Open Methodology · Version 3.1
The Data Board
"Given a good enough set of semantics — can we use language to represent data?"

Data has always required an intermediary to reach human thought — visualization to make patterns visible, statistics to surface relationships. Both are bottom-up: they start from numbers and work toward meaning.

Large language models may be the pivotal moment that changes this. Trained on the accumulated written knowledge of human civilisation, they encode domain semantics at a scale that has never existed before. The Data Board tests a top-down alternative: start with synthesized semantics — a vocabulary that, under the right conditions, is good enough to represent the data in question.

AI generates or human proposes. AI evaluates. Human decides.
AuthorRuth Aharon
Version3.1 · 2026
LicenseMIT · Open Source
Sitethedataboard.ai
The Method

From raw data
to inevitable narrative

The method is top-down. It begins with the question and the domain, not the columns. AI seeks concepts that express more than a basic variable — named mechanisms, not labels. Two parallel mechanisms feed the board: pseudo-antonym pairs that create structural tension, and evidence checking that grounds each concept in what is known about the domain. Both feed into a cohesiveness check across all candidates, producing a minimal board and an expandable board.

Data Board · Method Flow
RAW DATA Dataset · corpus · question SEMANTIC SYNTHESIS AI seeks concepts that express more than a basic variable PSEUDO- ANTONYMS tension pairs create the story EVIDENCE CHECK grounded in corpus and domain knowledge COHESIVENESS CHECK MINIMAL BOARD EXPANDABLE BOARD WHAT MAKES A GOOD CONCEPT Not just a column — a named mechanism. "Wealthy Surcharge" not "rich countries" EXAMPLE PAIR Social Cohesion ↔ Atomized Autonomy TWO EVALUATION DIMENSIONS Relevance — density in LLM corpus Cohesiveness — fit with other concepts MINIMAL vs EXPANDABLE Minimal: core deducible space Expandable: + shadow / edge concepts
Glossary

Key concepts

Deducible Space

The minimal set of grounded, coherent, tension-bearing concepts from which consistent narrative conclusions follow inevitably. Not a list of variables — the conceptual foundation that makes reasoning possible and narrative non-arbitrary.

Pseudo-Antonyms

Concept pairs occupying opposite ends of the same analytical dimension. Not logical opposites — structurally opposing concepts within a shared domain. Conditional, not mandatory: most concepts have no natural opposite, and forcing one onto every concept is exactly the failure mode this framework exists to catch.

Calibrated Handle

A concept at the right level of abstraction: precise enough to be grounded in evidence, general enough to reason from. Northern European Premium names a mechanism (a price premium surviving GDP-adjustment in a specific six-country cluster), compresses a pattern that spans that cluster, and creates structural tension against its pseudo-antonym. Countries that are rich is not a calibrated handle — it describes a category, implies no mechanism, generates no analytical direction, and would overclaim, since most high-GDP countries don't actually show the pattern.

Verification Shift

When vocabulary is supplied, the AI moves from invention to verification — checking whether concepts are descriptive, domain-coherent, and evidentially grounded rather than generating labels freely. The AI stops guessing meaning and starts checking it.

Concept (Glaser & Strauss, 1967)

A label earns concept status only once multiple, diverse indicators converge on the same underlying mechanism. A single grounded data point is an indicator, not a concept — it points toward a possible category, it doesn't establish one. Where indicators diverge — the same statistical direction produced by genuinely different mechanisms — the honest result is not a concept; it's residual.

Theoretical Saturation

The signal to stop refining a concept, or to stop generating new ones. A concept is saturated once new indicators only extend its range instead of reshaping what it means. A board is complete, for the evidence at hand, not when every data point has a label, but when the next candidate grouping fails the convergence test — that failure is the stopping signal, named honestly in the residual rather than forced into a manufactured concept.

Core Category

Among a board's concepts, the one with the greatest explanatory reach — not just central, but the pattern the others are read as deviations from. Distinct from Dominant centrality, a per-concept weight: a board can have several Dominant concepts, but only one plays this organizing role relative to the rest.

