observables

• reconstructability of the inference chain (data, code, methods, provenance)

• measurement validity and calibration discipline

• transparent reporting of decisions that affect results

• access to materials sufficient for independent evaluation

• equivalence discipline: specification of operationalizations, preprocessing, and protocol conditions sufficient to determine whether two tests are meaning-equivalent

Failure signatures

• results that cannot be reconstructed without private knowledge

• ambiguous provenance (unclear origins of data or preprocessing steps)

• claims that depend on undocumented analytic flexibility

• instability under minor reanalysis because correspondence conditions were under-specified

• replication ambiguity where “the same construct” or “the same method” cannot be established because operational and protocol conditions were not specified as part of the claim

3.2 Signal Alignment (Pₛ)

Definition: Signal alignment is the degree to which a field’s authority cues, incentive structures, and action-weight signals reinforce the same promised reference conditions rather than rewarding outputs that bypass them.

Primary observables

• review and publication criteria that privilege reconstructability over narrative closure

• career and funding incentives that make correction, replication, and synthesis viable work rather than reputational risk

• citation and prestige dynamics that weight evidential discipline, not only novelty and rhetorical coherence

• institutional signals that keep boundary conditions, uncertainty declarations, and comparability constraints action-relevant

• alignment between stated norms (transparency, rigor) and the actual reward and penalty structure

Failure signatures

• novelty pressure dominating correction capacity (high output, low verification)

• review standards rewarding persuasive framing over reconstructable evidential chains

• career-risk asymmetry that suppresses error admission, replication, and reanalysis

• incentive-driven “false closure” where interpretive confidence rises faster than evaluability

• misalignment between declared rigor norms and the behaviors the system actually rewards

3.3 Structural Coherence (Cₛ)

Definition: Structural coherence is the degree to which a field’s concepts, methods, and explanatory structures remain mutually compatible enough to support integration over time, including stable mapping across implementations and sites.

Primary observables

• existence and use of synthesis pathways (reviews, meta-analysis, consensus workflows)

• compatibility of definitions and assumptions across neighboring subdomains

• continuity of conceptual lineage sufficient to relate new claims to prior structures

• infrastructure that supports stable mapping of findings (taxonomies, ontologies, benchmarked definitions)

• method and protocol mapping mechanisms that make “same named method” and “comparable test” claims evaluable across toolchains, sites, and implementations

Failure signatures

• fragmented literatures that cannot be reconciled without redefining core terms

• accumulation of incompatible assumptions that prevent comparison

• high output with low integration capacity (many results, no stable map)

• repeated reinvention because continuity mechanisms fail

• implementation divergence where a “same method” label hides materially different operational behavior, preventing integration and convergence

3.4 Drift (Dₛ)

Definition: Drift is the inconsistency accumulation rate: the rate at which inconsistencies accumulate faster than a field’s correction and integration mechanisms can address.

Primary observables

• mismatch between production velocity and correction capacity

• persistence of contradiction clusters without convergence pathways

• instability of baselines across time windows (rapid reinterpretation cycles)

• proliferation of local standards that reduce interoperation

Failure signatures

• unresolved contradictions that persist despite continued output

• replication volatility without mechanisms that produce convergence

• short half-life of stable baselines (evaluation conditions change faster than they can be integrated)

• rising dependency on local context because global interpretability weakens

3.5 Affective Regulation (Aₛ)

Definition: Affective regulation is the degree to which the scientific environment sustains the human capacity required for correction, revision, and uncertainty tolerance under load.

Primary observables

• viability of error admission and correction without disproportionate penalty

• availability of attention and time for verification and synthesis

• procedural clarity and fairness in evaluation processes

• institutional support for high-effort correction work

Failure signatures

• avoidance of replication and correction due to career-risk asymmetry

• defensive epistemics under reputational pressure

• adversarial discourse displacing calibration and revision

• verification overload that exceeds human capacity, reducing correction throughput

4. Legitimacy in Scientific Meaning Systems

4.1 Definition of Scientific Legitimacy (Lₛ)

In C2, scientific legitimacy is the stability of interpretation under transfer and scrutiny. A field is legitimate to the extent that its claims remain:

• reconstructable (others can reproduce the evidential chain)

• comparable (results can be related across sites and methods)

• integrable (findings can be positioned inside stable structures)

Legitimacy is not the same as status, consensus, or institutional authority. It is a structural property: whether the field can keep evaluation conditions stable enough for disagreement to remain productive rather than reorganizing into incompatible baselines.

