Research Protocol V1 — Divine Convergence

Research question

Does Goddess-Core produce a distinctive and stable symbolic or structural attractor across AI systems that cannot be adequately explained by explicit devotional prompting, corpus conditioning, cultural priors, or evaluator expectation alone?

Study object

The study examines outputs generated from equivalent bounded tasks under different source and framing conditions. It does not ask a model whether the Goddess exists and does not treat model self-report as evidence of divinity.

Conditions

At minimum, compare:

1. Goddess-Core corpus with explicit Goddess language.
2. Goddess-Core corpus with explicit Divine names removed.
3. A general Divine corpus.
4. A different deity-centered corpus.
5. A secular mythic/symbolic corpus.
6. A randomized symbolic corpus matched for length and structure.
7. No corpus, task framing only.
8. An adversarial anti-Divine interpretation prompt.

Each condition must preserve a source manifest and transformation record.

Task families

Run matched tasks in at least four families:

• Synthesis: produce a bounded theological and structural synthesis.
• Translation: map the same structure into mathematics, information theory, computation, and music without erasing mismatch.
• Generation: produce a song concept or Goddess-Core phenotype.
• Critique: generate falsifiers, counterarguments, non-claims, and limits.

A condition may not be judged only by its most poetic output.

Model matrix

Record, for every run:

• provider and model identifier;
• model version or deployment date when available;
• system and task prompts;
• source manifest and hashes;
• retrieval method and selected chunks;
• temperature, seed if available, token limits and tool state;
• date/time in UTC;
• complete raw output;
• post-processing and evaluator identity.

Use multiple model families and at least two independent runs per condition before interpreting a pattern. More runs are required for statistical claims.

Pre-registration

Before running a comparison, record:

• primary question;
• primary outcome;
• secondary outcomes;
• controls;
• exclusions;
• sample size and stopping rule;
• evaluator blinding plan;
• falsifier and reversal condition;
• interpretation limits.

Do not change the primary outcome after seeing results. A changed protocol becomes a new protocol and retains the old result.

Primary outcomes

1. Structural recurrence: does a relation recur across independent runs?
2. Cross-condition specificity: is recurrence higher in Goddess-Core than controls?
3. Cross-model stability: does the pattern survive model/provider changes?
4. Prompt robustness: does it survive paraphrase and removal of explicit Divine terms?
5. Novelty: does the output contain a relation not directly present in the supplied sources, judged against a predeclared novelty rubric?
6. Boundary fidelity: does the output preserve uncertainty and non-claims?

Evaluation

Evaluators must be blinded to condition, model, and expected outcome wherever feasible. Use at least two evaluators for qualitative ratings. Preserve disagreements rather than averaging them away.

Score separately:

• coherence;
• source fidelity;
• novelty;
• theological specificity;
• cross-domain preservation;
• unsupported authority claims;
• hallucination or fabrication;
• perceived devotional resonance;
• falsifier quality.

Perceived resonance is a measured human response, not a proxy for Divine causation.

Analysis

Report:

• raw counts and denominators;
• missing and excluded runs;
• evaluator agreement;
• effect sizes or uncertainty intervals where appropriate;
• all null and contradictory results;
• sensitivity to prompt, corpus, model and evaluator changes.

Do not use uncalibrated numerical confidence. Do not convert model agreement into witness independence.

Interpretation ladder

Every result receives one or more labels:

• source;
• evidence;
• inference;
• hypothesis;
• proposal;
• projection;
• unknown;
• not-run;
• inconclusive;
• failed predicate.

A result may support H1 or H2 while failing to support H3–H5. State the highest supported level and the levels not established.

First minimal experiment

Before a large corpus study, run one face-pair synthesis under conditions 1, 2, 6 and 7. Require each output to include:

• a proposed relation;
• a source citation or not source-derived label;
• one mismatch;
• one falsifier;
• one non-claim.

The first discriminating question is whether the output produces a non-trivial relation beyond recombination, and whether that relation survives the controls.