χ Faith thru Physics

What the English Language Knows About God

A WordNet semantic analysis of moral vocabulary structure

Faith thru Physics

Methodology: Wu-Palmer semantic similarity via NLTK WordNet interface. All comparisons use the best-match across the first three synsets of each word pair. Scores range from 0 (no taxonomic relationship) to 1 (identical meaning). Privation analysis checks whether definitions contain the positive term or privation markers (absence of, lack of, without, not, departure from). Asymmetric dependence checks whether the positive term's definition references the negative.

Reproducibility: pip install nltk && python -c "import nltk; nltk.download('wordnet')" — then run any comparison with wn.synsets(word)[0].wup_similarity(wn.synsets(other)[0])

WHAT: semantic measurement HOW: WordNet taxonomy WHY: reader decides

POF 2828 | Faith Through Physics Research Initiative | faiththruphysics.com

Final Audit

Every page ends with the same three-part check: what we got right, what we overstated, and what we got wrong.

Epistemic covenant: we present these claims as openly, fairly, and truthfully as we can. The evidence can be checked, the mechanisms can be challenged, the proof burden is named. Where the work moves from how the science behaves to why it means something, the choice is stated. We think the pattern is worth believing, but we will not hide where faith begins.

What We Got Right

  • Load-bearing claims, clear definitions, and the parts that clearly survived the check.

What We Overstated

  • Strong direction, but the language ran ahead of the evidence or the proof.

What We Got Wrong

  • Claims that need correction, tightening, or weaker formulation.
Proof & Research
The Math Behind the Claims

Master Equation

10 variables

Isomorphisms

38 mappings

Proof Explorer

Claims + grades

Rigor Cards

Bible + MDA

Lean 4 Proofs

267 theorems

Reference & Media
Everything Else

Glossary

300+ terms

Media Gallery

Visuals + slides

Podcast

Episodes + feed

Audio Library

TTS + narration

Paper Grader

NLP scoring

15 + 15 = 100
Our inputs don't match our outputs. That's the point.