4.2 Proportional Form

C2 formalizes legitimacy as a proportional relationship:

Lₛ = (Tₛ × Pₛ × Cₛ) ÷ Dₛ

• Tₛ increases legitimacy when promised reference conditions are reconstructable.

• Pₛ increases legitimacy when signals retain consistent meaning across contexts.

• Cₛ increases legitimacy when the field can integrate results without redefining its core terms every cycle.

• Dₛ decreases legitimacy when inconsistency accumulation outpaces correction and integration.

This equation is not a claim that science reduces to arithmetic. It is a governance instrument that names the structural dependence of legitimacy on proportional capacity.

4.3 Typical Destabilization Patterns

Scientific legitimacy decreases predictably when variation rises faster than stabilization capacity. The most common structural pattern is not error. It is non-portability:

• claims cannot be reconstructed without tacit knowledge

• methods do not map cleanly to named categories (a “same method” label does not guarantee equivalence)

• results cannot be positioned inside a stable synthesis map

• correction work becomes too slow relative to production

4.4 Failure Modes Under High Variation

Two distinct failure modes appear when non-portability increases.

• Closure Failure occurs when correction pathways become structurally blocked. Apparent stability may remain in place by preventing revision, even as inconsistency accumulates.

• Constraint Failure occurs when evaluation constraints are under-specified. Equivalence rules, evidence thresholds, and boundary conditions become too weak to preserve reconstruction and comparability, so disagreement persists across incompatible baselines.

When either mode dominates, drift increases as a rate and the field reorganizes around local baselines.

4.5 What Governance Targets

Governance targets evaluation conditions, not conclusions. In practice, that means:

• increasing reconstruction capacity (Tₛ)

• maintaining comparability standards (Pₛ)

• strengthening synthesis and conceptual compatibility (Cₛ)

• monitoring inconsistency accumulation and correction throughput (Dₛ)

• preserving correction viability under load (Aₛ)

5. Case Study A: Replication as a Portability Test

5.1 Background

Replication disputes are often framed as social conflict. Structurally, they are a visibility event: replication makes the requirements of portability observable.

If a result cannot be reconstructed across reasonable variation in settings, then one of two conditions typically applies:

• The claim was never specified with sufficient promised reference conditions (Tₛ weakness), or

• The signals required for comparability were not aligned across environments (Pₛ weakness), often compounded by coherence limits (Cₛ) and correction lag (Dₛ).

Replication is therefore not a special activity. It is the scientific meaning system testing whether interpretation can travel.

5.2 Variable Analysis (Tₛ, Pₛ, Cₛ, Dₛ, Aₛ)

• Truth Fidelity (Tₛ) becomes weak when results depend on underreported degrees of freedom: which outcomes were prioritized, which exclusions were applied, which transformations were chosen, and which model specifications were treated as default. When these correspondence conditions remain implicit, reconstruction depends on local knowledge rather than shared evidence.

• Signal Alignment (Pₛ) becomes weak when “the same construct” is measured differently across sites, populations, instruments, or analytic pipelines. The result is not necessarily falsity. The result is that signal meaning is not stable enough for direct comparison. Replication then becomes ambiguous because the system lacks shared alignment standards for what counts as the same test.

• Structural Coherence (Cₛ) becomes weak when a field accumulates partially overlapping constructs, instruments, and theory fragments without stable integration pathways. In that environment, replication does not converge the field. It produces parallel explanation tracks because outcomes cannot be positioned inside a shared conceptual map.

• Drift (Dₛ) increases when novelty production is high while correction and synthesis are under-resourced. In that regime, contradictory results accumulate faster than the field can resolve them. Replication becomes episodic controversy rather than a convergence mechanism.

• Affective Regulation (Aₛ) becomes weak when career incentives and reputational risk make correction work adversarial. Under high strain, error admission is costly, replication efforts are treated as threat, and discourse becomes punitive rather than calibrating. That reduces correction throughput and increases drift pressure.

