Constitutional Narrative Structures Through Computational Philosophy: An Empirical Investigation of Recovery-Transition Dynamics Across Seven Nations
Bibliographic record
Abstract
Description: This study presents a novel computational framework for analyzing narrative structures in constitutional texts using Recovery-Transition dynamics modeling. We developed a three-stage analytical pipeline that converts constitutional texts to semantic novelty signals, detects structural breakpoints, and classifies text segments using competitive model fitting between triadic (recovery) and fractal (transition) mathematical models. Methodology: Text-to-signal conversion using multilingual sentence transformers Breakpoint detection via ruptures library binary segmentation Dual model classification with BIC-based selection Statistical validation against six null models (random shuffle, random walk, Gaussian noise, AR processes, fractal noise, periodic noise) Cross-validation for predictive testing Empirical Findings: Analysis of seven national constitutions (Brazil, China, Canada, France, Switzerland, South Africa, United States) revealed distinct, statistically significant narrative patterns for each document. Most patterns showed strong statistical significance (p < 0.05), with all patterns deviating significantly from multiple null models, indicating non-random structural organization. Key Results: A unique narrative grammar was identified for each of the 7 nations (e.g., Symmetrical 'ABBA' for Brazil, Progressive 'BBBA' for China, and Pure Transformation 'BBBB' for Switzerland). All identified patterns were proven to be statistically significant, deviating strongly from random chance. The framework successfully analyzed texts of vastly different scales, from 117 sentences (USA) to 2,840 sentences (Brazil). Theoretical Framework: The analysis draws on philosophical concepts of conditional universality - the proposition that systems exhibit universal structural patterns through context-dependent manifestations. Results suggest constitutional texts may follow common organizational principles while maintaining distinct national characteristics. Limitations: This represents exploratory research applying computational methods to constitutional analysis. Patterns reflect textual/linguistic structures rather than direct political meanings. Cross-cultural validity of semantic embeddings and causality between structural patterns and governance outcomes require further investigation. Significance: Demonstrates the feasibility of computational philosophy approaches for analyzing complex documents. Introduces Recovery-Transition dynamics as a framework for comparative textual analysis. Provides a baseline methodology for future constitutional structure research. Data Availability: Complete analysis pipeline code and results are included. Raw constitutional texts were processed from public domain sources. Keywords: constitutional analysis, computational philosophy, conditional universality, recovery-transition dynamics, narrative structure, resonance ontology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".