Sustainable Catalyst Methodology
Methodology
A shared operating method for research, documentation, experimentation, public evidence, calculation, modeling, publication, open-source development, and auditable decision support.
Sustainable Catalyst uses one institutional standard across the Open Knowledge Library, Research, Sustainable Catalyst Lab,
Site Intelligence, Workbench, Decision Studio, Platform Core, public documentation, and supporting modules: claims should be supportable, sources traceable, methods explainable, calculations reviewable, uncertainty visible, and responsibility human.
reviewed, corrected, versioned, or bounded, it is not finished.
Library + Documentation
Research + Lab
Site Intelligence
Workbench
Decision Studio
Methodology overview
A reviewable method for complex public-interest work
Sustainable Catalyst turns questions into connected public records: research routes, source records,
article maps, notebooks, experiments, datasets, indicators, models, calculations, interpretations,
briefs, repositories, documentation, release records, and decision packets.
The method resists unsupported certainty, silent transformation, black-box scoring, untraceable AI
output, false precision, incompatible comparison, undocumented model assumptions, and publication
without a correction path. The goal is not to make work look complete before it is trustworthy.
The goal is to make the reasoning and its limits visible.
Trace
Keep the route visible
Preserve the relationship among the original question, sources, methods, calculations,
interpretations, revisions, and final output.
Test
Make claims and models inspectable
Expose inputs, assumptions, units, transformations, compatibility rules, diagnostics,
uncertainty, and validation evidence.
Govern
Keep authority and responsibility explicit
Identify the responsible source, current status, review state, professional boundary,
human decision owner, and route for correction.
Institutional method architecture
One method across the Library, Lab, and Platform
Each institutional environment has a different function, but they share the same expectations for
provenance, evidence, explanation, validation, review, documentation, and responsible use.
Preserve and organize
Open Knowledge Library
Document identity, relationships, versions, collections, planned content, annotations,
source links, status, authority, and portable knowledge records.
Investigate and validate
Sustainable Catalyst Lab
Record questions, methods, instruments, observations, measurements, conditions, calculations,
validation, uncertainty, limitations, and reproducibility.
Connect and apply
Sustainable Catalyst Platform
Route research, inspect public evidence, calculate, compare, synthesize, document readiness,
preserve audit records, and export reviewable decisions.
Core principles
The working rules behind Sustainable Catalyst
These principles apply to public pages, research, documentation, repositories, datasets, models,
calculations, experiments, dashboards, AI-assisted workflows, exports, and decision records.
01
Claims must be supportable
A claim should connect to evidence, observation, measurement, calculation, source, method,
or clearly labeled interpretation. Unsupported claims should be revised, softened, sourced, or removed.
02
Sources must remain traceable
Source identity, date, publisher, geography, reporting period, retrieval state, definitions,
transformations, and limitations should remain connected to the output.
03
Methods must be explainable
Calculations, classifications, transformations, experimental procedures, model assumptions,
comparison rules, and decision logic should be understandable to another reviewer.
04
Analysis should be reproducible
Preserve inputs, units, code, formulas, versions, configurations, intermediate values,
exports, expected behavior, and validation evidence where reproducibility is feasible.
05
Uncertainty must be visible
Missing data, estimates, ambiguity, confidence, model limits, measurement error,
stale evidence, incompatible sources, and unresolved questions should be stated explicitly.
06
Status and authority must be clear
Living, current, draft, experimental, stale, unavailable, superseded, archived, verified,
and release-specific records should not be presented as equivalent.
07
Human judgment remains responsible
Tools may assist retrieval, analysis, drafting, calculation, comparison, classification,
and synthesis, but responsibility for interpretation and consequential use remains human.
08
Correction is part of the method
Public work should have a route for reporting errors, revising claims, updating methods,
superseding records, documenting change, and preserving history without hiding earlier states.
Operating workflow
From question to reviewable and reusable output
The workflow is modular rather than rigid. A project may use only part of the sequence, but each
completed stage should preserve enough context for the next stage and for later review.
- 01Question
Define the problem, inquiry, decision, audience, stakes, and intended contribution.
- 02Scope
Set definitions, system boundaries, geography, timeframe, exclusions, and professional limits.
- 03Map
Identify concepts, entities, actors, relationships, dependencies, claims, and related records.
- 04Source
Collect primary, institutional, scholarly, technical, public, observational, and generated material.
- 05Record
Create evidence, notebook, dataset, measurement, event, indicator, or document records with provenance.
- 06Model
Define assumptions, formulas, scenarios, classifications, comparisons, and analytical structure.
- 07Calculate
Run computation, code, graphing, simulation, statistics, or engineering analysis with visible units.
- 08Validate
Check inputs, edge cases, compatibility, expected outputs, uncertainty, and methodological limits.
- 09Interpret
Separate evidence, inference, judgment, values, recommendations, speculation, and open questions.
- 10Review
Apply editorial, technical, scientific, legal, ethical, accessibility, or professional review as needed.
- 11Publish
Create a page, paper, brief, dataset, model, notebook, repository, documentation record, or Decision Packet.
- 12Revisit
Correct, update, revalidate, supersede, archive, or extend the work as evidence and systems change.
question → scope → map → source → record → model → calculate → validate →
interpret → review → publish → revisit
Evidence and source discipline
Evidence is more than a link
A source becomes usable evidence only when its identity, context, coverage, method, state,
limitations, and relationship to the claim are understood.
Identity
Who produced the record?