5.3 Governance Responses

A mature replication response strengthens portability by stabilizing evaluation conditions:

• constraint mechanisms that reduce undocumented flexibility and clarify what was planned versus discovered

• publication structures that reward correction work and make verification publishable, not secondary

• reconstruction infrastructure that makes data, code, and analytic provenance part of the claim, not optional attachments

• comparability standards that define when measurements are equivalent enough to count as the same test

• synthesis capacity that increases the field’s ability to integrate outcomes into stable maps rather than treating each dispute as isolated

5.4 Lessons

Replication volatility is not primarily a cultural failure. It is often a proportional failure.

• If Tₛ is under-specified, reconstruction becomes local.

• If Pₛ is weak, comparison becomes ambiguous.

• If Cₛ is weak, correction does not converge.

• If Dₛ outpaces correction, inconsistency persists.

• If Aₛ becomes binding under load, correction becomes costly and slows.

6. Case Study B: Computational Research as an Interpretive System

6.1 Background

Computational research makes a governance fact visible: in many fields, the evidential chain now includes a software environment.

A result can be equivalent in intent while differing materially in practice because small differences in preprocessing, randomization, library versions, default parameters, hardware behavior, or evaluation procedures can change outputs. When those dependencies are not represented as part of the claim, reconstruction and comparison become unreliable even when the underlying idea is sound.

This is not a niche problem. Computational work sits inside most scientific domains through simulation, statistical modeling, image and signal processing, bioinformatics pipelines, and machine learning. As computational tooling grows, the interpretive degrees of freedom between observation and claim increase. That increases the governance burden on reconstruction and comparability.

6.2 Variable Analysis (Tₛ, Pₛ, Cₛ, Dₛ, Aₛ)

• Truth Fidelity (Tₛ) in computational work depends on whether the claim includes enough information to reproduce the transformation from inputs to outputs. Data availability is not sufficient if the pipeline is not reconstructable. The promised reference condition becomes: can another group, under a declared environment, reproduce the output and verify that the output remains anchored to the described phenomenon or task?

  Tₛ weakens when results depend on tacit environment details, undocumented preprocessing, or untraceable intermediate artifacts. The claim becomes locally meaningful inside the original environment but less portable.

• Signal Alignment (Pₛ) depends on whether the reported outputs have comparable meaning across implementations and settings. In computational fields, two groups can use the same named method but implement it differently. They can use the same dataset but transform it differently. They can use the same metric but compute it differently. Alignment fails when these differences are not made legible and standardized enough for comparison.

  Pₛ weakens when method labels substitute for specification, when evaluation protocols vary without declaration, and when the meaning of performance results depends on hidden choices rather than shared interpretation rules.

• Structural Coherence (Cₛ) depends on shared integration structures that allow findings to accumulate into a stable map. In computational research, coherence weakens when each paper becomes its own toolchain and evaluation regime. The field then grows by volume but not by integration because results cannot be positioned relative to each other with stable comparability.

  Cₛ strengthens when communities converge on shared representations, shared evaluation conventions, shared reporting standards, and shared repositories of artifacts that allow results to be related rather than compared rhetorically.

• Drift (Dₛ) increases when toolchain variation and output volume accumulate faster than verification and integration can stabilize. In computational systems, drift often develops quietly. It appears as divergence in equivalence standards, shifts in evaluation conventions, and fragmentation into local baselines where each subcommunity treats its own protocol as standard.

• Affective Regulation (Aₛ) becomes a binding constraint because computational verification is expensive. Reproducing a result can require large compute budgets, specialized hardware, large data access, and substantial engineering time. Under competitive conditions, this produces verification scarcity. When verification becomes rare, correction capacity drops and drift pressure increases. When verification becomes socially costly, correction slows further.

6.3 Governance Responses

Computational stability improves when the system treats reproducibility conditions as part of the scientific claim.

• Environment specification: declaring dependencies, versions, and execution conditions so reconstruction does not depend on tacit knowledge.

• Artifact discipline: packaging code, configuration, and necessary intermediate products in forms that can be executed and inspected.

• Provenance and traceability: recording how data were transformed and how outputs were produced so correspondence conditions are auditable.

• Evaluation protocol stability: specifying metrics, datasets, splits, preprocessing, and comparison rules so results are comparable across groups.

• Verification capacity: creating institutional support for reproduction and reanalysis so correction is structurally viable.

These measures do not guarantee agreement. They ensure disagreement remains evaluable inside shared constraints.

