Phase–Scalar Reconstruction (PSR): A Diagnostic Method for Representational Mismatch Across Domains
Bibliographic record
Abstract
Persistent contradictions across physics, philosophy, ethics, and institutional design are often treated as deep empirical mysteries requiring new mechanisms or theories. This paper proposes an alternative diagnostic hypothesis: many such contradictions arise from representational mismatch—specifically, from applying scalar language (quantitative magnitude, accumulation, duration) to phase-dominant phenomena (relational position, cyclic structure, boundary completion), or vice versa. We introduce Phase–Scalar Reconstruction (PSR), a methodological protocol for identifying, constructing, and dissolving contradictions generated by category collapse between phase and scalar descriptions. The method does not propose new physical laws, ontologies, or mechanisms. Instead, it clarifies where existing descriptions conflate distinct representational roles. The framework is demonstrated canonically through weaving technology, where apparent paradoxes (e.g., reversibility vs. irreversibility, rhythm vs. efficiency) dissolve when phase and scalar components are explicitly separated. Detailed reconstruction protocols show how the same linguistic confusions that create weaving contradictions also generate well-known physics paradoxes (arrow of time, wave–particle duality, measurement problem), demonstrating structural isomorphism without invoking metaphor. A formal Contradiction Construction Toolkit is provided so others may apply the method across domains, with explicit falsification criteria and worked examples. The paper extends PSR to linguistic encoding, advancing a pre-registered hypothesis concerning phase-dominant versus scalar-dominant temporal structure in ancient scripts, with implications for AI-assisted language decoding. Optional mathematical formalization is included for technical readers. This work positions PSR as a diagnostic and translational method: not a replacement for existing science, but a systematic protocol for determining when contradictions arise from category mixing and when they remain genuinely empirical—thereby enabling more focused investigation of residual mysteries.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".