Publisher, author, institution, dataset owner, instrument, repository, or reporting authority.
Coverage
What does it actually cover?
Geography, population, system boundary, variables, reporting period, units, and exclusions.
Method
How was it produced?
Measurement, sampling, estimation, transformation, modeling, coding, classification, or editorial process.
State
What is its delivery and validation state?
Live, cached, stale, delayed, unavailable, estimated, provisional, experimental, validated, or archived.
Compatibility
Can it be compared or combined?
Definitions, units, periods, geography, methods, missing values, and transformations must be compatible.
Claim relationship
What does the evidence support?
Direct observation, contextual background, correlation, inference, model input, illustration, or unresolved lead.
Modeling and calculation
Models clarify only when assumptions and limits remain visible
Mathematical, statistical, computational, economic, scientific, engineering, and decision models
are structured approximations. They should help explain relationships without being mistaken for the
systems they represent.
Record values, units, source, date, precision, missingness, defaults, and whether the value is observed or assumed.
State simplifications, boundary conditions, causal assumptions, distributions, thresholds, and excluded mechanisms.
Document normalization, scaling, filtering, aggregation, conversions, imputations, and derived variables.
Preserve formulas, code, versions, dependencies, solver settings, random seeds, intermediate values, and warnings.
Inspect numerical stability, residuals, condition, rank, fit, sensitivity, uncertainty, edge cases, and failure modes.
Explain what the result can support, what remains conditional, and where the model distorts or omits reality.
Validation and review
Validation is evidence about behavior—not proof of universal correctness
Validation should match the type of work. A webpage, dataset, scientific experiment, calculation,
AI route, software release, and decision brief require different checks and different reviewers.
Structural validation
Is the record complete and well formed?
Required fields, identifiers, schemas, links, formats, units, status, version, and relationships.
Source validation
Does the evidence support the claim?
Publisher identity, coverage, date, definitions, method, freshness, relevance, and limitations.
Computational validation
Does the calculation behave as expected?
Known cases, independent checks, dimensional consistency, edge cases, stability, diagnostics, and reproducibility.
Experimental validation
Are methods and observations credible?
Controls, calibration, instrumentation, repeatability, uncertainty, environmental conditions, and protocol adherence.
Interpretive validation
Does the conclusion exceed the evidence?
Inference, causality, generalization, confidence, alternative explanations, values, and unresolved questions.
Operational validation
Does the public system work responsibly?
Accessibility, mobile behavior, error states, privacy, security boundaries, service status, logging, and recovery.
Documentation and institutional memory
Documentation is part of the product, not an afterthought
Documentation should explain what a system is, how it works, which source is authoritative,
what changed, what remains limited, and how a reader can reproduce, review, or correct the work.
Why the record, tool, method, experiment, or release exists and what public need it addresses.
Included capabilities, excluded capabilities, intended audience, dependencies, and professional boundaries.
Current governing source, responsible area, owner, version, review date, and replacement record when superseded.
Inputs, outputs, workflows, configuration, schemas, methods, integrations, and expected public behavior.
Tests, examples, expected results, review evidence, limitations, warnings, and known failure states.
Release notes, migrations, corrections, compatibility, archived snapshots, and reasons for significant change.
Responsible AI
AI in the toolkit, never in control
AI may assist retrieval, routing, classification, drafting, translation, coding, calculation support,
comparison, summarization, and synthesis. It does not become the source of truth, the institutional
decision owner, or the final authority.
AI can accelerate work, but it cannot replace evidence, source authority, reproducibility,
qualified judgment, institutional responsibility, or review.
Applying the method
How the methodology appears across the public products
Each product implements the shared method through different records, controls, disclosures,
validations, and public outputs.
Route with confidence and boundaries
Show why a route was selected, what source it comes from, available alternatives, confidence, fallback state, and next actions.
Preserve metadata, status, and relationships
Track record identity, collections, planning state, authority, versions, related documents, annotations, and portable exports.
Document methods, observations, and validation
Preserve experimental conditions, instrumentation, measurements, calculations, results, uncertainty, provenance, and reproducibility.
Expose source, data state, and comparison limits
Keep publisher, connector, coverage, freshness, transformation, missing values, delivery state, and interpretation boundaries visible.
Make calculations inspectable
Preserve formulas, code, inputs, units, intermediate values, graphs, diagnostics, warnings, validation notes, and reports.
Keep evidence, assumptions, and readiness connected
Link artifacts, scenarios, claims, tradeoffs, calculations, risks, unresolved issues, review states, and audit-ready exports.
Responsible-use boundaries
Methodology improves reviewability; it does not create automatic authority
A documented, reproducible, or well-designed process can still contain poor evidence, wrong assumptions,
implementation errors, incomplete models, biased interpretation, stale data, or inappropriate use.
Correction and improvement
A public method needs a public correction route
Visitors should be able to report wrong routes, broken links, stale documentation, missing sources,
accessibility issues, calculation concerns, unclear authority, incompatible comparisons, and needed
methodological clarification.
Identify a claim, citation, source state, interpretation, or documentation record that may require correction.
Identify a broken workflow, unexpected result, validation concern, error state, accessibility problem, or missing boundary.
Suggest a clearer standard, review check, validation method, disclosure, source rule, or documentation requirement.
Next step
Use the methodology as the operating logic of the institution
Read the Foundations documentation, follow a research pathway, investigate in the Lab, inspect public
evidence, calculate in Workbench, or assemble a reviewable decision record in Decision Studio.