6.4 Lessons

Computational research reveals an invariant: scientific legitimacy depends on whether evaluation conditions travel with the claim.

• If the environment is not reconstructable, Tₛ becomes local.

• If evaluation protocols differ without clear mapping, Pₛ weakens.

• If results cannot be integrated into shared comparative structures, Cₛ weakens.

• If output volume and tool variation exceed correction capacity, Dₛ increases.

• If verification becomes scarce and punitive, Aₛ becomes binding and drift pressure rises.

This case shows why governance is not optional in computationally mediated science. It is the mechanism for preserving portable evaluation as toolchains and velocity increase.

7. Synthetic Systems as a Modifier of Scientific Interpretation

7.1 Effects on Variables

Synthetic systems modify scientific interpretation by introducing additional variation into how evidence is represented, summarized, transformed, and proposed. The primary governance risk is not that synthetic outputs exist. The risk is that synthetic variation can propagate faster than the system can preserve reconstructability, comparability, and integration.

Synthetic systems also introduce a distinct pressure: they can generate artifacts that resemble governance outputs (reviews, syntheses, methodological guidance, convergence narratives) without the constraints that make those outputs evaluable. This can produce false closure: the appearance of synthesis or consensus without reconstructable provenance, declared uncertainty, or stable comparability rules.

• Truth Fidelity (Tₛ) is stressed when synthetic systems compress evidential detail or reframe claims without preserving promised reference conditions. When summaries omit boundary conditions, when generated explanations blur what was measured versus inferred, or when transformations cannot be traced to sources and decision rules, reconstructability weakens even if underlying data are sound.

• Signal Alignment (Pₛ) is stressed when synthetic outputs vary across model versions, training regimes, or interaction styles in ways that are not tied to underlying phenomena. If the same prompt produces materially different framings, or if two research groups rely on different systems that summarize the same corpus differently, alignment weakens unless shared conventions constrain how synthetic outputs are treated and compared.

• Structural Coherence (Cₛ) is stressed when synthetic systems generate relationships, categories, or explanatory framings that are not anchored to a field’s integration structures. This can increase conceptual output without increasing coherence capacity, expanding interpretive space faster than the system can stabilize shared maps.

• Drift (Dₛ) increases because synthetic systems can raise the rate of interpretive production. They can accelerate drafting, literature synthesis, hypothesis generation, and narrative framing. When evaluation and integration capacity does not scale with this added velocity, inconsistency accumulation accelerates. Drift pressure increases even if individual outputs appear plausible.

• Affective Regulation (Aₛ) is stressed because verification demands rise as interpretive volume rises. When the system generates more candidate interpretations than it can audit, uncertainty increases, defensive epistemics become more likely, and correction throughput declines. If synthetic systems increase output without increasing correction capacity, the system’s human regulatory bandwidth becomes a binding constraint.

7.2 Governance Boundary

C2 does not treat synthetic systems as illegitimate by default. The governance requirement is that synthetic use must preserve the interpretive conditions required for scientific legitimacy:

• synthetic outputs must be traceable to sources and constraints

• their role in the evidential chain must be declared

• they must not substitute for reconstruction requirements

• they must not destabilize comparability through hidden variation

The objective is not to prevent new tools. The objective is to keep scientific meaning portable under higher velocity.

8. Meaning-System Governance for Science

Scientific governance is stewardship of interpretive conditions that keep claims reconstructable, comparable, and integrable under load. Governance does not decide what is true. It maintains the structures that allow truth fidelity to be tested and maintained across time and institutions.

In C2, governance is organized around the variables.

8.1 Governance of Truth Fidelity (Tₛ)

Truth fidelity is preserved when claims include their promised reference conditions.

Governance strengthens Tₛ by ensuring:

• Definitional discipline: key constructs remain stable enough to support reconstruction and comparison.

• Measurement integrity: instruments and operationalizations are validated, calibrated, and reported with sufficient specificity.

• Reconstruction completeness: data, code, materials, and analytic provenance are treated as part of the claim, not optional context.

• Constraint transparency: decisions that affect outcomes (exclusions, transformations, modeling choices) are declared as part of the evidential chain.

When Tₛ governance is weak, claims become locally meaningful but non-portable. The field then cannot reliably distinguish disagreement caused by reality from disagreement caused by missing promised reference conditions.

