Constitutional Map AI

Blog tutorial

Short entries for learning the atlas workflow faster: how to select countries, compare distributions in the 3D canvas, and validate a finding in the full constitutional text.

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Getting started

Getting Started with the Semantic Map

A short walkthrough for selecting countries, reading point clusters, and understanding what semantic proximity means in practice.

Read tutorialMar 30, 2026

Short tutorials for learning how to navigate the semantic map, compare countries, and validate findings in the full constitutional text.

Getting startedMar 30, 20264 min

Getting Started with the Semantic Map

A short walkthrough for selecting countries, reading point clusters, and understanding what semantic proximity means in practice.

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ComparisonMar 30, 20264 min

Comparing Countries Without Getting Lost in 3D

A compact routine for keeping comparisons readable, moving between countries, and grounding visual impressions in real constitutional text.

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Search workflowMar 30, 20265 min

From Search Result to Full Article

How to move from keyword or semantic search into the full constitutional text and confirm whether a promising result really supports your research claim.

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How to read the visualization

What You Are Seeing

Each point in the 3D view represents a constitutional article or other meaningful legal unit. Nearby points are not nearby because they come from the same country, but because the language of those passages is semantically similar. Use country selection to compare how different constitutions occupy the same semantic terrain.

The map and the country list are selection tools. They decide which constitutions are loaded into the scene. Selecting more countries does not change the geometry of the embedding itself; it changes which parts of that global semantic space you can inspect.

Semantic Space

The embedding turns legal text into vectors, and the clustering step groups vectors that tend to discuss related constitutional themes. In country mode, color shows political origin. In cluster mode, color shows thematic neighborhood.

Large, dense clouds usually indicate recurring constitutional ideas such as rights, institutions, emergency powers, elections, or amendment rules. Isolated points often mark unusual provisions, rare wording, or country-specific constitutional design choices.

The platform offers two types of search: keyword search finds literal term occurrences, while semantic search retrieves conceptually nearby passages even without matching terms. Search results highlight regions of the semantic space in the 3D canvas, linking what you read to where it sits.

How to Read the Metrics

In the country statistics, Coverage measures how much of the global cluster landscape a constitution reaches. Entropy measures how evenly its segments are distributed across that landscape: high entropy suggests a broader semantic spread, while low entropy suggests concentration in fewer themes.

In the article detail panel, Global Cluster is the identifier of the thematic group assigned to that segment in the worldwide clustering. When the value is -1, it means the segment was left outside the defined thematic groupings in that global step. Probability indicates how confidently the clustering model placed that segment in that group: higher values mean a cleaner fit, while lower values usually mark more ambiguous or boundary cases.