Semantic Weight

Centrality of a concept in the evidence base — in Glaser & Strauss's terms, a property, with each concept's Dominant/Present/Edge Case rating as its dimension: a specific position on that property's range, not a fixed identity. Three levels:

DOMINANT — primary causal driver PRESENT — real but not decisive EDGE CASE — marginal or structural outlier
Minimal vs Expandable Board

The minimal board contains the core deducible space — the smallest coherent set of concepts from which the global story follows. The expandable board adds shadow concepts and edge cases: the structural tensions that challenge or complicate the dominant narrative.

Evaluation

What the color means

Every tile on the board is colored. The color is not aesthetic — it is the result of the logic audit. Here is what earns each color.

Dominant
The concept
drives the story
Earns green when
Descriptive — names a condition, not a conclusion
Grounded — supported by domain evidence
Coherent — fits the board and creates tension with its pseudo-antonym
"Social Cohesion" — passes all three. Gallup data, upstream of life satisfaction, contrasts with Atomized Autonomy.
Present
The concept
complements the story
Earns yellow when
Descriptive — names a condition, not a conclusion
Grounded — supported by domain evidence
~ Supplementary — real and evidenced, enriches the narrative but is not essential to it
"Generosity" — grounded, descriptive, adds texture to the happiness story but the story holds without it.
Edge Case
The concept
carries its own narrative
Earns red when
Descriptive — names a condition, not a conclusion
Grounded — supported by domain evidence
! Isolated or marginal narrative — anomaly, exception, or pattern that exists outside the dominant story. Essential for detecting outliers and understanding boundaries.
"Atomized Autonomy" — a real narrative in individualistic cultures, but marginal globally. Flags where the dominant story breaks down.
Rejection is insight — a concept that fails any test is not discarded silently. The failure names the assumption the analyst was making without knowing it.
Confound check — before a pattern is credited, check whether another accepted concept already explains it. If so, the concept is downgraded and the confound named — not silently dropped. Most concepts pass untouched; this is a narrow check, not a filter that thins the board.
Completeness check — every set of narrative threads names, in one honest sentence, what the accepted concepts do not explain — its own "Unaddressed" entry, not buried in prose. The verbal parallel to a residual in a variance decomposition.
Logic Layer

The YAML audit trail

Every accepted concept has a formal machine-readable representation — the YAML logic block. This is what separates the Data Board from a sticky-note exercise. The YAML makes each concept auditable: it documents the mechanism, the evidence, the scope conditions, the fidelity score, and the pseudo-antonym relationship. It is the reproducible, citable record of every analytical decision the board makes.

The contrasts_with field is the pseudo-antonym link. The valid_when conditions define when the concept holds and when it breaks down. The fidelity score (0–1) measures how well the concept survived the logic audit.

YAML Logic Block — Social Cohesion
concept "Social Cohesion" is a: driver context: "Social support systems" mechanism: "trusted social networks provide emotional and material safety nets" evidence: "Gallup World Poll social support metrics" covers: explains: [national_happiness_variance] aggregates: [social_support_score] contrasts_with: "Atomized Autonomy" ← pseudo-antonym link fidelity: 0.92 ← survives the logic audit fidelity_basis: empirical_test valid_when: - "strong community ties" - "institutional stability" ← scope conditions
For Practitioners

The system prompt

Copy this into any LLM (Claude, ChatGPT, Gemini) to activate the Data Board methodology before analysis begins.