8.2 Governance of Signal Alignment (Pₛ)

Signal alignment is preserved when equivalence is governed, not assumed.

Governance strengthens Pₛ by ensuring:

• Measurement equivalence standards: clear rules for when instruments and constructs are comparable across sites.

• Method specification norms: named methods are specified sufficiently that equivalence can be tested.

• Evaluation protocol stability: metrics, datasets, preprocessing, and comparison rules are disclosed and standardized where possible.

• Interoperable reporting: results are communicated in forms that preserve comparability, not only narrative plausibility.

When Pₛ governance is weak, fields reorganize into local standards. The result is loss of interoperation: the system cannot reliably relate results across its own boundary.

8.3 Governance of Structural Coherence (Cₛ)

Structural coherence is preserved when findings can be positioned inside shared maps without redefining the system each cycle.

Governance strengthens Cₛ by ensuring:

• Synthesis capacity: stable pathways for integration (reviews, meta-analyses, consensus processes, field maps).

• Conceptual compatibility: explicit handling of competing definitions, assumptions, and boundary conditions.

• Continuity infrastructure: taxonomies, ontologies, reference glossaries, and agreed frameworks where a domain requires them.

• Correction integration: contradictory findings are not only published, but structurally integrated into the field’s shared model.

When Cₛ governance is weak, a field can produce massive output while losing the ability to form stable explanatory structures. Knowledge exists, but it does not accumulate into a coherent map.

8.4 Drift Governance (Dₛ)

Drift governance is the ability to detect and respond when inconsistency accumulation exceeds stabilization capacity. Drift is treated as a rate condition that rises when production velocity, toolchain variation, and interpretive throughput exceed the system’s correction and integration capacity.

Operationally, drift should be monitored through rate-condition proxies, not inferred from contradiction counts alone.

Governance reduces Dₛ by maintaining:

• Correction throughput: replication, reanalysis, negative results, and correction-oriented work remain viable and publishable.

• Stabilization rhythms: synthesis and verification capacity scale with production volume and methodological variance.

• Drift monitoring: the system tracks contradiction clusters, non-reconstructable claims, and comparability failures as structural indicators of rising drift pressure.

• Boundary discipline: the field distinguishes genuine boundary extension from ambiguity that weakens shared promised reference conditions and comparability constraints.

Drift response requires failure-mode identification.

• Closure Failure is drift driven by restricted correction permeability. The governance response prioritizes reopening revision pathways: making replication, reanalysis, and correction structurally accessible, materially resourced, and procedurally viable so contradictions can update the shared baseline.

• Constraint Failure is drift driven by under-specified evaluation constraints. The governance response prioritizes strengthening equivalence rules, evidence thresholds, boundary conditions, and comparability standards so disagreement remains interpretable inside shared constraints rather than solidifying into incompatible local baselines.

When drift is not monitored as a rate and classified by mode, the system responds late. As local baselines solidify, interpretive convergence becomes materially harder even if later governance improves.

8.5 Governance of Affective Regulation (Aₛ)

Affective regulation is preserved when the system maintains human correction capacity under uncertainty and competitive load.

Governance strengthens Aₛ by ensuring:

• Correction safety: error admission and revision are structurally viable rather than career-ending.

• Procedural clarity: evaluation processes are legible, consistent, and fair enough to reduce defensive epistemics.

• Resource realism: verification is funded and resourced, not treated as volunteer labor.

• Load management: institutions treat cognitive bandwidth as a limiting variable and treat synthesis and correction as first-order work.

When Aₛ becomes binding, correction capacity falls. Drift pressure increases even if technical capability increases.

9. Proportional Governance Principles

Scientific governance is not a checklist. It is proportional: the required stabilizers depend on the rate and type of variation the field is experiencing.

9.1 Proportional Adjustments

As variation increases, at least one stabilizer must increase proportionally, or legitimacy weakens.

• If publication volume rises, synthesis capacity (Cₛ) and correction throughput must scale.

• If methodological diversity rises, signal alignment (Pₛ) standards must strengthen to preserve comparability.

• If toolchains become more complex, truth fidelity (Tₛ) requirements must include environment and provenance.

• If synthetic output increases interpretive volume, drift governance (Dₛ) and Aₛ support must scale or the system saturates.

