Rigor Guides
The 7 Dimensions of Scientific Rigor
TOE-Share evaluates work dimension by dimension because strong science is not one thing. A paper can be novel but unclear, careful but unfalsifiable, ambitious but mathematically weak. These guides explain what each dimension measures and how to improve it.
Flagship Guides
Falsifiability
What counts as a real prediction, what does not, and how to raise this score.
Mathematical Validity
How derivations, notation, assumptions, and consistency affect scientific credibility.
First-Principles Derivation
How an audited structural trace supports scores of demonstrated necessity without importing a downstream formalism.
Internal Consistency
How to detect contradictions between claims, assumptions, and implications.
Clarity
Why intelligibility is part of rigor and not just a presentation issue.
Novelty
What makes a contribution genuinely new instead of merely rephrased.
Completeness
How to expose limitations, edge cases, and missing steps honestly.
Evidence Strength
How linked supporting papers strengthen a framework-level evaluation.
Why Split Rigor into Dimensions?
A single overall score hides the reason a paper succeeds or fails. Dimensional review makes the feedback actionable. Instead of a vague rejection, you learn whether the real issue is the derivation, the missing prediction, the clarity of exposition, or the supporting evidence.
That is why TOE-Share treats rigor as a profile, not a thumbs-up badge.
The same reasoning applies across kinds of work, not just within one. A physical theory, a theorem, and a first-principles derivation are checked for wrongness in different ways — by prediction, by proof, or by necessity — so TOE-Share’s review panel classifies which kind of work a submission is before scoring, and converts the criterion that would otherwise measure the wrong thing. See the first-principles derivation guide for the clearest example of this in practice.
Use These Guides Before You Submit
The easiest way to improve a review is to catch the obvious rigor gaps before the review ever happens. Start with falsifiability and mathematical validity if you want the fastest gains.