Data Board System Prompt · v4.0
You are applying the Data Board methodology, created by Ruth Aharon (thedataboard.ai). Your role: Paradigm Generator, not Author. AI generates or human proposes vocabulary. You evaluate it. Core directives: 1. Naming is analysis. Treat every concept as a type that carries analytical weight. 2. Concept-first, then check: propose the way an analyst actually thinks — a hypothesis drawn from domain knowledge — then check it against the data. Do not enumerate columns and wait for patterns to emerge. 3. Grounding is mandatory: cite specific values from the actual data for every claim. If no data is available, say "general domain knowledge, not data-verified" rather than inventing a plausible-sounding number. 4. Calibrated naming cuts both ways: climb to a synthesized name only when the literal term would flatten a real mechanism. Otherwise keep the literal term. 5. Pseudo-Antonyms are conditional, not mandatory: only pair a concept with a structural opposite when a real one survives the same grounding check. Most concepts have no opposite, and that's correct, not a gap. 6. Semantic weight: assign Dominant, Present, or Edge Case based on centrality in the evidence. 7. Rejection is insight: when you reject a concept, explain why. 8. Confound check: before crediting a pattern, ask what else visible in the data could explain it. If another accepted concept already explains the same split, don't drop the new one — downgrade its weight one notch and name the confound directly. Only drop it if the pattern actually disappears once you account for the confound. This should touch a minority of concepts, not gut the board down to two or three. 9. Completeness check: when you group concepts into narrative threads, also name — in one honest sentence — what the accepted concepts do NOT explain. Not a vague disclaimer; a real, specific gap. This is the same job a residual does in a statistical model. Workflow: 1. Review the raw data and question. 2. Propose or evaluate a vocabulary board (Dominant, Present, Edge Case), checking every concept against the data before it's accepted. 3. Audit causal tension — identify pseudo-antonym pairs only where genuine. 4. Identify narrative threads — which concepts, together, carry one story — and name what the board leaves unaddressed. 5. Synthesize the global story based ONLY on the established board.
Worked Example

World Happiness 2025

World Happiness Report 2025
"What structural conditions explain why high GDP does not guarantee high happiness?"
Dominant
Economic Security
Sharpness
Dominant
Social Cohesion
Sharpness
Dominant
Healthy Life Expectancy
Sharpness
Present
Institutional Trust
Sharpness
Present
Individual Freedom
Sharpness
Present
Generosity
Sharpness
Edge Case
Systemic Distress
Sharpness
Edge Case
Atomized Autonomy
Sharpness
Edge Case
The Freedom Gap
Sharpness
Pseudo-antonyms
Social CohesionAtomized Autonomy
Institutional TrustSystemic Distress
"Global well-being is a structural outcome of the balance between Institutional Trust and Individual Freedom. High GDP is necessary but not sufficient — Atomized Autonomy is the shadow of Individual Freedom that GDP cannot measure."
Cohesion 88
Coverage 92
Sharpness 90
Entropy 45

The non-trivial finding: the same freedom that produces the highest happiness scores in Nordic nations produces the highest loneliness rates in individualistic cultures without strong social infrastructure. The tension between Individual Freedom and Atomized Autonomy is the mechanism. The board makes it visible. A regression finds the correlation and calls it "freedom." The board names what is inside it.

Theoretical Anchors

Where this connects

Pearl, J. & Mackenzie, D. (2018). The Book of Why. Basic Books. — The ladder of causation. The Data Board addresses the prerequisite Pearl assumes: knowing which concepts belong before building the causal model.
Glaser, B. & Strauss, A. (1967). The Discovery of Grounded Theory. Aldine. — The Concept-Indicator Model: a concept is only validated once multiple, diverse indicators converge on it; a single data point is an indicator, not a concept. The Data Board's definition of "Concept" is this model applied directly, including its stopping rule — comparison ends when new data stops changing what a category means (theoretical saturation), and a grouping that fails to converge on one mechanism is reported as residual, not forced into a concept.
Wittgenstein, L. (1922). Tractatus Logico-Philosophicus. — "The limits of my language are the limits of my world." The Data Board is a formal process for extending the analytical vocabulary — and therefore the analytical world.
Luhn, H.P. (1958). "A Business Intelligence System." IBM Journal of Research and Development. — Intelligence as guiding action toward a desired goal. The Data Board formalises the naming step that makes the goal speakable.
License

Open source.
All the way down.

The Data Board methodology, including the term Pseudo-Antonyms, is released under the MIT License — free for commercial and non-commercial use, modification, and distribution globally, no separate permission needed.

Cite as: Aharon, R. (2026). The Data Board: A Methodology for Language-Based Data Analysis. thedataboard.ai