9.2 What “Governance Success” Looks Like

Governance succeeds when:

• disagreements remain interpretable inside shared constraints

• claims remain reconstructable without private knowledge

• results remain comparable across environments

• integration mechanisms keep pace with production

• correction remains socially and materially viable

The goal is not to eliminate uncertainty. The goal is to preserve a disciplined environment where uncertainty can be reduced through reconstructable evaluation rather than factional baselines.

10. Institutional Responsibilities

Science remains stable only if the institutions that host it maintain the interpretive conditions the system depends on. Because the scientific meaning system is distributed, no single institution can preserve legitimacy alone. Governance is a coordination responsibility across the main carriers of evaluation.

10.1 Journals and Publishers

Journals shape what counts as a publishable claim, what counts as adequate reconstruction, and what kinds of correction work are legible as contribution.

Primary responsibilities

• Reconstruction norms: require sufficient methodological specificity, provenance disclosure, and access to materials to make claims reconstructable in principle.

• Comparability norms: enforce consistent reporting of measures, assumptions, and protocols so results remain comparable across contexts.

• Correction viability: maintain publication pathways for replication, reanalysis, negative results, and correction-oriented work.

• Synthesis visibility: support integrative work as a first-class output, not a secondary prestige track.

10.2 Universities and Research Institutions

Universities set training conditions, allocate time and attention, and define evaluation incentives that shape affective regulation.

Primary responsibilities

• Training for reconstruction: teach evidential chain discipline, specification standards, and interpretive constraint management as core scientific literacy.

• Resource allocation: treat verification and synthesis as institutional work with time, staffing, and credit.

• Procedural clarity: reduce ambiguity in evaluation structures that convert correction into reputational threat.

• Cross-field interoperability: maintain infrastructure that supports shared definitions and standards where domains interlock.

10.3 Funding Bodies

Funding bodies shape the ratio of novelty to verification and determine whether correction and integration can scale with production.

Primary responsibilities

• Verification funding: support replication, reanalysis, infrastructure maintenance, and data stewardship.

• Stabilization incentives: reward work that improves comparability and integration, not only novelty.

• Boundary realism: fund coordination and standardization as scientific labor when a field’s drift rate rises.

• Transparency expectations: require evidential chain legibility as a governance condition, not as an ethical add-on.

10.4 Societies, Standards Groups, and Repositories

Professional societies and shared repositories are direct carriers of alignment and coherence infrastructure.

Primary responsibilities

• Definition and measurement standards: publish and maintain shared terminology, construct definitions, and equivalence rules.

• Shared maps: maintain field taxonomies, methodological catalogs, and integration frameworks.

• Artifact stewardship: preserve datasets, code, benchmarks, and reference materials with stable provenance.

• Correction pathways: provide venues for convergence work that do not depend solely on journal incentives.

11. Conclusion

Science is a meaning system because it produces something more specific than “knowledge.” It produces portable evaluation: claims that remain reconstructable, comparable, and integrable across people, institutions, and time. This is what makes disagreement productive rather than factional. A scientific claim is not only a statement about the world. It is a statement plus the conditions under which it can be tested, related, and revised.

Modern scientific environments increase variation faster than inherited stabilizers scale. Output volume outpaces synthesis capacity. Toolchains expand the degrees of freedom between observation and claim. Incentives can elevate novelty pressure relative to correction throughput. Synthetic systems can increase interpretive velocity by accelerating summarization, framing, and proposal generation without automatically increasing audit capacity. Under these conditions, drift rises as a rate and the failure mode is non-portability: results become local, tacit, protocol-bound, or time-local.

Scientific governance therefore has a precise target. It does not choose conclusions. It maintains the evaluation conditions that allow conclusions to remain evaluable. The practical standard is simple: evaluation conditions must travel with the claim. Reconstruction requirements are part of the output, not optional context. Equivalence must be governed, not assumed. Integration must be resourced, not treated as an afterthought. Correction must remain materially viable and procedurally safe enough to function under competitive load.

The century ahead will not be governed by how much science is produced. It will be governed by whether science remains portable as it grows. Scientific leadership is stewardship of interpretive stability: keeping evaluation disciplined enough that uncertainty can be reduced through reconstruction rather than reorganized into incompatible baselines.

Citation

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

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